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TECHNICAL JOURNAL

2014 Excellence Awards, Technical Papers 1

Contents About & Award Categories........................................................................................................................... 2

The Mobility Engineering Excellence Awards ........................................................................................... 2

Professional Category Awards ................................................................................................................ 2

Academia Category Awards .................................................................................................................... 2

Key Dates for 2015 MEEA ........................................................................................................................... 2

Apply for the 2015 Excellence Awards ......................................................................................................... 3

SAE-A National Office Contact Details ......................................................................................................... 3

2014 MEEA Award Winners......................................................................................................................... 4

Sponsors of the 2014 MEEA ........................................................................................................................ 5

The SAE-A .................................................................................................................................................. 6

Being a member ..................................................................................................................................... 6

2014 Judging Panel ..................................................................................................................................... 7

Platinum Award & Gold Award for Automotive Robert Bosch Australia,

Automatic Emergency Braking Rear (AEB-Rear) .......................................................................................... 9

Gold Award for Heavy Commercial Vehicle AutoTest Products Pty Ltd,

AutoMonitor: Complete Vehicle Condition Monitoring ................................................................................. 14

Gold Award for Manufacturing AutoCRC Ltd, Victorian Partnership for Advanced Computing, Malaysia

Automotive Institute, vMould – Rapid Plastic Injection Mould Design Optimisation ...................................... 23

Student Gold Award Tyler Plowright, University of Newcastle,

Aerodynamics of Australian Coal Trains ..................................................................................................... 31

Student Highly Commended Award Marc Russouw, Monash University,

Development of a Vehicle and Tyre Model for Formula SAE-A .................................................................... 32


About & Award Categories

The Mobility Engineering Excellence Awards The SAE-A Mobility Engineering Excellence Awards (MEEA) is the industry’s premiere event recognising

outstanding contributions to advancing technologies in the transport mobility field. The MEEA carries great

prestige within the engineering mobility profession. Award submission offers significant recognition, with all

submissions being reviewed by technical experts within the industry, and promoted through the Society’s

publications and media releases.

Each year nominations are sought from across the mobility engineering industry, with awards in the following

sectors:

Professional Category Awards: The Platinum Award - for the overall winner

Gold Award for Automotive

Gold Award for Caravans & Campers

Gold Award for Rail

Gold Award for Aerospace

Gold Award for Heavy Commercial Vehicles

Gold Award for Off-Road Vehicles

Gold Award for Manufacturing

Gold Award for Aftermarket

Gold Award for Young Engineer

Student Category Awards:

Gold Award for Student – Post Graduate

Gold Award for Student – Under Graduate

Highly Commended Award for Student

Key Dates for 2015 MEEA Nominations and Submissions Nominations for the 2015 MEEA officially open 26 May, 2015.

Professional and Young Engineer submissions close 12 October, 2015.

Post and under Graduate submissions close 26 October 2015.

Awards Ceremony The Awards ceremony and dinner will be held on 19 November 2015 at Fenix; 680 Victoria Street, Richmond, Victoria.

Dinner RSVP closes 12 November 2015.


Apply for the 2015 Excellence Awards The SAE-A’s Mobility Engineering Excellence Awards recognises excellence in engineering across a product,

process or service.

Register your interest now to [email protected], or (03) 9676 9568.

The SAE-A awards “Outstanding Contributions to Advancing Technology”, as well as recognising key persons in

the Young Engineer, Post Graduate and Undergraduate categories, for their key contributions to the engineering

mobility industry.

The SAE-A can help facilitate your nomination – contact one of the friendly staff at the National Office.

Submissions for the next MEEA close October 2015.

SAE-A National Office Contact Details Address: Unit 30 / 3 Westside Ave,

Port Melbourne Vic 3207

Phone: (03) 9676 9568

Fax: (03) 9646 7793

Email: [email protected]

Office Hours: Monday – Friday,

9:00am – 5:00pm

www.saea.com.au


2014 MEEA Award Winners

Platinum Award

& Gold Award for Automotive:

Robert Bosch Australia - Automatic Emergency Braking Rear The most prestigious award of the evening; the overall winner of the professional awards category, was

presented to Robert Bosch Australia for their highly innovative Automatic Emergency Braking Rear (AEB-Rear)

product. The AEB-Rear has significant export opportunities for many global platforms. AEB-Rear uses

ultrasonic and radar sensing technologies combined with automatic braking to reduce pedestrian injuries and

fatalities as well as unintended collisions caused when a vehicle is reversing.

Gold Award for Heavy Commercial Vehicle:

AutoTest Products Pty Ltd - AutoMonitor: Complete Vehicle

Condition Monitoring AutoTest Products Pty Ltd was awarded Gold in the Heavy Commercial Vehicle category for their AutoMonitor

product; a Complete Vehicle Condition and Roll-Over Monitor for heavy vehicles. A product of interest to safety

authorities and of value to fleet operators.

Gold Award for Manufacturing:

AutoCRC Ltd, Victorian Partnership for Advanced Computing,

Malaysia Automotive Institute – vMould: Rapid Plastic Injection

Mould Design Optimisation vMould, an easy to access web application and software package from Victorian Partnership for Advanced

Computing (VPAC), in collaboration with AutoCRC Ltd and Malaysia Automotive Institute, received a Gold

award for the Manufacturing category, for its ability to automate plastic injection mould design and production

process, reducing manufacturing time and costs.

Gold Award for Student:

Tyler Plowright, Monash University - Aerodynamics of Australian

Coal Trains In the Student category, Tyler Plowright of the University of Newcastle was awarded Gold for his work in

Aerodynamics of Australian Coal Trains; a research project to help improve the energy efficiency and therefore

reduce fuel consumption in these trains.

Highly Commended Award for Student:

Marc Russouw, Monash University - Development of a Vehicle and

Tyre Model for Formula SAE-A Marc Russouw of Monash University was Highly Commended for his project on the Development of a Vehicle

and Tyre Model for Formula SAE, a model used by the Monash Formula team in the development of their

overall vehicle model.


Sponsors of the 2014 MEEA

The State Government of Victoria The State Government of Victoria sponsored the Platinum Award.

Mr. Chris Wong, Manager Automotive Victorian Government was

proud to present the highest award of the night, the Platinum Award

for engineering Excellence.

The RACV The RACV sponsored the Professional Awards: Automotive, Heavy

Commercial Vehicle and Manufacturing.

Nicholas Platt, RACV’s Acting Manager of Vehicle Engineering

presented these three awards to the winning companies.

GM Holden GM Holden sponsored the Student Awards category, including both

Gold and Highly commended awards.

Brett Vivian, Executive Director of Engineering at GM Holden

presented the awards to the student winners.

The Suncorp Group Suncorp was a proud supporter of the 2014 MEEA


The SAE-A The SAE-A is a non-profit organisation that works to serve the needs of its members and to promote the

relevance of mobility related technologies to governments, industry and the community in general.

SAE-A is the world’s third oldest mobility society and was founded in Melbourne in 1927 to address the need

for further education for all facets surrounding Mobility Engineering and now encompasses all mobility

engineering industries in the Asia Pacific region.

The vision of the SAE-A is to advance the mobility engineering professions in Australasia through promoting

the transfer of technical knowledge and skills, encouraging research and development in the private, education

and government sectors and involving our members in the development and maintenance of Australasian and

global technical standards.

SAE-A membership encompasses the entire transport mobility sector:

Being a member SAE-A fosters a welcoming and collegiate environment for mobility engineering professionals. Membership

provides the opportunity to:

SAE-A membership is valued by industry as being evidence of an individual’s commitment to continuous

personal and professional development. Members receive publications, have the opportunity to be involved in

industry groups and receive substantial discounts across networking and training opportunities.

Contribute to your

Engineering

Community

Advance your

Technical

Knowledge

Grow your Industry

Connections

Advance your

Career

Automotive 4WD & Offroad Camper & Caravan Agriculture Motorbike

Mining Aero - Commercial Aero - Light Body Repairers Aftermarket

Students &

Universities Shipping Rail Heavy

Commercial

Bus & Public

Transport


2014 Judging Panel

Chair, SAE-A Excellence Awards - Shane Richardson Dr Shane Richardson is Principal Forensic Engineer and Managing Director of Delta-V Experts. Shane leads

a team of Engineers focused on evaluating, understanding and describing forensic engineering issues. Shane

investigates the dynamic exchange of energy between objects be it a pedestrian, cyclist, car or truck,

maintenance evaluations, mechanical failure analysis or workplace incident investigations. Shane also

conducts dynamic vehicle handing tests and develops engineering solutions to unique problems such as Roll

Over Protective Structures, Road Roughness Monitoring systems and instrumentation for sporting equipment.

Carl Liersch General Manager, Bosch Chassis Systems Control Engineering in Australia. Carl has worked in the automotive

supplier industry for 27 years, with the last 24 of those years with Bosch. During that time Carl has worked

primarily with vehicle safety systems. He was involved with the introduction of ABS and Airbag technology to

Australia in the early 1990s via the Australian OEMs. Since 2000, has worked on Electronic Stability Control.

Carl has tuned ABS and ESC systems for each of the vehicle manufacturers in Australia, for Jaguar and Ford

in Europe and the USA, and for Toyota in Japan. He leads the Chassis Engineering division in Australia with a

team of 50 engineers supporting safety system projects in Europe, Japan, India, Malaysia, the USA, and

Australia.

William Malkoutzis Proprietor, Talk Torque Automotive. Bill has been working for 33 years in the automotive industry, 13 years of

that career with Ford Australia, holding positions in both light and heavy vehicle design, test and development

in Australia and USA. He contributed to the development of the F series trucks and the design and introduction

of the Ford Capri in Australia and USA. A further 13 years with PBR Australia designing and developing braking

systems for customers in Australia, America, Asia and Europe. Bill has operated his own automotive

consultancy with various ongoing commercial clients for 8 years. He is a VASS authorised signatory for

modified light vehicles as regulated by VicRoads. He is the Immediate Past President of the SAE-A, serving

for over five years and has served as SAE-A Excellence Awards Chairmen for three years.

Clint Steele Senior Lecturer, Swinburne University of Technology. Clint Steele worked as a design or development engineer

in the automotive, mining equipment, water products and product design/development industries before

entering academia. He has also worked in Australia and China as a design engineer and the influence of

background (economic, cultural and environmental) upon engineering design practice is a research focus. An

additional interest is the attributes of expert engineering designers and how these can be developed in all

engineers. In his academic role Clint is also the academic advisor to the F-SAE team (Team Swinburne) and

a member of the electric vehicle research group (focusing on industry engagement). He is also on the board of

directors of the Alternative Technologies Association and SolarX.


Simon Watkins Professor Automotive Engineering, RMIT. He worked for British Aerospace at the Harrier Jump Jet plant in the

UK and studied the aerodynamics of advanced ground transport vehicles at City University, London. In 1983

he moved to Australia and has since researched and taught at RMIT. He has experience in Micro plane, car,

truck and train aerodynamics and heads a research group in vehicle aerodynamics and acoustics, consisting

of several graduate students and associated staff. He also is past chair of the SAE (International) Road Vehicle

Aerodynamics Forum based in Detroit and the Aero-acoustic subcommittee. He was the Chair and Conference

Convenor of the 2015 Asia Pacific Automotive Conference (APAC), held in Melbourne in March, preceding the

2015 Australian Grand Prix.

David Ford A University of Melbourne Honours graduate in Mechanical Engineering, David is a Fellow of the Institute of

Engineers Australia, SAE-International and SAE-Australasia. He is a former Senior Vice President and

Treasurer of SAE-Australasia. He was a Product Planning Manager and Chief Engineer at Ford Motor

Company in Australia, moving to the Ford Motor Company USA in 1990 to take up Director/Executive positions

in Product Development. He retired 1998 to return to Australia, where he has maintained international and local

industry and academic contacts and local business interests. He has also served on advisory committees for

the Engineering Schools of Melbourne, RMIT and Swinburne Universities and was Deputy President of the

Committee of Convocation of the University of Melbourne.

Andrea Winkelmann With more than 25 years in the automotive light, heavy and fuels industries, Andrea began her career in

Germany and completed her engineering degree in the UK. Immigrating to Australia in 2005 to take the role of

Senior Quality Engineer responsible for domestic vehicle warranty issues at Ford reporting to the executive,

and subsequently working at GM-Holden from 2007 were she hold the position as the Verification and

Validation Program Lead for the Global Zeta Platform, responsible for the project management of

vehicle/engine testing and system verification.

Andrea has recently taken up a position with ABMARC as Director of Engineering. In her new role she is

responsible for Emissions Testing for On & Off-road Engines and Vehicles using the AVL PEM System (the

only system of its type and capability in Australia), Dynamic Vehicle Testing and VASS Certification. Andrea

was awarded the ISSI – Eddy Dunn Endowment International Fellowship in 2014 to research the Impact of

International Heavy Duty Vehicle OBD Regulation Amendments (2013 onwards) on the Service and Repair

Industry in Australia.


Platinum Award & Gold Award for Automotive

Robert Bosch Australia,

Automatic Emergency Braking Rear (AEB-Rear)

Overview The Bosch Australia Chassis Systems Control team is the centre of global Bosch competency in Automatic

Emergency Braking technology.

The team has integrated a number of existing, new and emerging technologies to create a fully automated,

potentially life-saving feature that will be appearing in future model vehicles across the globe. Its development

required the clever combination of both ultrasonic and radar sensing technologies, along with an automatic

braking system, to create a solution to the reversing visibility problem, that has been causing ever increasing

numbers of pedestrian injuries and fatalities as well as unintended collisions the world over.

The system is tuned to be sensitive and discerning of pedestrians and obstructions at speeds up to 15 km/h,

to within 10 metres of the rear of the vehicle dependent on the path taken. It uses the sensing data and

associated algorithms to apply a calculated level of braking force if an obstruction or pedestrian is sensed to

be within and surrounding the vehicles path. Without the need of driver input, AEB-Rear will stop the vehicle

and prevent collisions and injuries, while ensuring that false interventions are kept to a minimum.

Judges’ Comments The standout feature of this submission is the manner in which Bosch simply and efficiently brings together

currently available technologies in a practical way to address a current need; reducing the potential of

pedestrian, particular children, injuries and fatalities. The potential of this product to save lives, combined with

major global export opportunities of an Australian developed product, has deemed Bosch the Overall Winner

of the 2014 MEEA.


Robert Bosch (Australia) Automatic Emergency Braking Rear (AEB Rear) Function for Avoiding Back Over

Accidents

Mark Anderson Robert Bosch Australia

ABSTRACT

Automatic Emergency Braking (low speed) Rear (AEB-L Rear) is a function developed at Bosch Australia with support from Automotive New Markets Program (ANMP) that utilises vehicle sensors in combination with the Electronic Stability Control Program (ESP) to prevent a reversing accident. It brakes the vehicle when it is reversing towards a person or object that the driver would normally want to avoid. The detection utilises standard surround sensors such as Ultrasonic parking sensors and a pair of rear radars.

INTRODUCTION

Tragically, on average, seven children are killed each year and 60 are seriously injured after being hit or run over by a motor vehicle at home [1].

This statistic is mirrored around the world with over 60 child backover fatalities occurring in the U.S.A. in 2013 and over 1,180 since 1990 [2]. What makes this data even more shocking is that most of these accidents take place in home driveways and thus it is the children’s’ parents or family members who are most likely behind the wheel.

There are many awareness factors that contribute to backover deaths; such as lack of driver visibility for small children (many of whom are not tall enough to be seen in the rear window or rear-view mirror), a lack of road/vehicle safety knowledge in small children, and unpredictable juvenile behaviour. And, while many accidents can be avoided or minimised by reversing sensors or cameras, to date there does not exist any full-proof automobile technology that can eliminate human error and mitigate unexpected and sudden risks while reversing a vehicle.

Automatic Emergency Braking Rear (AEB Rear) is a function developed at Bosch Australia with support from Automotive New Markets Program (ANMP) aimed at reducing this unnecessary carnage.

Figure 1 – 3 Year Old Child standing behind a Ford Territory

SENSORS

The implementation of the AEB Rear utilises two different sensing technologies. The different types of sensors have been chosen to complement each other in terms of Field Of View (FOV) to optimise the combined FOV and minimise risks associated with individual sensor types.

Radar

The Bosch Mid-Range Radar Rear (MRR Rear) is the chosen technology for this implementation. These radars are a 77 GHz dual mode antenna radar utilising SiGe MMIC technology. The radar antenna has a range of approximately 100 meters with an opening angle of 150 degrees. The sensors are installed in pairs facing approximately 45 degrees off the centreline of the vehicle at the rear corner of the vehicle.


Figure 2. Image of a single MRR Rear radar FOV with an example of Blind Spot Detection illustrated.

The radars are conventionally installed for Cross Traffic Alert (CTA) and Blind Spot Detection (BSD). The AEB Rear function was designed to function when the radars are installed in their conventional locations.

The primary area of interest for AEB Rear has been determined to be within ten meters of the rear of the vehicle. This is based on limiting the speed at which the function operates to fifteen kilometres per hour. This speed was chosen based on a subjective evaluation of reversing speeds in a normal environment.

The distance was estimated by a build-up of the detection time, monitoring time, and stopping distance.

The limiting of the distance reduces some of the complexity in terms of vehicle corridor estimation, which when driving in reverse, can change quickly with relatively little steering input.

Tracking

It was found that in the near range for slow moving objects with lower radar cross sectional area that the tracking algorithms that have been implemented for other use cases needed to be modified. This tailoring enabled better tracking of children within the radar FOV.

The tracking algorithm was further developed to maintain a memory of objects that enter the blind spot of the radar immediately behind the vehicle. For pictorial representation of the Blind spot, refer to figure 3.

Figure 3 – FOV (2 Dimensional) of different sensing technologies.

Filtering

The second component of the radar development is to implement appropriate filtering techniques to identify relevant objects and to exclude non-relevant objects. An important part of this exercise was in the initial requirements development and elicitation phase of the project. With the appropriate requirements development, a technique was developed that enabled the filtering of relevant target objects from non-relevant objects. The technique used is currently being investigated for a patent application and is therefore protected internal IP until it is deemed appropriate for it to be released.

Ultrasonic Sensors

The ultrasonic sensors that have been used for the implementation are standard Bosch UltraSonic Sensors (USS).

A limitation of relying on current USS technology solely, is angular resolution and their overall measurement distance. They also inherently have distances at which they are more sensitive to false detections. This is largely determined by mounting configurations.

Given these limitations, in arbitration it is important to minimise the reliance on USS particularly at higher speeds where a false detection could occur in a region already swept by the radar.

ARBITRATION

In order that the information from the different sensors are utilised in the most opportunistic fashion, sensor data arbitration was implemented. This arbitrates between the information from the Radar including the Radar target tracking algorithm and the ultrasonics to generate the best possible understanding of where objects of interest are behind the vehicle and to inform the actuation algorithms to react accordingly.

By utilising radar technology to observe with better angular resolution, better tracking and better filtering


capabilities, it is important to limit the reliability on the USS where possible.

This limitation filter can be seen in figure 4.

Figure 4 - USS intervention limitation curve

ACTIVE BRAKING

Within the Bosch ESP there is an existing Automatic Emergency Braking (AEB) function in the forward facing direction. This function has been then tailored for the rear case. The application of the measured stopping distance for the development vehicle enabled the braking of the vehicle to be optimised. This stopping distance curve was calculated based on experimentation utilising a pressure build from the ESP in a “nominal condition” (dry, flat, bitumen, vehicle loaded with driver plus measurement equipment). This is confirmed with a MATLAB simulation based on the vehicle parameters. Refer to figure 5 for the reverse stopping distance curve. The stopping distance curve will need to be tailored to each vehicle application due to dependencies not only on the vehicle setup but also the brake characteristics, in particular the rear brakes.

Figure 5 - Stopping Distance curve for demonstration vehicle

SYSTEM INTEGRATION

The prototype vehicle has integrated the function through utilising the existing USS with the CAN signals shared through a gateway on the high speed CAN. The MRR CAN signals are shared through a separate gateway also on the high speed CAN. The Arbitration and active braking is implemented on the ESP unit.

SUMMARY / CONCLUSIONS

The testing that has been completed to this point in the project show promising results in the effective reduction of reversing accidents. A major consideration is ensuring the implementation does not result in an excessive number of unwanted interventions that would result in a negative perception of the support function. Through the final stages of development, with the team focussing on the requirements, the implementation of the function looks promising for contributing to the reduction of reversing accidents.

REFERENCES

1. Department of Infrastructure and Transport. (2012, August). http://www.bitre.gov.au/publications/2012/files/is_043a.pdf.

2. Kids and Cars. (2014). Statistics. Retrieved from http://www.kidsandcars.org/statistics.html

ACKNOWLEDGMENTS

I would like to acknowledge financial support of ANMP and Robert Bosch (Australia) Pty Ltd to make this program happen. I would like to acknowledge the support of the project sponsors, Mark Jackman, Carl Liersch and John Nicolopoulos in believing in the project from the start, promoting and supporting it at every opportunity. I would also like to acknowledge the contribution of Andrew Greenbury and Joseph Cavallaro who first demonstrated the concept as part of the new products development group and last but not least the team who have worked tirelessly over the past 18 months implementing, testing, learning and then doing it all again to get the function to where it is today.


CONTACT

Mark Anderson Robert Bosch Australia Email: [email protected]

DEFINITIONS / ABBREVIATIONS

AEB Rear: Automatic Emergency Braking Rear

SiGe MMIC: Silicon-Germanium microwave monolithic integrated circuit

FOV: Field Of View - 3 dimensional space in which a sensor or group/s of sensors can identify objects.

MRR Rear: Bosch Mid Range Rear radar

CTA: Cross Traffic Alert. Function for detecting traffic driving across the vehicle path

BSD: Blind Spot Detection. Function for detecting traffic sitting or passing through in the blind spot of the vehicle from the driver’s perspective

USS: Ultra Sonic Sensors

ESP: Electronic Stability Program

AEB: Automated Emergency Braking


Gold Award for Heavy Commercial Vehicle

AutoTest Products Pty Ltd,

AutoMonitor: Complete Vehicle Condition Monitoring

Overview While a number of heavy vehicle/driver monitoring devices are available locally and internationally

(Tachographs; GPS tracking systems; vehicle monitoring systems), the AutoMonitor integrates these functions

into a compact digital system. The AutoMonitor also includes a Rollover Prediction system, increasing the

operator safety in addition to the other monitoring and recording functions. AutoMonitor is predictive, with the

ability to determine the mass and Centre of Gravity of the vehicle, and can thereby warn drivers to slow as they

approach the rollover point. The system is likely to be of interest to safety authorities and be of value to fleet

operators, in local and international markets. Judges Synopsis Gold was awarded to AutoTest in the Heavy Commercial Vehicle Category for AutoMonitor, a locally developed

and manufactured product that helps address a safety need in the trucking industry, as well as providing

operational information in one complete package.


Rollover Prediction In Dynamic Environment

Muhammad Alamgir AutoTest Products Pty Ltd

ABSTRACT

Given the rising statistics of fatalities and injuries

caused by vehicle rollover accidents, an effective

solution to predict and prevent an impending rollover

is imperative. An accurate rollover prediction method

using multi-dimensional sensor inputs is proposed. A

rollover prediction scale or rollover index function

taking multiple inputs is presented. Several field tests

are carried out to validate the proposed rollover

prediction approach. The findings indicate that the

proposed method is not just effective but also very

practical.

INTRODUCTION

Rollover of automotive vehicles is a growing problem

as the number of vehicles running on roads is

increasing. Whenever a fatal accident occurs, there is

a one in four chance that the vehicle rolled over [1].

In the Northern Territory, one of the states of

Australia, the rollover rate is found to be as high as

over 70% [1]. The rollover rate of heavy vehicles in

Tasmania is found to be 16.3% of all truck crashes

[2]. According to a report from AARB, using the data

from Victorian Crash Database and National

Transport Insurance (NTI) over years 2003–2007,

10% of all truck crashes in Victoria were involved in

rollover [3][4]. While trucks are more likely to roll over

because of their higher centre-of-gravity, studies

have shown a significant number light vehicles have

also been involved in rollover incidents [5] [6]. In fact,

vehicle rollover accounts for 35% of all fatalities in a

single vehicle transport injury event [7]. The rollover

instability is the most significant issue causing

crashes resulting in severe injuries and fatalities [8].

Besides the fatalities and injuries caused by vehicle

rollover, the rollover of vehicles also results in large

repair costs, which according to a report is estimated

to be an average of $33,000 per incident on top of an

average four weeks down time [9]. Many vehicles

these days are equipped with safety related

technologies such as ABS, ESP, and air bags, which

are intended to add stability to the vehicle handling in

order to reduce the number of accidents or injuries.

However, a vast majority of vehicles are lacking

rollover prevention related technologies suggesting a

strong demand for a vehicle rollover prediction based

system that can save both lives and cost.

There are many factors that affect the stability of a

vehicle and ultimately cause it to roll over. Under

typical driving conditions, factors such as over-

speeding around corners, manoeuvres caused during

obstacle avoidance and severe lane changing can

induce lateral acceleration which if it exceeds the

stability threshold value of a vehicle can cause it to

rollover [8]. Over-speeding has been the main cause

of accidents in the majority of rollover cases.

According to an accident investigation report, 59.6%

of speed related truck crashes resulted in rollover

[10]. Similar findings were obtained for the truck

crashes in Tasmania, where 50% of rolled-over trucks

were found to be over-speeding [2]. When a vehicle

is driven around a bend, the lateral acceleration (ay)

exerted on the vehicle is a function of the current

speed of the vehicle v and its turn radius R:

𝑎𝑦 =𝑣2

𝑅 (1)

According to equation (1), the lateral acceleration

increases by the square of the speed. If the speed of

a vehicle is reduced from 60 km/h to 50 km/h, when it

travels around a bend with radius 500m, the lateral

acceleration will be reduced by 30%. Thus, if speeds

are lowered around bends, rollover likelihood is

lowered. Another factor that affects the lateral

acceleration is the yaw rate of the vehicle. The lateral

acceleration can also be described in terms of yaw

rate ψ ̇and the speed of the vehicle (v) as:

𝑎𝑦 = 𝑣�̇� (2)

Once the factors contributing to rollover of a vehicle

are understood, a rollover prediction system could be

designed using the knowledge of the current dynamic

state of the vehicle, the rate at which dynamics are

varying, and the future state when rollover will occur.

A considerable amount of research work has been

undertaken in this field. The traditional techniques

measure the lateral acceleration exerted on the

vehicle and compare it against the rollover stability

factor known as Static Rollover Threshold (SRT) or

Static Stability Factor (SSF) of the vehicle [11]. Since

the lateral acceleration is the only input used to

determine vehicle rollover and the rolling dynamics of


the vehicle are not considered, such techniques

suffer from early trigger and late trigger problems.

Other improved techniques consider rolling dynamics

and predict rollover occurrence using additional input

parameters such as roll rate and roll angle [12][13].

Further improved methods use more input

parameters such as yaw rate, yaw angle or steering

angle and vehicle speed to predict rollover with

greater confidence [14][15]. However, many of these

techniques are based on simulation results and not

practical observational findings.

To determine the current rollover state of a vehicle, a

scale known as Rollover Index (RI) is used [16].

Rollover Index indicates the propensity of a vehicle to

become unstable and rollover. There are different

variants of the Rollover Index function, however, the

objective of a rollover index function remains the

same [17][18]. Often Load Transfer Ratio (LTR) is

used as an indicator to determine the stability of the

vehicle rollover system [19][20][21].

The rollover prediction technique described in this

paper is based on a novel approach that observes the

dynamics of vehicle rolling effects using multiple

sensors and predicts impending rollover based on a

real-time rollover index indicator. This paper first

presents a rollover analysis in dynamic situation.

Once the factors involved in the vehicle rollover are

highlighted, a rollover indicator function is described.

The performance of the algorithm is later verified with

field test results.

ROLLOVER ANALYSIS

Rollover dynamics incorporate lateral force exerted

by an external system, the rotational forces due to the

rolling moment of the sprung mass, the road bank

angle, and the damping forces of the suspension

system and tires. Figure 6 illustrates a generic

rollover model. When the vehicle rollover system is at

equilibrium, the tire forces F1 and F2 are equal.

To describe the model in figure 6 in a dynamic rollover

situation, we need to consider the moment forces

acting on the system at the time of cornering

manoeuvre [16].

(𝐼𝑥𝑥 + 𝑚ℎ𝑟2)�̈� = ∑ 𝑀𝑥 (3)

(𝐼𝑥𝑥 + 𝑚ℎ𝑟2)�̈� = 𝐹𝑦ℎ𝑟 cos 𝜙 + 𝑚𝑔ℎ𝑟 sin 𝜙

+𝑇𝑠

2(𝐹𝑠1 − 𝐹𝑠2) (4)

Figure 2. Rollover moment forces.

The term (𝐹𝑠1 − 𝐹𝑠2) can be expressed in terms of the

forces acting in the suspension system:

𝐹𝑠1 − 𝐹𝑠2 = −𝑘𝑠𝑇𝑠 sin 𝜙 − 𝑏𝑠�̇� cos 𝜙 (5)

By substituting (5) into (4), the moment equation can

be described as:

(𝐼𝑥𝑥 + 𝑚ℎ𝑟2)�̈� = 𝐹𝑦ℎ𝑟 cos 𝜙 + 𝑚𝑔ℎ𝑟 sin 𝜙

−𝑇𝑠

2𝑘𝑠 sin 𝜙

2−

𝑇𝑠2𝑏𝑠�̇� cos 𝜙

2 (6)

(𝐼𝑥𝑥 + 𝑚ℎ𝑟2)�̈� = 𝑚𝑎𝑦ℎ𝑟 cos 𝜙 + 𝑚𝑔ℎ𝑟 sin 𝜙

−𝑇𝑠

2

2(𝑘𝑠 sin 𝜙 + 𝑏𝑠�̇� cos 𝜙) (7)

When considering the effects of road bank angle, the observed lateral force exerted on the vehicle is given as [22]:

𝐹𝑙𝑎𝑡 = 𝑚(�̈� + 𝑣𝑥�̇� − 𝑔 sin 𝜃𝑟) (8)

Where, �̈� is the lateral acceleration, vx is the speed of

the vehicle, ψ ̇

angle. If the rollover effects are observed using an

accelerometer mounted on the chassis, the observed

lateral acceleration in terms of the road angle is

described as:

𝑎𝑚𝑒𝑎𝑠 = �̈� + 𝑣𝑥�̇� − 𝑔 sin 𝜃𝑟 (9)

If equation (9) is substituted in (7), the moment equation will then be formed in terms of road angle as:

(𝐼𝑥𝑥 + 𝑚ℎ𝑟2)�̈� = 𝑚𝑎𝑚𝑒𝑎𝑠ℎ𝑟 cos 𝜙

+ 𝑚𝑔ℎ𝑟 sin(𝜙 − 𝜃𝑟)

−𝑇𝑠

2

2(𝑘𝑠 sin 𝜙 + 𝑏𝑠�̇� cos 𝜙) (10)

hcg

T

H

hbase

Fy

W

Fnet

F2 F1

hr

r

c.r.

c.g.


Equation (10) can be used to directly determine the stability of a vehicle’s rollover dynamics. Using a tri-axial accelerometer and a tri-axial gyroscope, many of the parameters of the rollover moment equation can be obtained. However, the absolute values of an accelerometer cannot be used due to the tilting of the vehicle and the road bank angle. The absolute acceleration vector will have to be projected onto the linear plane of earth’s gravity, which requires the information of current tilt angles of the vehicle. First of all, the tilt or rotation angles need to be calculated by integrating the instantaneous readings of the gyroscope.

𝜃 = ∫ �̇� 𝑑𝑡 (11)

𝜙 = ∫ �̇� 𝑑𝑡 (12)

It is challenging to estimate the rotational angles by integrating the measurements of the gyroscope because of the drift error associated in gyroscope sensor readings when integrating over long periods. A hybrid technique is used in which readings from the accelerometer are used as feedback to determine rotational angles with a sufficient degree of accuracy. Once the orientation angles of the vehicle are estimated, the net acceleration vector relative to the earth’s gravity plane can be calculated using the Euler’s angle method [23]:

𝑎𝑥 = 𝐴𝑥 cos(𝜃) − 𝐴𝑧 sin(𝜃) (13)

𝑎𝑦 = 𝐴𝑦 sin(𝜃)sin(𝜙) + 𝐴𝑦 cos(𝜙) + 𝐴𝑧 cos(𝜃) sin (𝜙)

(14)

𝑎𝑧 = 𝐴𝑥 sin(𝜃) cos (𝜙) − 𝐴𝑦 sin(𝜙) + 𝐴𝑧 cos(𝜃)cos (𝜙)

(15)

Where, Ax, Ay, and Az are absolute acceleration

readings from an accelerometer, and are pitch and roll angles, respectively.

ROLLOVER INDICATOR

The rollover stability of a vehicle can be indicated by rollover index scale. The rollover index scale is usually described as the lateral load transfer ratio (LTR), which is expressed as:

𝑅𝐼 = 𝐿𝑇𝑅 =𝐹2−𝐹1

𝐹2+𝐹1 (16)

When the tire forces F2 and F1 are equal, the overall load transfer ratio becomes zero and the rollover system is considered stable. But when the LTR approaches 1 or -1, the vehicle rollover state becomes unstable and ultimately one of the side tires

is lifted up, which causes the vehicle to rollover on the opposite site.

The load-transfer ratio (LTR) cannot be computed in real-time because the tire forces F1 and F2 are not easily measured externally. However, the LTR can be estimated using the following equation [22]:

𝐿𝑇𝑅 =𝐹2−𝐹1

𝐹2+𝐹1=

𝑘𝑠𝑇𝑠 sin 𝜙+𝑏𝑠𝑇𝑠�̇� cos 𝜙

𝑚𝑔 (17)

The above LTR expression requires the information of the vehicle’s suspension system. However, the suspension system related coefficients can be eliminated by rearranging (10) and then substituting it into (17) [24].

𝑘𝑠𝑇𝑠 sin 𝜙 + 𝑏𝑠𝑇𝑠�̇� cos 𝜙 =2𝑚ℎ𝑟

𝑇𝑠[𝑎𝑚𝑒𝑎𝑠 cos 𝜙 +

𝑔 sin(𝜙 − 𝜃𝑟)] (18)

The rollover index can now be expressed in terms of vehicle dimensions, lateral acceleration and orientation angles, as:

𝑅𝐼 = 𝐿𝑇𝑅 =2ℎ𝑟𝑎𝑚𝑒𝑎𝑠 cos 𝜙+2𝑔ℎ𝑟 sin(𝜙−𝜃𝑟)

𝑇𝑠𝑔 (19)

=2ℎ𝑟

𝑇𝑠𝑔(𝑎𝑚𝑒𝑎𝑠 cos 𝜙 + 𝑔 sin(𝜙 − 𝜃𝑟)) (20)

The road bank angle can be estimated in a steady state condition using equation (9):

sin 𝜃𝑟 =𝑣𝑥�̇�−𝑎𝑚𝑒𝑎𝑠

𝑔 (21)

The road bank angle r is thus estimated from the speed of the vehicle, its yaw rate and the measured lateral acceleration:

𝜃𝑟 = sin−1 (𝑣𝑥�̇�−𝑎𝑚𝑒𝑎𝑠

𝑔) (22)

A general expression of the rollover index function in terms of multiple inputs can be given as:

𝑅𝐼 = 𝑓(𝑎𝑦 , 𝜙, 𝜃𝑟 , ℎ𝑟 , 𝑇𝑠) (23)

=2ℎ𝑟

𝑇𝑠𝑔(𝑎𝑦 cos 𝜙 + 𝑔 sin(𝜙 − 𝜃𝑟)) (24)

ROLLOVER PREDICTION SCHEME

The rollover prediction scheme determines the rollover index value (RI) at the instant when a vehicle becomes unstable and rollover is imminent. When the rollover system becomes unstable, one of the wheels


of the vehicle is lifted up in the air. At the instant of wheel lift-off, the load transfer ratio is either above +1 or below -1. Hence, the rollover index, when vehicle becomes unstable, can be described as:

2ℎ𝑟

𝑇𝑠𝑔(𝑎 𝑚𝑒𝑎𝑠 cos 𝜙 + 𝑔 sin(𝜙 − 𝜃𝑟)) ≥ 1 − 𝜇 (25)

Where, µ is a safety margin used to account for unconsidered material properties of tires and chassis system. After rearranging (25), the maximum lateral acceleration sustainable by the vehicle could be expressed as:

𝑎𝑦,𝑚𝑎𝑥 ≥ (1 − 𝜇)𝑇𝑠𝑔

2ℎ𝑟 cos 𝜙−

𝑔 sin(𝜙−𝜃𝑟)

cos 𝜙 (26)

If the dynamic effects of vehicle rollover are not considered, the rollover, the maximum lateral acceleration sustainable by the vehicle can be simply given as [16][24]:

𝑆𝑅𝑇 =𝑎𝑦,𝑚𝑎𝑥

𝑔=

𝑇

2ℎ𝑐𝑔− 𝜑 (27)

Where, SRT is the Static Rollover Threshold (SRT) or Static Stability Factor (SSF) of the vehicle, and 𝜑 is the collection of other factors affecting rollover stability of a vehicle such as suspension system, tires, and chassis properties. According to the National Heavy Vehicle Regulator (NHVR), the minimum Static Rollover Threshold (SRT) limit of a vehicle holding dangerous goods is 0.40 g, while any other vehicle has this minimum limit set to 0.35 g [8], thus the maximum lateral acceleration has to be greater than the SRT limit

𝑎𝑦,𝑚𝑎𝑥 ≥𝑆𝑅𝑇𝑙𝑖𝑚𝑖𝑡

𝑔≥ (1 − 𝜇)

𝑇𝑠𝑔

2ℎ𝑟 cos 𝜙−

𝑔 sin(𝜙−𝜃𝑟)

cos 𝜙 (28)

To make the rollover prediction system more redundant, other input parameters such as vehicle speed and yaw rate could also be used to predict an impending rollover, since the lateral acceleration could also be described these parameters as:

𝑎𝑦 = 𝑣𝑥�̇� − 𝑔 sin 𝜃𝑟 (29)

Thus, the maximum vehicle speed and the maximum yaw rate can be described as:

𝑣𝑥�̇�𝑚𝑎𝑥 ≥ 𝑎𝑦,𝑚𝑎𝑥 + 𝑔 sin 𝜃𝑟 (30)

An early rollover warning system can have multiple warning levels, where different RI threshold levels can be defined for each type of alert.

Table 1 - Rollover alert threshold levels

Time-to-Rollover (TTR) technique has been used in the past [25][26], which determines the likelihood of rollover by predicting the time remaining in rollover based on the present trend of the

vehicle motion. In order to predict an impending rollover, the current rate of change of rollover index is averaged over the past one second. The average rate of change of the rollover index is

used to extrapolate rollover index data and predict the future

rollover index at time t+ .

𝑅𝐼𝑡+𝜏 = 𝑅𝐼𝑡 + Δ𝑅𝐼𝑎𝑣𝑔 × 𝜏 (31)

The rollover index (RI) can be predicted at 0.5 second

ahead of time by substituting =0.5 into (32).

𝑅𝐼𝑡+0.5𝑠 = 𝑅𝐼𝑡 + Δ𝑅𝐼𝑎𝑣𝑔 × 0.5𝑠 (32)

The time to rollover (roll) can thus be determined by rearranging equation (31):

𝑇𝑇𝑅 = 𝜏𝑟𝑜𝑙𝑙 =𝑅𝐼𝑚𝑎𝑥−𝑅𝐼𝑡

Δ𝑅𝐼𝑎𝑣𝑔 (33)

Where, RImax is the rollover index when vehicle becomes unstable, RIt is the current rollover index, and Δ𝑅𝐼𝑎𝑣𝑔 is the average rate of change of rollover

index over the past one second.

PREDICTION SYSTEM VERIFICATION

In order to verify the accuracy of the rollover prediction system, several test drives were performed at different driving manoeuvres. A rollover sensor module was constructed and mounted on a car. The sensor module consisted of a tri-axial accelerometer and a tri-axial gyroscope. The speed of the vehicle was taken from a vehicle module. Additionally, a GPS device was also mounted on the vehicle to obtain track log of the vehicle’s movement.

The first test was carried out at a freeway entrance, which had the shape of a long U (Figure 3). The vehicle was travelling fast at about 40 km/h when it entered the long U-turn. The rollover system detected an impending rollover event and it raised a warning alert. The speed was then gradually slowed down to keep the vehicle stable and in control.

Alarm type Typical threshold level

Warning Alert RIwarn = RImax * 0.40

Critical Alert RIcrit = RImax * 0.60


Figure 3. Test 1 – vehicle drive path.

Figure 4. Test 1 - vehicle speed and lateral acceleration.

Figure 5. Test 1 - results showing vehicle's roll angle and roll rate.

Figure 6. Test 1 - rollover index and predicted dynamic state.

A second test was done at a round-about, making a 270-degree turn. The approaching speed of the vehicle was quite high at 35 km/h. The vehicle had to be slowed down to keep it under control.

Figure 7. Test 2 - vehicle speed and lateral acceleration.

Figure 8. Test 2 - roll angle and roll rate.

0 1 2 3 4 5 6 725

30

35

40

45Vehicle Speed (Vx)

Speed (

kph)

0 1 2 3 4 5 6 7-0.2

0

0.2

0.4

0.6Lateral Acceleration (ay)

Accele

ration (

g)

time (s)

0 1 2 3 4 5 6 7-2

0

2

4

6Roll angle

Angle

(deg)

0 1 2 3 4 5 6 7-100

-50

0

50Rotation rate

Rota

tion r

ate

(d/s

)

time (s)

roll rate yaw rate

0 1 2 3 4 5 6 7-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Rollo

ver

Index

time (s)

Rollover Index (RI)

critical

actual

predicted

alert

0 2 4 6 8 10 12 1420

25

30

35Vehicle Speed (Vx)

Speed (

kph)

0 2 4 6 8 10 12 14-0.5

0

0.5

1Lateral Acceleration (ay)

Accele

ration (

g)

time (s)

0 2 4 6 8 10 12 14-2

0

2

4

6Roll angle

Angle

(deg)

0 2 4 6 8 10 12 14-100

-50

0

50Rotation rate

Rota

tion r

ate

(d/s

)

time (s)

roll rate

yaw rate


Figure 9. Test 2 - rollover index and predicted dynamic state.

CONCLUSION

The rollover prediction techniques described here considered multiple parameters, which relate to the dynamics of the vehicle, to accurately predict an impending rollover situation. Once an impending rollover event is accurately predicted, the rollover occurrence can potentially be prevented by means of alerting the driver of the rollover risk as indicated in this paper; or by activating a control prevention system, which includes active braking [27][28] for controlling vehicle speed, active steering for controlling yaw rate [29], anti-roll bar [30], and active suspension system [31][32]. The results from the field test conform the validity of the proposed prediction system, where the predicted rollover index at 0.5 second ahead of time was close to the actual rollover index. The techniques described here have already been implemented in the AutoMonitor product manufactured by AutoTest Products Pty Ltd.

REFERENCES

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15. Trent, V., Greene, M., “A Genetic Algorithm Predictor for Vehicular Rollover,” IECON -Proc.- 3:1752–1756, 2002.

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0 2 4 6 8 10 12 14-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Rollo

ver

Index

time (s)

Rollover Index (RI)

critical

actual

predicted

alert


17. A., Brown, T., Martens, J., Hac, “2004-01-1757 Detection of Vehicle Rollover,” SAE SP (1869):153–161, 2004.

18. Chou, T., Chu, Tzyy-Wen, “An improvement in rollover detection of articulated vehicles using the grey system theory,” Veh. Syst. Dyn. 52(5):679–703, 2014.

19. Larish C., P.D., Tsourapas V., Rajamani R., “A new predictive lateral load transfer ratio for rollover prevention systems,” IEEE Trans Veh Technol IEEE Trans. Veh. Technol. 62(7):2928–2936, 2013.

20. Zhu Tianjun and Zong Changfu, “Research on Rollover Early Warning Algorithm for Heavy Tractor- Semitrailer Based on PTR Metric,” Intelligent Systems and Applications, 2009. ISA 2009. International Workshop on, 1–4, 2009, doi:10.1109/IWISA.2009.5073081.

21. Imine, H., Benallegue, A., Madani, T., and Srairi, S., “Rollover risk prediction of an instrumented heavy vehicle using high order sliding mode observer,” Robotics and Automation, 2009. ICRA ’09. IEEE International Conference on, ISBN 1050-4729: 64–69, 2009, doi:10.1109/ROBOT.2009.5152185.

22. Rajesh Rajamani, “Vehicle Dynamics and Control,” Springer-Verlag New York Inc., New York, NY, 2012.

23. E. J. Wallner and J. K. Schiffmann, “Automotive Rollover Sensing,” Advanced microsystems for automotive applications 2000, Springer, Berlin; New York, ISBN 3540670874 9783540670872: 205–217, 2000.

24. Rajamani, R., “Roll Dynamics and Rollover Prevention,” Vehicle dynamics and control, 427–456, 2012.

25. Yu, H., Guvenc, L., Ozguner, U., “Heavy duty vehicle rollover detection and active roll control,” Veh. Syst. Dyn. 46(6):451–470, 2008.

26. Wang Jian, Yu Guizhen, Li Zhenhua, Zhang Wei, and Ding Nenggen, “Real-time roll state estimation and rollover prediction for light SUVs,” Mechatronics and Automation, 2009. ICMA 2009. International Conference on, 4387–4392, 2009, doi:10.1109/ICMA.2009.5246635.

27. Bo-Chiuan Chen and Huei Peng, “Differential-Braking-Based Rollover Prevention for Sport Utility Vehicles with Human-in-the-loop Evaluations,” Veh. Syst. Dyn. 36:359–389, 2001.

28. Gaspar, P., Szabo, Z., and Bokor, J., “Prediction Based Combined Control to Prevent the Rollover of Heavy Vehicles,” Intelligent Control, 2005. Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation, ISBN 2158-9860: 575–580, 2005, doi:10.1109/.2005.1467078.

29. Mammar, S. and Koenig, D., “Vehicle Handling Improvement by Active Steering,” Veh. Syst. Dyn. 38(3):211–242, 2002, doi:10.1076/vesd.38.3.211.8288.

30. Cronje, P.H., Els, P.S., “Improving off-road vehicle handling using an active anti-roll bar,” J. Terramechanics 47(3):179–189, 2010.

31. Shim, T., Velusamy, Pradheep C., “Improvement of vehicle roll stability by varying suspension properties,” Veh. Syst. Dyn. 49(1-2):129–152, 2011.

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CONTACT

Muhammad Alamgir AutoTest Products Pty Ltd Phone: +61 3 9647 9797 Email: [email protected]

DEFINITIONS

: pitch angle

: roll angle

r: road bank angle

roll: time left in rollover

ameas: measured lateral acceleration

ay: lateral acceleration

c.g.: centre of gravity

c.r.: centre of roll

Fnet: net force

Fy: externally exerted lateral force

H: maximum height of the vehicle

hbase: load base height

hcg: current centre of gravity height

hr: roll height (hcg – hbase)

m: vehicle mass


Mx: external rolling moments

R: turn radius

T: track width

Ts: suspension track width

TTR: time-to-rollover

v, vx: longitudinal vehicle speed

W: gravitational weight force


Gold Award for Manufacturing

AutoCRC Ltd, Victorian Partnership for Advanced Computing, Malaysia Automotive Institute,

vMould – Rapid Plastic Injection Mould Design Optimisation

Overview Mass reduction is directly linked to vehicle efficiency and performance improvement. Injection-moulded plastic

parts represent a significant proportion of the mass and cost of a modern vehicle. Manufacturers are looking

to automation to reduce costs, and whilst the majority of associated processes have been automated, an

exception is the challenging area of mould design and production. The Victorian Partnership for Advanced

Computing, in collaboration with the AutoCRC, the Malaysian Automotive Institute, Sipro Plastics and Dolphin

Products have developed a user-friendly software package, vMould, to automate the mould design and

production process. The collaboration resulted in the vMould Package which has global application in improving

component quality and reducing cycle times, thus reducing the mass and costs of plastic components.

Judges Synopsis vMould software received the Gold award in the Manufacturing Category for the innovative use of knowledge

from specialist tool makers, the application of flow analysis and the focus to reduce manufacturing time and

cost, to create a useful and accessible tool for industry.


vMould – Rapid Plastic Injection Mould Design Optimisation

Jarrod Sinclair VPAC Innovations

ABSTRACT

vMould is a new software approach to plastic injection mould design optimisation that directly supports the engineer with critical design directions pertinent to the best practices and workflows already used in industry. It includes six main features that cover CAD quality assessment, draw direction identification, the ideal location for a single gate, locations for air vents to ensure quality filling, the shape of the cavity required to compensate for warpage and shrinkage, and finally identification of hot spots required for cooling design. The software is delivered using the cloud as a web-based application that abstracts away the complexities of numerical computations used by each optimisation feature. This approach lowers the total cost of operation by industry, including associated software, hardware and staff training.

A multidisciplinary team of computational analysis engineers and software developers conducted almost two years of research and development to produce vMould. This included close collaboration with industry leading plastic injection mould manufacturers. The collaboration was critical in identifying actual business and engineering processes used to form the optimal mould required for start of production. vMould was designed to directly support these industry processes without dictating yet another software workflow.

As a result, vMould has proved to be quick to adopt, easy to operate, and effective in improving the quality and lowering the time to market of plastic injection moulds. The software delivers productivity gains by automating the baseline business processes of mould design, and frees up time for the engineer to focus on driving innovation in the organisation.

INTRODUCTION

The manufacture of plastic injection moulded parts is

a key process in vehicle production. Some plastic

parts include door panels and handles, interior

components, instrument panels, bull bars,

powertrain components and many more. Plastic is

chosen for these parts as it is relatively inexpensive,

provides structural efficiency, and can take almost

any shape defined by the vehicle styling and

functional design requirements.

The plastic injection mould manufacturing process

involves a significant amount of expertise to ensure

consistently high quality parts are produced at cost

competitive rates. The ideal mould and machine

setup is a large investment in both time and cost.

Typically, prior experience of the manufacturer and

toolmaker drives the efficiency of this setup stage.

To gain greater competitiveness, additional

technologies are required to assist the engineers

throughout the design and production process.

Software known as Computer Aided Engineering

(CAE) can be used, however the impact on the

overall business efficiency needs to be studied in

order for improvements to be made.

This work focuses on the use of CAE in the design

of plastic injection moulds, particularly for the key

decision making steps early in the engineer’s

workflow. A new software suite known as vMould is

presented, that provides a novel approach to direct

optimisation of the key design steps. The software is

developed as a web application that can be easily

deployed and exploited for efficiency gains in the

plastic injection mould design process.

Objectives and Outline

This work was split into three main stages:

1. Research best practices for mould design at

Australian and Malaysian plastics manufacturers

and toolmakers, focusing on the daily use of CAE

tools to improve business efficiency.

2. Develop new CAE methods to address gaps in

current workflows in industry.

3. Implement, deploy and test the CAE methods to

industry partners.

This paper will present the main highlights and

developments of these three stages, providing

industry examples where appropriate.


RESEARCH FINDINGS

Consultation and collaboration with industry partners

has elucidated that there are approximately twelve

key decision points in the design of a plastic injection

mould suitable for mass production. These are

typically conducted sequentially, and include:

1. Determination of the best draw direction and

therefore the part orientation in the mould

(sometimes mistakenly viewed as locked in by

the product designer, and not the mould

designer).

2. Identification of manufacturing enablers such as

slides required to ensure the part can be

produced using a two-piece core and cavity

setup.

3. Incorporation of robust draft angles to prevent

material failure during part ejection.

4. Determination of the injection point for the plastic

melt to flow through a gate into the mould volume.

5. Determination of air vent locations to ensure melt

flow without air pockets and other quality

degradations.

6. Identification of hot spots in the tool and design of

liquid cooling channels to reduce the hot regions.

7. Allowance for shrinkage and warpage of the

plastic part during the cooling phase, using

standard shrinkage rates and trial-and-error with

physical tests.

8. Build complete tool structure and mechanisms in

CAD.

9. Select best tool material based on production

volumes and loads (such as injection pressures

and clamp forces).

10. Fabricate the mould using CNC or other

processes, apply material treatments (such as

heat treatment) and assemble for testing.

11. Commence physical testing (known as mould

trials), adjusting process parameters and

modifying the mould design were available.

12. Conduct detailed 3-dimensional CAE analysis to

help fix any issues that are not easily understood

with the physical testing. Fixes at this stage

require re-fabrication of the mould once

improvements have been made.

It can be seen from the sequence of decision points

above that industry does not generally employ CAE

throughout the design process. Rather, detailed CAE

analysis is initiated once a mould has been fabricated

and there is need to fix issues and improve the

design. Furthermore, it is common practice in industry

to use a “guess and iterate” method when deciding on

the design direction throughout the twelve steps,

sometimes employing detailed CAE either in-house

or with third-parties if it is suspected that issues may

arise. This adoption model of CAE by industry is

costly and inefficient.

We conclude that newer CAE methods are required

to directly assist the engineer in making informed

decisions during the twelve steps, in order to reduce

issues with the mould design that are experienced

downstream in the process. Less rework of the

mould, and ultimately no re-fabrication before the

start of production, will lead to greater productivity

and efficiencies of the entire business. Therefore,

CAE needs to closely match the best-practices used

by industry and not formulate a new workflow.

The goal for vMould is to provide a rapid and easy to

use series of software tools that can propose ideal

solutions for each step, so that the design engineer

can integrate the solutions along with their expertise

to develop the optimal mould faster than if they used

only their expertise. In this way vMould can be used

as either a confirmation step or an idea generator

depending on the degree of mastery of the toolmaker

KEY FEATURES

Six main features were developed to address the

shortcomings of current CAE tools used by industry

for plastic injection mould design optimisation. The

rationale, algorithm and user experience for each

feature is described below.

Part and Mould Geometry

CAD is the central software tool for all aspects of

engineering design of plastic injection moulds and

parts. Specifically it includes:

The plastic part is defined in CAD by the client

wanting the plastic parts, or in-house in

consultation with the automotive company.


The shape of the mould is created in CAD from

the geometry of the plastic part and other

engineering inputs, such as draw direction.

The tool design and mechanisms are designed

and mocked up in CAD, and generally CNC

machined during fabrication.

Figure 14: CAD Quality assessment feature

However there are many CAD applications and file

formats. Therefore, vMould uses the neutral STL

format as it is highly portable and compatible with all

CAD software. STL is a surface triangulation of the

CAD geometry, rather than a mathematical function

such as NURBS. Files need to be refined in areas of

high curvature in order to discretise the shape whilst

still capturing the surface smoothness. vMould

includes quality checks to ensure that the STL file is

appropriate for use during the optimisation steps.

Figure 14 shows the web interface for the CAD

Quality assessment tool using a representative

plastic part from industry. The checks include

identifying error regions for:

1. Water-tightness (free edges).

2. Degenerate surfaces (t-junctions).

3. Self-intersections (generally formed from

insufficient refinement in areas of high curvature).

Identification of STL errors is typically symptomatic of

fundamental CAD issues, and serves as a good guide

for general CAD quality. Issues that are identified by

vMould can be corrected in CAD and re-analysed to

ensure correctness. Once the part contains no STL

errors, the optimisation processes below can

commence.

Draw Direction Optimisation

The first decision point in mould design at industry is

determining the orientation of the part within the

mould. This may seem like an obvious process,

however it is a critical decision that could lead to

significant rework and additional costs if a non-

optimal orientation is chosen. Furthermore, designers

of plastic automotive parts may specify their chosen

orientation, but this critical business decision should

be not be outsourced, and must be completely

controlled by the plastic injection mould

manufacturer.

Figure 15: Draw Direction algorithm

Draw direction provides the split plane of both sides

of the two-piece mould, and the extraction vector

when the part is ejected after filling, cooling and

packing. The optimal draw direction is given by the

orientation that reduces the amount of manufacturing

enablers such as slides and other actuators, and

preserves the surface finish quality requirement.

Typically, experience with similar parts guides the

decision in industry, however this becomes difficult for

unique and complex plastic parts. vMould provides a

more quantitative method to arrive at the most optimal

draw direction that takes into account the 3D part

geometry and key features such as ribs, walls and

holes.

A bi-directional shadow analysis was developed

using computational visualization techniques


including ray casting and occlusion detection, as

shown in Figure 15. The main process includes:

1. Select a part orientation vector.

2. Compute the lit portions of the surfaces in the

draw direction using ray casting.

3. Compute the lit portions of the surfaces in the

opposite draw direction.

4. Boolean the two fields in order to classify the

base, draw and slide portions of the surface.

5. Compute the surface area of each classified

portions.

6. Iterate for all orientation vectors in order to find

the minimal slide surface area.

Figure 16: Draw Direction feature

The algorithm implemented into vMould is shown in

Figure 16 for an automotive door handle part. It can

be seen that the user simply uploads the part

geometry (as an STL file) and runs the draw direction

optimisation using the default settings in this case.

The computations are run in the background on cloud

servers, and the user can monitor the progress via the

web interface. Once the optimal solution has been

found, the results are presented to the user visually,

and the relevant CAD files can be downloaded and

used in any CAD tool for further processing.

Numerical optimisations were developed in the draw

direction tool to intelligently reduce the amount of

orientations processed using a multilevel grid

refinement algorithm, leading to a total run time of a

few minutes.

Gate Locator

In mass production of plastic parts, it is desirable to

inject the plastic melt into the mould cavity and fill the

volume completely in the shortest period of time

whilst ensuring a quality resulting plastic part. A naïve

solution would suggest that numerous injection

locations would be beneficial to achieve the best

outcome, however seam lines and other quality

defects are introduced when more than one location

is used in production environments. Therefore,

industry typically limits the number of locations to only

one gate, and its placement is critical to achieve

desired production rates.

For vMould, an idealized gate locator was developed

to assist the design engineer in this vital decision. The

method was based on a recursive graph

decomposition. The process included:

1. Discretize the volume of the mould cavity into

voxels at a given resolution

2. Arrange the voxels based on their network

connectivity

3. Split into two equally weighted groups, determine

centroid location

4. Repeat with successively refined voxels until

convergence in centroid location value

Figure 17: Gate Locator feature

The resulting implementation is shown in Figure 17.

Whilst there is no graphical representation of the gate

location within the web interface, the user can easily

import the resulting coordinate into CAD in order to

continue mocking up the overall tool design.

Air Vents Locator


Before the plastic melt is injected into the cavity of the

mould, the pieces of the tool are clamped together to

form a watertight seal. This is done with enough force

to resist injection pressures that can prematurely

open the mould during filling. To ensure conservation

of mass during this filling stage, venting locations are

required for air to gather and escape. In the right

positions, the air vents ensure the plastic is filled with

high quality due to minimal trapped air pockets and

other fouling sites.

An experienced plastic injection engineer will have a

good understanding of the main locations for air vents

given the cavity geometry and injection gate location.

However, for complex and non-standard plastic parts,

or to gain further production efficiency from increased

injection speeds, it can be difficult to predict all of the

ideal air vent locations based purely on prior

experience. This is because the flow of melt during

filling is highly non-linear, transient and three-

dimensional in space.

A rapid air vent locations advisor was developed for

vMould that takes into account the non-linear fluid

dynamics process of melt filling and indicates the

ideal locations for vents on the surface of the mould

geometry. The process involves:

1. Discretise the cavity volume into a three-

dimensional mesh suitable for CFD.

2. Define the gate location on the surface of the

mould geometry (can be specified from the Gate

Locator tool discussed previously).

3. Implement a pseudo-outlet boundary condition at

all wall boundaries of the cavity domain

(excluding the inlet gate).

4. Compute the advancing front of the melt filling

process with a multiphase finite volume model.

5. Rank the localized outflow rates and isolate key

air vent locations.

Figure 18: Air Vents Locator feature

Figure 19: Suggested locations for air vents on an automotive door handle

Figure 18 shows the implemented air vents locator

feature, and Figure 19 shows the locations of air vents

on a 3D part. It be seen that the tool predicts air vent

locations as required at the end of enclosed volumes,

which allow air to flow out of the cavity in order for

plastic melt to properly fill the domain.

Cavity Shape Optimisation

One of the most challenging mould design steps is

compensating for the correct amount of shrinkage

that occurs when the plastic melt solidifies during and

after injection to form the final plastic part. Typically,

uniform volume percentage shrinkage rates are used

from best practices of the material and the part type.

However, these spatially uniform values are not

appropriate for increasingly more three-dimensional

and unique plastic automotive parts.


Figure 20: Cavity Shape algorithm

Figure 21: Warpage of the converged cavity shape compared to the desired part geometry

An iterative solution method was developed for

vMould that helps predict the ideal cavity shape

accounting for non-uniform thermal warpage during

the solidification process. A three-dimensional

thermal/structural model was created of the original

mould cavity shape, and subjected to the thermal

loads imposed due to polymer phase change. Given

the deflection response of the material, a second

candidate cavity shape was created to compensate

for this deflection in order for the final warpage to

more closely align with the desired part geometry.

The process is continued until the final post-deflection

shape closely matches the desired part. This is

shown in Figure 20 for the automotive door handle,

where the colours represent various iteration steps in

the optimisation process. Figure 21 shows the

deformation of the final cavity shape in red, and that

it closely matches the required part geometry in white.

Cooling Design

Hot spots exist at various locations throughout the

material of the tool due to the high temperature of the

plastic melt required for flow in the filling stage. During

and after this stage, heat is transferred via conduction

to the mould itself and needs to be dissipated in order

to promote proper solidification of the plastic part.

Typically, cooling channels of flowing water are used

to transport this heat energy out of the system.

However, it is a design challenge to determine the

best location and flow rates to apply the cooling

strategy. Again, best practices and prior experience

guides the selection of this. This is difficult to optimize

for complex plastic parts that involve many conjugate

regions for thermal diffusion.

To provide a more quantitative solution, a thermal

model of the conjugate heat transfer was developed

in vMould. For this, a combined pseudo-fluid/solid

model of the solidifying plastic melt and the metal

material tool was developed in three-dimensions.

This involves the following process:

1. Discretise the cavity into a inner fluid domain of

volumetric mesh elements, and a surrounding

solid domain.

2. Incorporate the thermal characteristics from the

filling simulation conducted in the air vent locator

tool.

3. Allow for conjugate heat transfer into the solid

domain.

4. Track local maxima of temperature, and

determine the rate of energy extraction required

for the ideal cooling design.

SOFTWARE DEPLOYMENT

To ensure the mould design optimisation features

presented above are delivered to the engineer in the

simplest form possible, a new cloud based software

design was implemented into vMould. This provides

the lowest total cost of operation to the end-user, as

only a web browser is required without needing

expensive software, hardware or networking onsite.

Further, the nature of web-based software is for

simplified user interfaces that do not require training

or significant expertise to operate. The quintessential

example of this approach is the Google search

interface that anyone can use without knowledge of

the complex algorithms or substantial computations

conducted behind the scenes.


Figure 22: CAD Quality assessment feature

Web-services were used to wrap each optimisation

feature discussed above, producing self-sufficient

components defined by an input and output interface

known as an Application Programming Interface

(API). This is shown for the entire infrastructure in

Figure 22. Web services were chosen as they are

simple to define using the REST specification that is

compatible with most internet ready connections. The

JSON language was used to communicate the

specifics of the input and output data fields, as it is

easy to parse and is commonly used for modern web-

service APIs.

The front-end website of vMould, that is the main

interface that the user interacts with, was defined as

a separate server programmed in HTML, CSS,

JavaScript and higher level libraries jQuery and

AngularJS. This ensured portability and easier

maintainability with advancements of the web

platform. The front-end website communicates with

the back-end computational servers as the user

requests to start and monitor the optimisation

features. The design and orchestration of the vMould

web servers allows for flexible scalability based on

the number of concurrent users requesting

optimisation tasks. This exploits the benefits of

modern cloud architecture, that is inexpensive to

operate at scale and transparent to the end-user.

SUMMARY/CONCLUSIONS

This paper summarized the research, development

and technical details of a new software tool “vMould”,

developed exclusively for the plastic injection

moulding industry. Firstly, the best-practices of

industry partners were evaluated and opportunities

for productivity gains using software automation were

identified. Next six independent features were

developed to aid in the decision making process of

mould design engineers, utilising numerical

computation and optimisation. Algorithms were

developed using visualization methods,

computational geometry, fluid dynamics, structural

mechanics and thermal flows. Finally, these were

implemented into a software suite using web-services

to provide scalability. The final software is delivered

to the end-user via a web browser, and does not

require significant training or any onsite infrastructure

costs. As a result, vMould provides a unique

productivity gain to the plastic injection mould

manufacturing industry. Tests with industry partners

has shown substantial efficiency gains, and added

confidence in the decisions required to start

production of plastic parts.

Future improvements to vMould will focus on deeper

integration with CAD software via plugins in order to

reduce the dependence on STL file uploading to run

the tools. Also, vMould will connect to manufacturing

machines to further automate the optimisation of

process parameters throughout production.

ACKNOWLEDGEMENTS

We would like to thank the AutoCRC and the Malaysia

Automotive Institute (MAI) for providing the

collaborative framework, financial support and

industry vision to conduct this work. Also, thanks to

our industry partners Sipro Plastic Industries Sdn Bhd

and Dolphin Products Pty Ltd for providing invaluable

industry knowledge and an environment to trial ideas.

CONTACT

Jarrod Sinclair VPAC Innovations



Student Gold Award

Tyler Plowright, University of Newcastle,

Aerodynamics of Australian Coal Trains

Overview Tyler Plowright from the University of Newcastle and Pacific National carried out investigation into the

aerodynamics of Australian coal trains and wagons. The aim of the research was to look into potential

aerodynamic drag reduction designs for both locomotive and coal wagons that would help to improve the

energy efficiency of the train and wagon and therefore reduce fuel consumption. The research also looked into

the different aerodynamic flow that are present around and inside the coal wagon.

The study assessed a variety of current and future technologies that improve aerodynamic drag, and the pro

and cons of these designs. Four of these designs were modelled and analysed using Computational Fluid

Dynamics to calculate the drag reduction and determine the fuel saving.

CFD simulation has shown that by reducing the aerodynamic drag in the inter-wagon gap has the best results

for drag reduction and fuel consumption. The calculated fuel savings can be 4.5% for vortex generators or

13.5% for inter-wagon shrouds for an unloaded train.

This study will allow for better informed decisions on the potential benefits and costs associated with adopting

drag reducing devices.

Judges Synopsis Tyler’s study of the Aerodynamics of Australian Coal Trains received Gold in the Student Category for his

application of aerodynamics and CFD to help improve efficiencies in Australian Coal Train operations,

highlighting the potential for improvement and savings in this industry. Tyler carried out both computer

simulations and actual testing to demonstrate to validity of the data he has presented.


Student Highly Commended Award

Marc Russouw, Monash University,

Development of a Vehicle and Tyre Model for Formula SAE-A

Overview The interactions of surfaces have puzzled engineers and scientists for decades, and tyres have remained one

of the most complex elements of car design. Marc Russouw has taken on this challenging problem by using a

combination of established testing methods and engineering analysis tools to produce an efficient method to

capture tyre performance data and incorporate this into vehicle models for improved vehicle modelling.

Judges Synopsis The breadth of work undertaken by Marc Russouw was well defined and is a fine example of how engineers

can use scientific knowledge for substantial and practical benefit.


Development of a Vehicle and Tyre Model for Formula SAE-A

Marc Russouw Monash University

ABSTRACT

A broad spectrum of modelling techniques exists for

predicting tyre performance and force characteristics

under various operating conditions. These range from

purely theoretical models to semi-empirical fits of data

collected from constrained or on-vehicle testing. This

paper puts forward a combination of existing

techniques for modelling the performance of a racing

slick tyre using data obtained from constrained tyre

testing with the aim of reducing computational

expense and capturing salient differences in tyre

behaviour under changes in inclination angle,

pressure and normal load.

The force vs. slip data is non-dimensionalised and

compressed such that a single characteristic curve

taking into account the above parameters can be

fitted and then expanded. Response surface

modelling of the characteristic curve coefficients is

also used to interpolate tyre performance between

the test data points. These non-dimensional fits are

then expanded for any combination of the input

parameters that are fed into the model.

This information is subsequently used to create

simple steady-state vehicle models based on both

constant radius and constant velocity constraints to

investigate vehicle turn-in and limit performance.

Additionally, this paper presents steady-state testing

of a Formula SAE-A vehicle which attempts to

validate model behaviour and reconcile differences

between the data obtained from constrained testing

and that obtained from the vehicle.

INTRODUCTION

Maximising the overall dynamic performance of a

Formula SAE-A car requires an understanding of how

the tyres and vehicle as a whole behave under the

various conditions encountered and manoeuvres

performed on a track. Milliken and Milliken [4]

developed the Milliken Moment Method in order to

simulate the performance of a vehicle under constant

corner radii and velocities in terms of yaw and lateral

motion. To be able to construct a steady-state model

it is however first necessary to model the tyre force

outputs for given pressures, inclination angles and

vertical loads. Tyre data was obtained from

constrained testing done by the Tire Testing

Consortium at Calspan [3] using the setup shown in

figure 23 below:

Figure 23. Constrained tyre testing rig at Calspan TIRF, pictured with 13" Formula SAE tyre mounted [3].

The coordinate system used in this paper for force

and slip quantities is the same as that defined for

tyres by the SAE [9] as seen in figure 24.

Figure 24. SAE Tyre coordinate system.

The technique of non-dimensionalising tyre data was

first developed by Radt [3] and is one of the simplest

manners in which to obtain fits to force vs. slip data

obtained empirically. The transformations applied to


the data using this technique yield a dimensionless

tyre lateral force F ̅ and also slip angle �̅�. These are:

�̅� =𝐹𝑦

𝜇𝑦𝐹𝑧 (1)

�̅� =𝐶𝑠𝑙𝑖𝑝 tan 𝛼

𝜇𝑦𝐹𝑧 (2)

𝐶𝑠𝑙𝑖𝑝 = 𝜕𝐹𝑦

𝜕𝛼|

𝛼=0 (3)

Where 𝐹𝑦 is the lateral force in N, 𝐹𝑧 is the normal

force in N, 𝜇𝑦 is the maximum lateral force coefficient,

𝛼 is the slip angle in radians and 𝐶𝑠𝑙𝑖𝑝 is the cornering

stiffness in N/rad. For the extensions of these

quantities to modelling longitudinal force and

moments, see [4].

Due to asymmetries in the construction of the tyre as

well as inclination angle effects, two additional

dimensionless parameters are used to shift the

compressed curve (see figure 25 below) to the origin

of the non-dimensional force vs. slip graph [5].

𝑉𝑦 = 𝐹𝑦

𝐹𝑧|

max(𝐶𝑠𝑙𝑖𝑝) (4)

𝐻𝑦 = �̅�|max(𝐶𝑠𝑙𝑖𝑝) (5)

Where 𝑉𝑦 and 𝐻𝑦 are the dimensionless vertical and

horizontal shifts. The effects of cornering stiffness are

unable to be taken directly from the raw data, as the

hysteresis during the tyre slip angle sweeps creates

two different curves passing the zero force point on

the force vs. slip curve. To overcome this, the two

positive slip sweeps were averaged and this was then

averaged with the negative slip sweep (see figure 26)

such that the bias from the two positive sweeps would

be removed. A third order polynomial was fitted to this

data between -2.5 and 2.5 degrees slip angle and

shifts obtained from the intercepts along with the

gradient (cornering stiffness) at the ‘shifted’ origin

(see figure 26). This allowed the parameters required

to non-dimensionalise the data prior to fitting the

‘Magic Formula’ in (6) and (7) to be determined.

Figure 25. Force vs. time plot showing 2 positive (1 partial) and 1 negative force sweep.

Figure 26. Polynomial fit to positive and negative sweeps.

The curve to fit takes the following form:

�̅� = 𝐷 sin (𝐶 𝑎𝑡𝑎𝑛 (𝐵 {(1 − 𝐸)�̅� +𝐸

𝐵𝑎𝑡𝑎𝑛(𝐵�̅�)})) (6)

However, this can be further simplified as shown in

[5] to the following relation:

�̅� = sin (1

𝐵𝑎𝑡𝑎𝑛 (𝐵 {(1 − 𝐸)�̅� +

𝐸

𝐵𝑎𝑡𝑎𝑛(𝐵�̅�)})) (7)

Applying transforms and shifts 1-7 to the raw data

then enables all of the curves to be compressed into

a single characteristic line as shown on the right in

figure 27.

Figure 27. Raw Data (Left) vs. Non-Dimensional Transform (Right)

INPUT PARAMETER EXTENSION

Both Kasprzak and Patton [2,5] extend the number of

operating parameters to be input into the model by


fitting response surfaces for each of the six model

parameters required for the Magic Formula in (7).

Instead of fitting one line to the compressed data,

multiple lines are fitted and the B and E coefficients

obtained for equation (7) using a non-linear least-

squares fit. This data can then be represented as 2nd

order surface fits to enable continuous estimation of

the parameters given inputs within the tested range.

Dimensionless cornering stiffness, for example, is

represented as:

𝐶𝑠𝑙𝑖𝑝̅̅ ̅̅ ̅̅ = 𝑓(𝐹𝑧 , 𝛾) (8)

Figure 28. Response Surface fit for dimensionless cornering stiffness (see Appendix for the other parameter fits)

Given that there is only information for 3 inflation

pressures available (10, 12, 14 psi), adding a

pressure parameter into the model is achieved via

additional surfaces. This is shown for maximum force

coefficient in figure 29 below:

Figure 29. Surface fitting for maximum lateral force coefficient for 3 discrete pressures.

Once all of these surfaces have been collected and

stored as coefficients to the quadratic surface fit given

by (9), the model can be expanded for any

combination of inclination angle, slip angle, normal

load and discrete inflation pressure points that fall

within the tested range. Figure 30 is an example of

model expansion across the tested normal loads and

this shows good agreement with the constrained

testing data.

𝐶𝑠𝑙𝑖𝑝̅̅ ̅̅ ̅̅ = 𝐶0 + 𝐶1𝐹𝑧 + 𝐶2𝛾 + 𝐶3𝐹𝑧

2 + 𝐶4𝛾2 + 𝐶5𝐹𝑧𝛾 (9)

Figure 30. Non-dimensional Model fit (black line) vs. raw data.

Furthermore, the model’s response to varying

inclination angles and pressures is illustrated by

figures 31-32.

Figure 31. Model response to variation in pressure.

Figure 32. Model Response to variation in inclination angle.

The method described above can also be extended

to fitting pure longitudinal slip data. All that is required

is to replace the lateral force and slip quantities with

their longitudinal analogues. However, instead of

equation (2), from [5] the normalized slip quantity

becomes:

�̅� =𝐶𝑥𝜅

𝜇𝑥𝐹𝑧 (10)

𝜅 =𝑁𝑅0−𝑉 cos 𝛼

𝑉 cos 𝛼 (11)

𝐶𝑥 = 𝜕𝐹𝑥

𝜕𝜅|

𝜅=0 (12)

Where 𝜅 is the slip ratio, V is axle speed in m/s (or

belt speed in the case of the constrained test), 𝑅0 is


the rolling radius in m for the tyre at a slip angle of 0

and 𝐶𝑥 is the longitudinal slip stiffness of the tyre in N.

BICYCLE MODEL

Once the tyre model is established, a simple 3 DOF

‘bicycle model’ can be created (see figure 33). The

coordinate system used is the same as that of [9].

Figure 33. Bicycle model setup [7]

To analyse the vehicle’s steady-state behaviour the

Moment Method as described in chapter 8 of [4] is

employed. There are two types of moment method

diagrams (MMD), the 𝐶𝑁 − 𝐴𝑦 diagram which

corresponds to constant velocity manoeuvres around

different path radii and assumes that all of the lateral

force produced by the vehicle must maintain the

vehicle in the trim condition (equal the centrifugal

force) [6].

𝐶𝑁 =𝑁

𝑚𝑔𝑙 (13)

𝐴𝑦 =𝐹𝑦

𝑚𝑔 (14)

𝑟 =𝐴𝑦

𝑉 (15)

This type of diagram is most useful for simulating

vehicle behaviour under a variety of operating

conditions and can be incorporated into a lap

simulation algorithm.

The second type of representation is the 𝐶𝑁 − 𝐶𝑦

diagram where path radius is constant through for

each steered angle/CG slip angle grid point and

velocity varies. The previous constraint where the

lateral force generated by the vehicle balances the

acceleration of the vehicle mass no longer needs to

be met and can be used to resist aerodynamic side

forces or any other lateral force disturbances the

vehicle encounters (see [5]). This is the more

generalized of the two forms and is more useful for

tuning setup for a particular corner radius (a situation

encountered in the Skid Pad event for a Formula SAE

car).

The 𝐶𝑁 − 𝐶𝑦 diagram is used for the bicycle model

and has the following input parameters:

Table 2. Input Parameters for Bicycle Model CN-CY diagram

The two dependent parameters, front steered angle

𝛿𝐹 and CG slip angle 𝛽𝐶𝐺 (both in degrees) are used

to construct a grid of combinations with each

parameter varied from -10° to 10° in 0.5° increments

.The algorithm requires both a starting velocity and

acceleration for each point and the convergence

process is described by the flowchart in figure 34.

Figure 34. Flow chart for bicycle model C_N-C_Y diagram.

Weight transfer is neglected for the model and thus,

for the tyre normal loads, equations (16) and (17) are

used.

Wheelbase 1.55 m

Mass 280 kg

Weight Distribution 50:50 Front:Rear

Radius 15 m

Camber Angle 0° Front & Rear

Toe Angle 0° Front & Rear

For 𝑅 = 15 𝑚 point

𝛿𝐹 = 1° and 𝛽𝐶𝐺 = 0°

Calculate tyre slip

angles and 𝐹𝑧

Guess 𝐴𝑦, 𝑉

Calculate tyre lateral force

given by tyre model.

Calculate 𝑉𝑛𝑒𝑤 and

𝐴𝑦 𝑛𝑒𝑤

Is 𝐴𝑦 𝑛𝑒𝑤 − 𝐴𝑦 𝑜𝑙𝑑 < 𝜖 ?

No

Is 𝑉𝑛𝑒𝑤 − 𝑉𝑜𝑙𝑑 < 𝜖 ?

𝐴𝑦 𝑛𝑒𝑤 = 𝐴𝑦 𝑜𝑙𝑑 + 0.1

𝑉𝑛𝑒𝑤 = 𝑉 𝑜𝑙𝑑 + 0.1

No

Yes

Yes

Grid point converged,

move to next 𝛿𝐹 and 𝛽𝐶𝐺

point


𝐹𝑧 𝑓𝑟𝑜𝑛𝑡 = 𝑚𝑔𝑎

𝑎+𝑏 (16)

𝐹𝑧 𝑟𝑒𝑎𝑟 = 𝑚𝑔𝑏

𝑎+𝑏 (17)

Where m is the vehicle mass in kg, g is the

gravitational constant 9.81 𝑚/𝑠2, a is the distance

from the CG to the front axle in meters and b is the

distance from the CG to the rear axle in meters. Once

the tyre loads are evaluated using the tyre model, the

following equations are used to evaluate the new

lateral acceleration and velocity values.

𝐴𝑦 𝑛𝑒𝑤 =∑ 𝐹𝑦,𝑖

2𝑖=1

𝑚 (18)

𝑉𝑛𝑒𝑤 = √𝐴𝑦𝑅 (19)

If the difference between the old and new values for

each of these quantities is less than a user specified

value, 𝜖, the solution has converged for that specific

point on the steer and CG slip angle grid.

Figure 35. Converged CN-CY diagram for Bicycle Model

Figure 35 shows the result, which as expected for the

model, is symmetric about both axes. To visualise

what happens along a line of constant steer, front and

rear lateral forces are plotted against CG slip angle in

figure 36:

Figure 36. Front and rear end lateral force contributions with steer angle held constant.

Figure 36 shows an offset in the 𝛽𝐶𝐺 direction such

that at 0 degrees chassis slip angle, the front end of

the vehicle is producing a net lateral force in the

positive direction. The magnitude of this force

corresponds to the same as that at the rear when the

CG slip is at 4 degrees. This lateral shift in the force

curves thus agrees with expectations.

Figure 37. Yaw Moment and Lateral Acceleration for constant

steer, varying CG slip angle.

Figure 37 confirms that the CG slip angles at which

the signs of the tyre slip angles and hence forces in

figure 36 are opposite produce the largest yawing

moments (between -6 and 0 degrees). Lateral

Acceleration increases as the combined magnitudes

of both the front and rear tyre forces increase as well,

which also agree with expectations.

The Bicycle Model’s predictions in terms of vehicle

behaviour as steered angle and CG slip angle are

varied with respect to one another provides a

qualitative and initial understanding of vehicle yaw

dynamics. Incorporating vehicle weight transfer and

extending the model to 4 wheels is the next step.

FOUR WHEEL MODEL

Extending the Bicycle Model to four wheels requires

only a few additional steps and parameters to be

added to the existing algorithm. The input parameters

for the four wheel model are:


Table 3. Input Parameters for 4 Wheel Model

The assumptions that are made with this model are:

1. No camber variation due to KPI and castor

settings when the front wheels are steered.

2. Parallel steering.

3. No aerodynamic effects

4. No tyre aligning moment contribution.

5. The tyres are assumed to be free-rolling (no

longitudinal slip effects).

In order to create a C_N-C_Y diagram for a 4 wheel

model, the only significant difference to the algorithm

flow chart shown in figure 34 is computing the normal

loads on each wheel. The geometric suspended and

non-suspended weight transfers in addition to the

elastic weight transfer at each end of the vehicle are

determined via the following relations:

∆𝐹𝑧,𝑛𝑠𝑚,𝑓 =𝐴𝑦𝑚𝑛𝑠𝑚,𝑓ℎ𝑛𝑠𝑚 𝐶𝐺,𝑓

𝑇𝑓 (18)

∆𝐹𝑧,𝑠𝑚,𝑓𝑟 =𝐴𝑦𝑚𝑠𝑚,𝑓ℎ𝑅𝐶,𝑓

𝑇𝑓 (19)

𝑀𝑥 = 𝐴𝑦𝑚𝑠𝑚(ℎ𝐶𝐺,𝑠𝑚 − ℎ𝑅𝐶,𝐶𝐺) (20)

∆𝐹𝑧,𝑒𝑙𝑎𝑠𝑡𝑖𝑐,𝑓 =𝑀𝑥

𝑇𝑓

𝐾𝑓

𝐾𝑓+𝐾𝑟 (21)

ℎ𝑅𝐶,𝐶𝐺 = ℎ𝑅𝐶,𝑓 +(ℎ𝑅𝐶,𝑟− ℎ𝑅𝐶,𝑓)𝑎

𝑎+𝑏 (22)

Where the ℎ𝑅𝐶 and ℎ𝐶𝐺variables denote the height of

the roll centre and CG, T track width, K denotes roll

stiffness and 𝑀𝑥 roll moment. Subscripts f and r

denote front and rear respectively and sm and nsm

denote suspended and non-suspended mass

respectively.

Figure 38. CN-CY Diagram for a four wheel model.

The addition of weight transfer to the model produces

a 𝐶𝑁 − 𝐶𝑌 diagram that is similar to figure 35,

however, the lines of constant CG slip angle are non-

linear and the constant steer angle lines are also

slightly different. This type of free-rolling diagram is

however limited in its practical application as most lap

time simulations require constant velocity as an input.

Furthermore, there are few examples in open

literature where this type of diagram is used for setting

up a car.

Figure 39. Flow chart for bicycle model C_N-A_Y diagram.

Wheelbase 1.55 m

Front Track Width 1.1 m

Rear Track Width 1.05 m

Front Roll Centre Height 0.025 m

Rear Roll Centre Height 0.045 m

CG height (SM) 0.305 m

CG height (NSM) 0.25 m

Suspended Mass 150 kg

Non-Suspended Mass 12.5 kg (each corner) Driver Mass 80 kg

Weight Distribution 50:50 Front:Rear

Front Roll Stiffness 570 Nm/deg

Rear Roll Stiffness 470 Nm/deg

Velocity (CN-AY) 15 m/s

Radius (CN-CY) 15 m

Camber Angle 0° Front & Rear

Toe Angle 0° Front & Rear

For = 15 𝑚𝑠−1 , choose grid

point 𝛿𝐹 = 1° and 𝛽𝐶𝐺 = 0°

Calculate tyre slip

angles and 𝐹𝑧

Guess yaw rate

Calculate tyre lateral force

given by tyre model.

Calculate 𝑟𝑛𝑒𝑤

Is 𝑟 𝑛𝑒𝑤 − 𝑟 𝑜𝑙𝑑 < 𝜖 ?

No

𝑟 𝑛𝑒𝑤 = 𝑟 𝑜𝑙𝑑 + 0.1

Yes

Grid point converged,

move to next 𝛿𝐹 and 𝛽𝐶𝐺

point


Developing a 𝐶𝑁 − 𝐴𝑌 diagram requires a modified

algorithm flowchart to that of figure 34.

Due to the fact that the yaw rate is changing for every

loop, this requires the wheel slip angles in addition to

the normal loads to be updated on every iteration.

𝛽𝐹𝐿 = arctan (𝑉 sin 𝛽𝐶𝐺+𝑟𝑋𝐹𝐿

𝑉 cos 𝛽𝐶𝐺+𝑟𝑌𝐹𝐿) (23)

𝛽𝐹𝑅 = arctan (𝑉 sin 𝛽𝐶𝐺+𝑟𝑋𝐹𝑅

𝑉 cos 𝛽𝐶𝐺−𝑟𝑌𝐹𝑅) (24)

𝛽𝑅𝐿 = arctan (𝑉 sin 𝛽𝐶𝐺−𝑟𝑋𝑅𝐿

𝑉 cos 𝛽𝐶𝐺+𝑟𝑌𝑅𝐿) (25)

𝛽𝑅𝑅 = arctan (𝑉 sin 𝛽𝐶𝐺−𝑟𝑋𝑅𝑅

𝑉 cos 𝛽𝐶𝐺−𝑟𝑌𝑅𝑅) (26)

Where r is yaw rate in radians per second, V is

velocity in m/s, X denotes CG-to-axle distance in

meters and Y is the half track width in meters.

These then yield the final slip angles:

𝛼𝐹𝑅 = 𝛽𝐹𝑅 − 𝛿𝐹𝑅 (27)

𝛼𝐹𝐿 = 𝛽𝐹𝐿 − 𝛿𝐹𝐿 (28)

𝛼𝑅𝑅 = 𝛽𝑅𝑅 (29)

𝛼𝑅𝐿 = 𝛽𝑅𝐿 (30)

Figure 40. CN-AY Diagram at 15 m/s.

It is evident that relaxing the corner radius constraint

and imposing a velocity for each grid point across the

range of steered and CG slip angles has resulted in

different stability (gradient of constant steer angle

lines) and control (gradient of constant CG slip angle

lines) characteristics. For lap simulations this enables

the possible range of corner radii for a given setup

and speed at any instant along the vehicle’s trajectory

to be determined. Due to the steered angles on the

front wheels producing a tire ‘drag’ force component

when resolved into the body reference frame,

longitudinal acceleration also needs to be accounted

for in the model. Since this is only a ‘free-rolling’ (zero

longitudinal slip) model, all of the tyre’s grip potential

is assumed to be used to generate lateral force. This

is only true in reality during a cornering manoeuvre

when the car is at the apex (lowest radius of the

vehicle’s trajectory) or coasting during part of the

manoeuvre (no braking/throttle input applied). The

slip angle induced drag however, becomes significant

at high steered and CG slip angles as seen in figure

41.

Figure 41. CN-AY diagram showing longitudinal effects on free-rolling solution.

This demonstrates the importance of including

longitudinal dynamics in the model to obtain the

particular solution or limit envelope for a given

longitudinal acceleration constraint.

CONSTRAINED TESTING

Though the tyre model may respond well to the limited

number of inputs, there are many more parameters

that are able to influence tyre behaviour. Tyre surface

temperature is closely linked with load sensitivity [10]

and the effect it has on maximum lateral grip

coefficient is also significant. This is evident when

looking at the magnitude of the lateral grip coefficient

plotted against surface temperature during a

constrained test run in figure 42.

Figure 42. Lateral grip coefficient as a function of tyre surface


temperature. Polynomial and normal distribution function fits included.

𝜇(𝑇) = 𝐶0 + 𝐶1𝑇 + 𝐶2𝑇2 + 𝐶3𝑇3 (31)

𝜇(𝑇) =1

𝐶1√2𝜋𝑒

−(𝑇−𝐶2)2

2𝐶22

(32)

Figure 42 shows that it is possible to obtain

reasonable fits using either (31) or (32), however

tread temperature itself is a function of many

parameters such as road surface, wear, ambient

conditions, tyre compound etc. Temperature effects

can therefore be included in a lap simulation if a

temperature profile gathered from test data is used

(however this information will yield qualitative results

at best as vehicle parameter sweeps will change the

actual temperature profile in reality).

From figure 23, the setup of the test road surface

contributes to the unrealistically high grip coefficients

measured. The belt surface does not have asperities

that are representative of those found on typical

tracks and the nature of the setup means that rubber

is continuously deposited on the tyre path. Whilst this

creates greater adhesion between the tyre and the

belt surface, this condition is not encountered on long

asphalt tracks where dirt deposits and variation in

surface asperities can decrease the grip coefficient

predicted from constrained testing by up to 50%. [10]

The test machine also shows difficulty in maintaining

the required normal load during a sweep due to the

deflection of the tyre at the contact patch increasing

with slip angle. This can also affect the accuracy of

the tyre model generated from this data. Figure 43

illustrates this phenomenon, which is more severe for

higher loads and also softer compounds (see figure

44).

Figure 43. Variation in measured lateral load with normal load.

Figure 44. Variation in measured lateral load with normal load for a

softer compound.

Another discrepancy between constrained testing

data and on-track data is the transient behaviour of

the tyre on-track. The slip rate during constrained

testing is fixed at +/- 5 degrees per second during the

sweeps, however this can be much higher on a

Formula SAE-A car (up to 80 degrees/second). It is

known that rubber modulus (and in the context of a

Formula SAE-A car, properties of the rubber in the

contact patch) will change due to strain hardening

and crystallisation at higher rates of strain (slip) [10].

This in turn will affect how temperature builds up in

the tyre on-track, and is something that is not

accurately modelled in the constrained test.

Though the constrained testing does not accurately

capture the transient behaviour of the tyre, it still

provides excellent qualitative information on the

steady state performance of a tyre. Additionally, it

provides a good basis from which the relative

performance of a tyre under various conditions can

be modelled and quantified.

MODEL VALIDATION

In order to confirm whether the trends predicted by

the steady state simulation are correct as it develops,

a series of manoeuvres were tested using the

Monash Motorsport 2012 Formula SAE-A car as a

testbed. The car was instrumented with a non-contact

optical Correvit LFII sensor to measure CG slip angle

and also to determine the individual wheel slip angles.


Figure 45. Sensor setup on M12 during a shakedown.

Constant radius circles of radii 5,7.88,8, 12 and 20

meters were driven as shown in figures 46-48:

Figure 46: CG Slip Angle vs. Inverse Corner Radius for constant radius manoeuvres.

Figure 47: CG Slip Angle vs. Yaw Rate

Figure 48: CG Slip Angle vs. Lateral Acceleration

Though the manoeuvres take place under constant

lateral acceleration, as shown at the tips of the 𝐶𝑁 −

𝐴𝑌 diagram in figure 19, including longitudinal

dynamics will enable direct comparison between the

model and the collected data. From the figures above

it can be seen how the vehicle balance (shifting CG

slip angle) close to the limit of the 𝐶𝑁 − 𝐴𝑌 diagram

on-track is influenced by lateral acceleration, corner

radius and yaw rate and hence justifies the approach

of extending the vehicle model to include yaw

dynamics.

FUTURE WORK

At this stage, the model agrees with expectations,

however, in order to progress it to the stage where

parameter studies can be done in order to refine car

setup, a combined longitudinal and lateral tyre needs

to be developed.

Furthermore, the tyre data fitting procedure can be

applied to data measured on-track through strain

gauging the wishbones on one or more corners of the

vehicle. Since there are some compounds that have

not yet been tested by the TTC, in addition to

validating tyre constructions that have already been

tested, new tyre data can be obtained.

There is also scope to extend the work presented to

form the basis of a Lap simulation which accounts for

yaw dynamics which cannot be investigated in

simpler particle model based simulations.

CONCLUSION

The development and validation of a tyre and vehicle

simulation for Formula SAE-A was described. Using

data provided by constrained testing, a tyre model

was created using Non-dimensional Tyre Theory and

this was shown for a pure lateral force model to be in

good agreement with the data obtained from

constrained testing. The tyre model was then


extended systematically to a two wheeled vehicle

model and finally a four wheeled model to create a

vehicle performance envelope accounting for yaw

dynamics in addition to longitudinal and lateral

acceleration. Finally, data from constant radius

manoeuvres collected using the Monash 2012

Formula SAE-A car was processed and will serve as

a baseline against which the model will be checked

as it develops.

ACKNOWLEDGEMENTS

The author would like to thank:

Dr. Scott Wordley, for his support and guidance

throughout the project.

Martin Bett, Luke Pherrson, Damien McArthur,

Andrew Salvo, Alex Biviano, Tom Knast, Ashan

Perera,Yuval Shavit, Daniel Teo, Katherine

Venables, Terence Avadiar, Peter Roufaeil, Areeb

Hassan and Ryan Paine for their support and

guidance during testing and the tireless commitment

of the students involved in the Monash Motorsport

Formula SAE-A Team.

Calspan TIRF and TTC, as without their volunteers

running the constrained testing, there would be little

or no tyre data available in Formula SAE and hence

writing this paper.

Joe Cavallaro, Peter Herath and Robert Bosch

Australia for their support of the team with testing at

their facilities and use of their measurement systems.

REFERENCES

1. Calspan Corporation. Capabilities Report.

Technical, Buffalo, NY: Calspan Corporation,

2012.

2. Kasprzak, Edward. Inflation Pressure Effects in

the Nondimensional Tyre Model. Technical

Paper, Warrendal,PA: Society of Automotive

Engineers, 2006.

3. Kasprzak, Edward M. “Test photos from

Calspan.” Calspan, 2014.

4. Milliken, W.F., and D.L. Milliken. Race Car

Vehicle Dynamics. Warrendale: Society of

Automotive Engineers, 1995.

5. Patton, Chris. Development of Vehicle Dynamics

Tools for Motorsports. Corvalis: Oregon State

University, 2013.

6. Ringwood, Pete. Determining a Tire's Influence

on Vehicle Handling and Limit Performance.

Sydney: University of Technology Sydney, 2012.

7. Ryu, Jihan. Vehicle State Estimation with GPS. -

December 2004.

ddl.stanford.edu/GPSestimation (accessed

February 17, 2014).

8. SAE International. “SAE Collegiate Design

Series.” Society of Automotive Engineers. 2013.

students.sae.org/cds/formulaseries/rules/2014_f

sae_rules.pdf (accessed 2 24, 2014).

9. Society of Automotive Engineers. Vehicle

Dynamics Terminology - SAE standard J670e.

Warrendal,PA: Society of Automotive Engineers,

1976.

10. Trevorrow, Nick. A Theoretical Model and

Experimental Investigation of Racing Slick Tyre

Performance and Behaviour. Melbourne: Monash

University, 2006.

CONTACT

Marc Russouw Monash University

Phone: +61 0425 719 680


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