section 2 - uab.edu · 55μs -75μs. four images of specimen cfmix05 are shown in figure 9. the...

UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 Page 37

SECTION 2

MATERIALS, DESIGN

& MANUFACTURING

UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 9

Proceedings of the Fifteenth Annual Early Career Technical Conference The University of Alabama, Birmingham ECTC 2015

November 7, 2015 - Birmingham, Alabama USA

PULTRUDED GLASS-GRAPHITE/EPOXY HYBRID COMPOSITES SUBJECTED TO TRANSVERSE HIGH STRAIN-RATE COMPRESSION LOADING

Mohammad Afrough, Tejas S. Pandya

The University of Mississippi Oxford, Mississippi, USA

ABSTRACT

In a previous investigation, the energy absorption and

dynamic response of different combinations of fiber-reinforced

pultruded hybrid composites made of unidirectional glass and

graphite fiber/epoxy, was investigated. It was found that placing

glass fibers in the inner core of composites results in a higher

ultimate compressive strength and specific energy absorption.

In the present work, the dynamic response of pultruded glass-

graphite/epoxy hybrids has been investigated, with specimens

of rectangular cross-section being subjected to compression

loading in a direction transverse to the fiber orientation. Crack

initiation and propagation was monitored with a high-speed

video camera, and the effects of hybridization were analyzed. It

was found that the location of glass or graphite fibers inside the

pultruded composites has no significant effect on the ultimate

compressive strength under transverse compression loading.

The energy absorption in all the hybrid specimens was almost

identical. The graphite/epoxy composite showed higher specific

energy absorption due to its lower density, and the glass/epoxy

composite had the lowest specific energy absorption.

INTRODUCTION Composites are utilized because they have desired

properties that cannot be attained by other types of constituent

materials. Fibrous composites, including reinforcing fibers

embedded in a matrix material, are commonly used in different

applications. Fiber-reinforced composite materials present

different features in terms of stiffness, specific strength,

deformation etc. Their usage encompasses a wide range of

automotive, aerospace and marine applications.

The two most common reinforcing fibers are glass and

graphite. Glass fibers have high tensile strength and low tensile

modulus. On the other hand, graphite fibers possess very high

tensile modulus, low weight and low impact resistance. A

number of investigations have been carried out on pultruded

composites [1-13]. Their results illustrate that hybridization of

both glass and graphite, with different percentages within the

same epoxy matrix, has a significant effect on the mechanical

properties.

In many applications, composite materials are subjected to

dynamic loading, which is produced by vibration or wave

propagation [14]. Therefore, it is necessary to investigate strain

rate sensitivity, failure modes and other behaviors of composite

material under dynamic loading. Several studies have been

performed on strain rate sensitivity [15-17]. Ochola et al. [18]

tested a glass fiber reinforced polymer at different strain rates.

The results show that the value of mean compressive ultimate

stress for this composite increases by 20.9% as the strain rate is

increased from 10-3

to 450 s-1

. Various set-ups like drop-weight

and split Hopkinson pressure bar (SHPB) have been used [19,

20].

Furthermore, the direction of loading and the fiber

orientation play important roles in the dynamic behavior of

composite materials. Yokoyama et al. [21] investigated the

behavior of cubic specimens of carbon/epoxy laminates in all

three principal material directions under high strain rate

compression testing and discussed the composites’ failure

mechanism. They described that by increasing strain-rate, while

the compression is along the fiber direction, ultimate strength

increases, and energy absorbed up to failure strain decreases. In

previous investigations by the authors [22], the energy

absorption and dynamic response of different combinations of

glass and graphite pultruded composites under longitudinal

compression loading was studied. It was found that locating

glass fibers in the inner core raises the ultimate compressive

strength and results in better dynamic behavior of the

composite. Since there are many applications where loads are

applied in a transverse direction, evolution and optimization of

pultruded composite materials from this point of view is a need.

The purpose of this study is to analyze the dynamic

behavior and energy absorption of pultruded glass-

graphite/epoxy hybrid composites by applying transverse

compression load at high strain-rate. A modified SHPB test

system has been used for producing dynamic load.

Additionally, a high-speed camera was deployed to record the

events and monitor crack initiation, propagation and failure of

samples during the tests.

SPECIMEN DETAILS Samples from a long beam with rectangular cross-section

composites were manufactured by a pultrusion process (Pulstar

804 machine). Glass and graphite were the reinforced fibers.

Volume fraction of epoxy was constant at 40%. The graphite

fibers were AS4W-12K (Hercules), the glass fibers were E-

Glass (PPG 2001, #12), and the epoxy was EPON 862/W/537

(Shell Chemical Company, USA). All the samples were cut

precisely in 6.6 mm x 6.6 mm sections from a long rectangular

beam of 3.3 mm thickness (Figure 1).

UAB School of Engineering – Mechanical Engineering - ECTC 2015 Proceedings – Vol. 14 0

Figure 1. Dimensions of tested samples

Table 1. Fiber combinations with layup sequence for hybrid composites [13]

The six fiber combinations with layup sequence for hybrid

composites of samples are shown in Table 1. Reference [23]

describes more details of the manufacturing process for these

pultruded samples. The measured bulk densities of the

specimens are shown in Table 2.

Table 2. Measured average bulk densities of the pultruded hybrid specimens

CFMIX01 CFMIX02 CFMIX03

Density

(kg/m3)

1569 1924 1807

CFMIX04 CFMIX05 CFMIX06

1809 1749 1784

EXPERIMENTAL TECHNIQUE High strain-rate testing of the samples was carried out

using a modified SHPB located at the Blast and Impact

Dynamics Laboratory, University of Mississippi. A schematic

of the SHPB set-up is shown in Figure 2. It consists of a striker

bar, two strain gages, an incident/input bar and a

transmitter/output bar. The bars are made of maraging steel.

Specimens are sandwiched between the incident and

transmission bars. A copper pulse shaper was applied to attain a

triangular incident pulse.

Figure 2. Schematic of a Split Hopkinson Pressure bar [20]

Petroleum jelly was used to place specimens between the

incident and transmitter bars, and also to avoid friction and

shear effects on the samples during testing.

A high- speed video camera (HyperVision HPV-2,

Shimadzu Corporation, Japan) was employed to record the

events and monitor the crack initiation, propagation and failure

of samples during the SHPB compression loading tests. One

hundred and two frames of each event were recorded at a frame

rate of 500,000 fps. Two 1000W strobes were used to provide

the required lighting.

RESULTS AND DISCUSSION Six combinations of pultruded glass-graphite/epoxy hybrid

samples were tested. Figure 3 shows the generic stress wave

pulses including incident, reflected and transmitted pulses. For

validating the dynamic equilibrium condition, a specimen must

be in force equilibrium [22]. The stresses in both interfaces of

each specimen were calculated to ensure that equilibrium was

attained (Figure 4).

Five samples were tested for each combination at average

strain rate of 800 s-1

. Figure 5 shows typical strain-stress

behavior for all the combinations. All curves are plotted until

the shattering point of each specimen, with high-speed

photography utilized for capturing this moment. They are

essentially coincident with each other until ultimate

compressive strength, with all of them possessing equal initial

stiffness. This synchronization appears to be from the matrix

domination response of these pultruded composites, with load

applied in the transverse direction.

Figure 6 illustrates the average ultimate compressive

strength for six specimens of each combination. CFMIX01 with

60% graphite possess the highest ultimate compressive strength

and CFMIX02 with 60% glass has the lowest one. The

compressive strength of CFMIX05, the hybrid with 40%

graphite in the outer layer and 20% glass in the core, is slightly

nearer to that of graphite/epoxy. The small difference between

the highest and lowest ultimate compressive strength

demonstrates that using different portions of glass and graphite

fibers does not dramatically change the compressive strength

under transverse compression dynamic loading. This small

difference results from the stronger bond between graphite fiber

and epoxy matrix compared to the bond between glass fiber and

epoxy matrix, perhaps due to the sizing of graphite fibers. As

can be seen in Figure 7, graphite fibers (Figure 7, a), have

mostly disintegrated after the compression event while glass

fibers (Figure 7, b), remained almost intact.

Fiber Orientation

Thickness = 3.3 mm

mm

6.6 mm

6.6 mm


Figure 3 Typical Incident, reflected and transmitted pulses from SHPB test of a pultruded hybrid composite sample

Figure 4. Validation of stress equilibrium for tested pultruded hybrid composite sample

Figure 5. Typical stress-strain curve for all hybrids tested under transverse SHPB transverse compression loading

Figure 6. Compressive ultimate strength (MPa) for all hybrids tested by SHPB

Figure 7. Micrograph view of specimens after compression event (500x), (a) graphite fibers, and (b)

glass fibers

A comparison between this study and the previous study

[22] shows that in transverse compression dynamic loading, the

ultimate compressive strength of graphite-glass/epoxy

pultruded hybrids is about one-third of the longitudinal

compression dynamic loading.

As can be seen in Figure 5, all specimens absorbed almost

equivalent amounts of energy (integration of the area under

stress-strain curve) during the dynamic compression tests, but

the specimens with greater volume fraction of graphite fibers

absorbed marginally more energy. However, the specific energy

absorption (energy absorbed per unit mass) for each specimen

is distinct due to their different densities (Figure 8). As shown

in Table 2, specimens with more graphite fibers have lower

density, which resulted in higher specific energy absorption.

Specimens with 60% graphite (CFMIX01) and 40% graphite

(CFMIX05) show the highest specific energy absorption, while

60% glass (CFMIX02) exhibits the lowest specific energy

absorption.

(a)

(b)


Figure 8. Specific energy absorption for all specimens tested by SHPB

As mentioned, a high-speed video camera was deployed to

monitor the crack initiation, propagation and failure of samples

during the SHPB compression loading tests. For these samples,

the event (from time zero to crack initiation of specimen) takes

55μs -75μs. Four images of specimen CFMIX05 are shown in

Figure 9. The stress versus time history of this specimen is

depicted in Figure 10. The maximum load (ultimate strength)

occurs at 51 μs and the crack initiates at 66 μs. The image at 79

μs shows that the fiber glass in the specimen core detaches

from epoxy matrix. The results indicate that the entire sample

has not been completely damaged at the peak stress. It should

be noted that the compression load is applied transverse to the

fiber direction, i.e., load is perpendicular to the epoxy and fiber

orientation for each of the hybrid composites.

Figure 9. Four images of specimen CFMIX05 under compression at different times

Figure 10. Stress versus time history of CFMIX05

CONCLUSION In this investigation, six combinations of pultruded glass-

graphite/epoxy hybrids have been experimentally studied under

transverse high strain-rate compression loading, using a

modified SHPB. It was observed that failure of specimens

tested along a transverse direction is dominated by matrix

failure. It was also observed that 60% graphite (CFMIX01) has

the highest ultimate compressive strength. The ultimate

compressive strength was found to be marginally greater with a

higher percentage of graphite. This marginal difference results

from the stronger bond between graphite fiber and epoxy

matrices compared to a bond between glass fiber and epoxy

matrix, perhaps due to the sizing of graphite fibers. The

location of glass or graphite fibers inside the pultruded

composites had no significant effect on the ultimate

compressive strength under transverse compression dynamic

loading. While all specimens absorbed almost an equivalent

amount of energy, the graphite/epoxy demonstrated

significantly higher specific energy absorption compared to the

other combinations. This was due to its lower density, while

glass/epoxy showed the lowest specific energy absorption.

ACKNOWLEDGEMENT The authors wish to acknowledge funding received from

US Army Research Office-DURIP Grant # W911NF-13-1-

0248 for the high-speed digital cameras used in this research.

The authors would also like to thank Dr. Raju Mantena and Mr.

Seyed Soheil Daryadel for their guidance in performing this

research, and Mr. P. Matthew Lowe, for his help with sample

preparation.

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Loading and Buckling Response Characteristics of Pultruded

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Reinforced Plastics and Composites, vol. 22, Issue 7.

[2] Nori C.V., P. Raju Mantena, and McCarty T.A., 1996,

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0 μs

66 μs

51 μs

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[12] Dev Barpanda and P. Raju Mantena, 1996, “Dynamic

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[13] Sujit Kumar and P. Raju Mantena, 1996, “Dynamic and

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174-182.




KSU AUTONOMOUS GO-KART FRAME

Andrew Geiman, Dr. Kevin McFall

Kennesaw State University, Marietta, GA, USA

ABSTRACT An autonomous go-kart frame was required in order to

expand the testing capabilities of line detection and line

following. The paper below covers the design and construction

of this frame. The project life cycle was one semester and the

costs could not exceed $650. Every aspect of the design needed

to be analyzed on paper or using finite element analysis to

ensure the design would effectively meet the design

requirements. The autonomous go-kart frame would need to

hold a microcontroller, batteries, motor drivers, a camera and

sensors to perform its autonomous movement on roads. The

two forward wheels would be driven by individual motors,

while the rear wheels are on casters. This configuration allowed

the vehicle to more and steer along pavement. The final design

was successful because it was able to meet both the schedule

and the cost requirements.

Figure 1: Rendering of the final model assembly.

INTRODUCTION During the previous semester a peer designed a “Low-

cost Platform for Autonomous Ground Vehicle Research” [3].

This design was the initial testing bed for software that would

allow the kart to move autonomously through the streets of

Kennesaw State University. The project lacked funding for

structure, but a new semester leads to new prospects and better

funding. This large opportunity for design freedom and

educational expansion could not be ignored. A vision of a

higher performance, modular and easy to use go-kart frame

drove the objectives. The main goals of this project are to

improve the functionality of the go-kart in carrying capability,

strength, speed, and steering, while keeping cost low.

GATHERING INFORMATION To begin the design process, a viewing of the formula one

team’s racecar at KSU was conducted. The race team was

nationally recognized and a natural place to start looking when

dealing with a moving vehicle. Information obtained about the

steering system and wheel base dimension was vital to moving

the project forward. After the viewing it became clear that it

would be too expensive to build a custom steering system at

this time. The race team used professionally manufactured parts

and welds their frame together. The professionally cut

annuluses are then welded together by the team’s most

experienced welders. After examining the racecar material

geometry, a search for material costs was performed. Using

simple square models on paper three different quotes were put

together. The first quote was an all steel frame with thin walled

tubes that would have to be welded together to make a frame.

The second quote was simple aluminum bar stock that was bent

and riveted and the third was a quote for extruded aluminum

8020. The first round of quotes included CV joint for a shock

absorbing system, pivot joints for the steering system, and

shocks. All three quotes came in above price point, the cheapest

being the aluminum bar stock at $1010. The CV joints were

$110 each and the shocks were $15 each. These items along

with the pivot joints were cut. After running the quotes again

the aluminum bar stock came was only at $481 per appendix,

which was back inside the set price limit. The next step was a

decision matrix, to ensure the project on schedule.

PROBLEM STATEMENT Field testing on a previous generation go-kart’s wooden

frame design revealed the need for a sturdier frame. The

previous frame was difficult to drive due to its low torque

output and the tended to veer in one direction. The direct drive

system did not work when turning and the wooden panel

deflects significantly due to the weight of the equipment. Tiny

caster wheels in the front of the kart provided little turning

ability and would jam often due to deflection. All around the

design was effective for the first field test but further progress

required a stable rugged platform.

CUSTOMER REQUIREMENTS The customer required a frame which could mount 100 lbs

of equipment and still clear speed bumps at low speed. This


would allow the kart to travel along the school road and mount

any configuration of equipment required. The new frame

needed to be light, have seven sensor mount points, and be able

to absorb vibration from the road. The kart would need to be

sufficiently light to be carried by two people along with the

equipment needed to operate it. The seven sensors consisted of

one for the forward camera, an encoder on each of the two

drive wheels, and a collision distance sensor on each of the four

corners of the kart. The frame needed to be modifiable in case

unforeseen challenges arise in the future. Deflection at the

caster wheels needed to be less than two degrees to prevent

rotational jamming and sticking. The kart also had to be able to

turn. Vibration on the equipment needed be kept to a minimum

to ensure equipment life.

DESIGN SPECIFICATIONS The kart would be about four feet by four feet to match the

size of a small car and allow for parking simulations. To

accommodate a lightweight design requirement the frame

needed to weigh less than fifty pounds. The frame also needed

be able to navigate speed bumps; the average speed bump is

five inches high and eighteen inches deep. To reduce deflection,

in the equipment mounting surface and the entire kart to less

than an eighth of an inch, rigid beams with supports were used.

Materials needed to be strong, lightweight with good fatigue

life and corrosion resistance. Many of these choices came down

to three main factors; how affordable is it, can it be

manufactured and will it satisfy the customer’s requirements?

DESIGN MATRIX The design matrix using score multipliers was used to

calculate the best design. The multipliers are based on how

important that parameter is to the project. Then a datum

concept is picked at random and all other concepts are

compared to it. Once a winner is found the datum is switched to

the winner and the matrix is run again. After two matrixes the

Riveted Bar frame concept was chosen.

Table 1: Design Matrix

FRONT WHEELS A minimum of a 10” diameter wheel was required to

traverse speed bumps without the frame bottoming out. If a ten

inch affordable wheel could be found then it would satisfy this

object's selection. Many race car /go-kart manufacturers sell

wheels in sets of four and at very high prices. However, the kart

requires two different types of wheels, since there is no steering

system. Harbor freight sells affordable wheels which allows for

low cost replacement. Since it is a brick and mortar store,

replacement of these parts could be purchased same day if a

problem were to arise.

REAR WHEELS Large ten inch caster wheels are needed for this area of the

kart. The rear wheel assembly can take a maximum load of

three hundred pounds, which far exceed the working load of the

kart. Like the previous parts there needed to be cheap easy

replacement with a high safety factor. Rubber tires are prone to

wearing out and would be a hassle replacing them. With a

vendor already chosen for the front wheels it is simple to

search. Harbor freight also offers a ten inch caster system for

only $15 each with the top mounted double bearing assembly

will allow for easy rotation. Making the choice to purchase

Harbor Freight wheels saved a minimum of $50.

DRIVETRAIN The heritage motors are two 2.5 cm electric motors from

the AndyMark Inc. The 12 volt DC motors can run at 5,310

RPM unloaded. They are low torque high RPM motors. Since

the motors could not turn the kart individually the prototype

required two drivetrains for maneuvering. The data from the

prototype made it clear that a drivetrain was needed. This

combined with the steering system quotes lead to the decision

for two drive train systems. Purchasing the CIMple boxes

raised the overall cost by $100, but still saved money compared

to $350 required for steering equipment. The Andy mark

CIMple box was an obvious choice, it was built for the current

motors, the gear ratio is 16:46, and at $50 apiece they are

affordable. Adding to their allure was the fact that two of the

motors used can be mounted to it giving the kart the ability to

double its power in the future. There were also sensor mount

points drilled out to record the rpm of the wheels, which

satisfied a customer requirement. The drivetrain came with a

precision machined shaft with a keyway already cut out

allowing for a keyed coupling to be used. The mounting plate

had holes drilled ready to mount on purchase to make assembly

even easier. Since the CIMple box provided a solution for all

of the customer requirements two were purchased to be added

to the go-kart.

VIBRATION ABSORBERS COUPLINGS There are many types and styles of shock absorber

available but many of them are expensive and do not allow easy

installation. The chosen design needs to be simple, easy to use,

cheap and effective. These criteria lead to a simple rubber

cylinder with threaded bolts attached to each end for mounting.

The size and effectiveness of the absorber was unknown during

procurement, this lead to two types being purchased, one eight

millimeter large set and one six millimeter small set. The two

sizes would go through two field tests to see if they are

sufficient for use.


MOUNTING PLATFORMS The platform must hold a minimum 100 pounds of

equipment; this includes multiple batteries, motor drives, and a CPU. The preferred way to get a cheap, light weight, strong and durable platform was to get eighth inch aluminum plate and mount the vibration absorber to it. Composites were considered next but the price increases when a large surface area and thickness are required. Carbon fiber plate and G10 composites were two of the best composites available. However carbon fiber was problematic to work with and G10 prices climb quickly when a four foot by one foot sheet was needed. There was also a concern that the stress concentrations in the mounting holes could crack the brittle composites due to the vibration.

SENSOR MOUNTS The material selected to hold the 4 sensors mounted on the

corner of the vehicle was Aluminum 6061-t6. It matched many of the other components and provided a very strong and versatile structure. The mount was a simple square tube that is three feet long and half an inch wide, the walls are eighth inch thick. Since the weight of the sensor and beam weight was less than half a pound combined, the main driving factor for this component was the deflection due to acceleration and deceleration. Five pounds of force were chosen to size the width and wall thickness, this was derived from the mass of the bar and the sensor assembly mass at a conservative acceleration of 7.5 feet per second squared. This was assuming the cart could reach 15 feet per second in 2 seconds. The component becomes a simple beam and the max deflection is 5.96E-5 inches. This does not account for manufacturing tolerances but under its own forces the beam was very stable.

WHEEL ROTATION SENSOR The sensors to monitor the wheel velocity were chosen

from AndyMark website. They mate up to the drivetrain easily and required no tooling holes or modifications. The optical encoder simply mounted directly on to the drive shaft, where the drive train housing had pre drilled holes.

MODELING To begin the process of modeling, the purchased pieces

were drawn in their simplest forms possible, how they shipped. This meant the manufacturing issues were brought to the forefront while still in the design phase and many of the issues could be solved early. This proved to be an advantage and made manufacturing the parts very streamlined later on. An example of this is the main beam which holds the drive train in place. If the part needed a mounting hole, one was added in using Hole Wizard and a drill size was recorded. This insured that all pieces could be made with little use of heavy machinery. The most complicated parts to model where the couplings and the rear wheel bracings. The couplings were hard due to the level of precision required and the rear wheel bracing was hard due to the bend required.

COUPLINGS With the precision ground shaft to work with a coupling

system needed to be manufactured to connect the wheels and drivetrain. This involved designing, analyzing and manufacturing them in house to save cost. Raw material would then need to be chosen and purchased ahead of time so these could be used on the prototype. The first phase was sizing the coupling dimension, things such as thickness, height, diameter and features (figure 2). The wheel bolt pattern was found using caliper and the shaft size and tolerance was given in the drivetrain specification. A key slot was also needed and this was a manufacturing issue at first. Later after talking to a few machinists it was discovered that a post machining process was necessary.

Figure 2: Rendering of coupling 1.0.

After the keyway was performed the coupling needed to be

analyzed to ensure that it was in the ballpark on thicknesses. The bolt preload needed to be considered as well and sizing the new bolts that would hold the wheel and coupling together need to be found too. After running a shear stress calculation on the included bolts, it was discovered they had a factor of safety in the teens. Next the moment and preload tension were calculated along with the combined load of all three. The FEA to support the hand calculations is below in Figures 3 and 4.

The next phase was optimizing the design, finalizing a material and tolerancing the hole for the drive shaft for a loose press fit. For material aluminum 6061-T6 was used for its great properties, the diameter and height of the part was chosen and the raw material was purchased. Coupling 2.0 (Figure 5) came out of the optimization lighter and more efficient.

In Figures 6 and 7 a clear difference can be seen in the displacement of the Rear Wheel bracket designs. For this part the driving analysis factor was the displacement of the end, because if the angle becomes too steep the caster wheels will bind. This was clearly demonstrated again in Figures 10 and 11, where the factor of safety never drops below five.

All components were analyzed using a combination of finite element analysis and hand calculations. The entire frame assembly underwent many simulations to determine if the system would work. (Figures 12, 13, and 14) Ensuring the


system will not fracture when loaded to the specified weight.

The results of the coupling FEA can be seen in Figures 8 and 9.

The coupling is constrained at the bolt holes with a force and

torque applied to the center drive shaft holes.

Figure 3: Factor of Safety fringe plot of Coupling 1.0.

Figure 4: Fatigue Life fringe plot of Coupling 1.0.

Figure 5: Rendering of coupling 2.0.

FEA

Figure 6: Displacement fridge plot of Rear Wheel Bracket

1.0.

Figure 7: Displacement fridge plot of Rear Wheel Bracket

2.0.


Figure 8: Stress fridge plot of Coupling 2.0.

Figure 9: Displacement fridge plot of Coupling 2.0.

Figure 10: Factor of Safety fridge plot of Rear Wheel

Bracket 1.0.

Figure 11: Factor of Safety fridge plot of the Rear Wheel

Bracket 2.0.

Figure 12: Von Mises Stress fridge plot of the Final

Assembly.

Figure 13: Factor of Safety fridge plot of the Final Assembly, with the lowest FOS being 6.495 around the

platform holes.

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MANUFACTURING The first pieces to be manufactured were the two

couplings; this was a request by the customer. Once the

material, tooling and FEA were in and completed

manufacturing could begin. The tolerances and dimensions

were double checked per machining good practices. The CNC

machine schedule was developed before this project but is was

modified to ensure minimal tool damage. However two end

mill bits were broken on the first coupling, the first broke due

to a feed speed error. The aluminum became hot and gummy

due to the temperature, and then bonded to the carbide steel end

mill bit. The second bit was smashed by the machine due to

operator error. After the first bit broke the machine was started

half way through the milling cycle, this was a huge mistake!

The end mill bit was driven deep into the material at the

jogging velocity and the bit was shattered. After these two

mistakes were corrected the milling process went smoothly.

With the coupling done, the tolerances and drive train assembly

were tested before the delivery deadline. The next pieces to be

manufactured were the main 90 degree corner pieces which

allowed for the cart to begin taking shape. The bends were

made on a Chinese pipe bender and then holes were drilled in

the desired pattern with a #30 drill bit. Match holes were then

drilled in the main beams to ensure a clean fitting joint. Once

the pattern was copied and the mating joints were numbered,

Clecos were installed. These are temporary mechanical rivets,

and allow for the joint to be assembled and disassembled with

no damage to the material. The Clecos allowed for a rigid

layout to be done and a few mistakes were found and then

eliminated. Cut lengths were checked and then the two middle

support structures with L-brackets were added to the frame. The

forward camera plate was riveted into place along with the

camera post bracket, followed by the mounting holes for the

drive train. The drive train was not attached yet to allow for

easy maneuvering of the assembly. The rear wheel mounting

brackets were cut and bent followed by the bracings. These

were then mounted to the frame using match holes and then

riveted into place. It was important to allow for all of the

manufacturing imperfection and again, match holes were used

to ensure the rear wheels could be mounted properly. The

wheels and drive train were added next to form an almost

complete cart. The main mounting plates were added using

match and tapped holes, then sensor beam and finally the

camera post was put into place.

PERFORMANCE VALIDATION Three tests were performed on the frame before it was

delivered. The first test was done by analyzing the frame for

excessive deflection when loaded with hand pressure. As

rudimentary as it sounds this was a vital step in checking the

handmade kart. The criteria for passing the test were no visual

damage or errors in the construction. When this test was passed

the kart was loaded to 70% the max recommended load and

pushed at speed. This checked the operations of the vehicle

under load without damaging it in case of issues. The criteria to

pass this test were no excessive or unpredicted displacement

and smooth operation of the vehicle. The third and final test

was to load the vehicle to 140% of the recommended load. The

vehicle was not operated under these conditions but inspected

for any mechanical failures. The FEA analysis showed that this

load would not damage the kart and performing the test ensures

the quality of the manufacturing. The criterion for success was

no failure and no audible straining.

FUTURE RECOMMENDATIONS Many future recommendations are to improve the karts

performance life and increase its weight capacity. The top

recommendation is to add a support beam from the forward

middle rail to the forward beam that has the camera mounted to

it. This would help eliminate vibration in the camera post and

provide a clearer picture. Adding two more motors to the frame

would allow for faster operating speed and more realistic

testing of the visual code. The final recommendation for the

vehicle is to add support material to both rear and front wheel

supports. These four areas were under the most stress. Adding a

welded backbone to all four of the rear brackets would allow

for a 40% increase in operation load. Also adding a bottom

mounting plate supported on both side to the forward motor

mount would allow for the same load increase.

CONCLUSION The fabrication of the frame took over forty man hours,

with another forty-eight hours of CNC time. The frame includes

wheels, tire, motors, structure, drive train and sensors mount

points. The combined weight of these items was less than fifty

pounds allowing for a two man team to easily carry an

unloaded structure. Final costs of the project totaled $612,

which was $130 more than the original estimate but still less

than the price limit. This was a 21% error in estimated cost but,

there was a surplus of material when the project ended. All of

this time and money gave way to a go-kart frame that is light

weight, durable, modular, and stable. The kart can hold one-

hundred pounds of equipment that allows for autonomous

driving testing. It makes it cheaper and easier to test the next

wave of technology that will make the world a safer place.


REFERENCES [1] Budynas, R. G., and Nisbett, J. K., 2011, Shigley's

mechanical engineering design, McGraw-Hill, New York.

[2] Oberg, E., 2012, Machinery's handbook a reference book

for the mechanical engineer, designer, manufacturing engineer,

draftsman, toolmaker, and machinist, Industrial Press, New

York.

[3] Ollukaren, N., and McFall, K, 2014, Low-cost Platform for

Autonomous Ground Vehicle Research, Proceedings of the

14th Early Career Technical Conference, Vol. 13.

[4] Wahab, M. A., 2008, Dynamics and vibration: an

introduction, John Wiley, Chichester, England.

[5] “www.AndyMark.com,” AndyMark Robot Parts Kits

Mecanum Omni Wheels.

APPENDIX A

APPENDIX B




AZI-DRIVE: A NOVEL THRUSTER MAPPER FOR TWIN PROPELLER MARITIME VESSELS

Andrew Gray

University of Florida Gainesville, Florida, U.S.A

Kevin French

University of Florida Gainesville, Florida, U.S.A.

Jacob Panikulam University of Florida

Gainesville, Florida, U.S.A

Zach Goins University of Florida


Dr. Eric Schwartz University of Florida


ABSTRACT

Over-actuated surface vessels, while highly maneuverable,

can be challenging to control. This paper describes the process

of implementing the controls of a novel propulsion system onto

a small, fully autonomous surface vehicle (ASV). Two

independent azimuth steered rimless propellers provide

propulsion for the vessel. Using a nonlinear control allocation

with singularity avoidance method, the approach was tested

with simulations and then implemented on an ASV. Using the

singularity avoidance variable, users could adjust the

responsiveness of the system. Testing confirmed that the

method allowed the ASV to move in all three dimensions

required of a surface vehicle.

INTRODUCTION For most maritime vessels, a propeller and rudder are

adequate to allow a boat to transit from one point to another.

However, for complex maneuvers, such as station holding

performed by dynamic positioning (DP) vessels, more

complicated propulsion systems are required. These systems

are generally over-actuated [1] and require complex control

software to utilize the thrusters.

Students from the Machine Intelligence Lab (MIL) at the

University of Florida have a history of developing autonomous

maritime vessels [2][3][4][5]. PropaGator 2, MIL’s most recent

ASV, shown in Figure 1, was designed to have a top speed of

10 knots while maintaining the maneuverability of a DP vessel

[6]. To accomplish these characteristics, a planing hull was

developed with a low drag coefficient in the forward direction.

For propulsion, two student-designed azimuth thrusters were

installed on the back half of the vessel.

While the hull design and propulsion system on

PropaGator 2 allowed for high speeds, the complexity of the

system created a challenging control problem. In simulation,

the two thrusters were able to control the boat simultaneously

in all three dimensions [7], but implementation proved difficult.

Additionally, the placement of the thrusters at the rear

combined with large drag resistance across the perpendicular

plane made strafing a challenge. The thrusters produce more

thrust in the forward direction than in the reverse. While the

drag of the propellers is very small, the drag force experienced

by the boat is far greater in the reverse than in the forward

direction. Another challenge was that due to the plowing

characteristics of the boat, speed must be considered in its

control.

The propulsion system on PropaGator 2 does not fit the

model of a traditional DP ship. First, PropaGator 2 is only six

feet long. Most DP vessels are meant for the open ocean and

are hundreds of feet long. PropaGator 2 is only able to operate

in small bodies of water with limited waves. Traditional DP

ships will have two to three times more thrusters than

PropaGator 2. Numerous thrusters allow for redundancy and

can make the vessel’s movements more responsive. With only

two azimuth thrusters, the ASV cannot afford to lose a thruster;

in some cases, the ASV may be less responsive than a vessel

with four or five thrusters.

To control the propulsion system on PropaGator 2, a

student-implemented constrained nonlinear control allocation

Figure 1. The autonomous boat PropaGator 2


with singularity avoidance thruster mapper was used. The

thruster mapper allowed the ASV to move in any direction

while maintaining the ability to hold its position and heading.

This ability classified PropaGator 2 as a DP vessel and was

proven by testing the platform in several small lakes.

PLATFORM SUMMARY PropaGator 2 was made to be fast, maneuverable, and

lightweight. A catamaran hull was selected as the hull type

because catamaran hulls typically produce less drag when

compared to a similar sized monohull [8]. Hard chines were

added to the bottom of the vessel to allow for a planing hull,

using previously conducted hydrofoil research [6]. A hard

transom was installed at the back of the boat. The hard transom

caused the water to cleanly separate from the boat in order to

reduce drag. Fiberglass was selected for the hull material

because of its strength and low weight. For propulsion, two rim

driven azimuth thrusters, shown in Figure 2, were installed near

the stern. To reduce drag, both propellers are enclosed in Kort

nozzles and are independently steered with servos inside the

hull.

To allow the boat to “sense” its environment, a suite of

sensors was installed. A Spartan AHRS-8 IMU, paired (through

a Kalman filter) with a GPS module, allowed PropaGator 2 to

determine its position, heading, roll, pitch, and yaw. For object

detection and recognition, an IDS imaging camera and a SICK

LIDAR were installed onto the front of the boat. For

communication between the shore and the boat, a wireless

router was used.

Two types of drive systems were considered: a differential

drive and a constrained azimuth drive system. Originally, a

differential drive system was considered. The thrusters would

have been locked in a forward facing direction during this

configuration. Turning was handled by differential thrust.

While control of the vessel would be simple, station holding

and strafing would not have been possible with this type of

drive system. Because of these limitations, the azimuth system

was chosen, resulting in better maneuverability.

The azimuth thruster drive system took advantage of the

thrusters’ ability to create thrust vectors in different directions.

This allowed the boat to maintain its heading z (rotation) while

traversing in the x (surge) and y (sway) axis as shown in figure

3. However, because the thruster system is over-actuated, the

solutions to produce the desired thrust vectors were not unique.

Additionally, the thrusters could not rotate more than several

full revolutions due to cable binding. To prevent binding, the

thrusters were limited to 180 degrees of rotation at a rotation

speed of 340 degrees per second. With the new constraint on

the thruster rotations, producing thrust vectors became much

more challenging. A method was needed that could determine

appropriate vectors while using a constrained and over-actuated

system. To achieve this goal, a constrained nonlinear controller

was implemented on PropaGator 2.

NONLINEAR CONTROL ALLOCATION The constrained nonlinear control allocator used on

PropaGator 2 was created specifically for surface vessels with

multiple thrusters and was meant to be solved in real time [9].

Additionally, the allocator had a term which penalized solutions

containing singularities. In order to use the allocator, the

propulsion system had to be defined first. Each thruster had to

be characterized by the maximum and minimum amount of

thrust , maximum and minimum angle limitations , and the

maximum and minimum rotation speed . The allocator

initially developed for surface vessels is:

Figure 3. Three axes of motion performed by PropaGator

2

Figure 2. CAD model of the azimuth thruster used on PropaGator 2


(1)

where

, (2)

, (3)

, (4)

, (5)

The first term in equation (1) is the total power

consumption, of the actuators. The next term, ,

adds a penalty based on the force difference . is found by

subtracting the achieved force from the desired force .

The weights in the matrix are chosen so that whenever

possible. In an effort to limit drastic changes, the third term

penalizes changes in thruster azimuth angle. The matrix is

tuned until drastic changes are acceptably limited. The fourth

term penalizes singularities, thereby affecting the

maneuverability of the vessel through the use of the variable.

A high would result in high maneuverability at a cost of more

power. Whereas a low value would result in a less responsive

but more efficient solution.

The above equation is nonconvex, and solving it is

generally intractable for real-time applications. The

optimization is considered nonconvex because of the

potentially nonlinear power cost in (1), the nonlinear constraint

(2), and the singularity avoidance nonconvex term in (1). The

high computation time caused by the nonconvex equation

would make the optimization problem not useful to applications

that required the solutions in rapid succession like PropaGator

2. In order to make the computation tractable, a linear

approximation of the constraints and dynamics yielded a

tractable quadratic program.

The resulting approximation is as follows [9]:

(6)

linear constraints

, (7)

, (8)

, (9)

, (10)

IMPLEMENTATION To make PropaGator 2 controllable on the water, three

levels of software were required: the path planner, the

controller, and Azi-drive. At the highest level, the path planner

was used. The path planner constantly checked the position

and orientation of the boat. Once given a new goal position

from the user, the path planner produced a desired position and

orientation (DPOSE) to guide the boat towards the goal

position. The DPOSE was placed two meters away from the

boat, between the current position and the goal position. As

the boat moved towards the DPOSE, the DPOSE was moved

closer to the goal position. When DPOSE was less than two

meters from the goal position, the distance between the DPOSE

and the boat was decreased at a linear rate determined by the

path planner, until the boat was within a one meter radius of the

goal position.

Moving the boat in the right direction based upon the

position of the DPOSE was performed by the controller. Using

a proportional derivative (PD) controller, the responsiveness of

the boat could be changed. Differences in headings and

position from the DPOSE were translated into a wrench (i.e., a

force and torque vector). The wrench was then passed to the

Azi-drive shown in figure 4.

Azi-drive converted the wrench values into thruster

positions and thrust amounts by solving the associated

quadratic program. In order to produce a thrust vector that best

fits the given wrench, Azi-drive calculates the best value and

translates that value into thruster forces. The best fit solution is

based upon minimizing the error in the cost function. The

singularity avoidance gain, , governs the cost for producing a

solution in a singularity state, i.e., one where enormous

(potentially infinite) thrust is required to produce particular

wrenches.

Avoiding singularities was important for PropaGator 2

because singularities caused the boat to become unstable. A

Figure 4. Flow chart of maneuverability, starting at the

path planner and ending at thruster commands


singularity with the thrusters was encountered when the

thrusters were turned toward each other. The thrusters were

still generating individual forces, but the boat did not move

because of the orientations. Gradually, the controller increased

the amount of thrust generated by each thruster in an attempt to

move the boat. The increase in thrust did not move the boat,

and the cycle repeated, forcing the user to step in and stop the

program (i.e., causing the autonomy to fail).

The thruster mapper solution rate was too slow when Azi-

drive was first programmed. Python was used to initially solve

the optimization problem. Python is a convenient system to

quickly develop programs. However, because Python is a

runtime language, programs written in the language run

significantly slower than compiled languages. Using Python,

the computer on PropaGator 2 was able to solve the

optimization problem at a rate of 10 Hz. The slow update rate

caused oscillations in orientation of the boat that could not be

avoided. A faster solution for the optimization problem was

necessary.

The code-generation tool, CVXGEN, provided a way to

rapidly solve the nonlinear control allocation equation. To use

CVXGEN, a convex optimization problem is defined in a high

level form. The high-level definition is passed to the CVXGEN

tool, which in turn produces a quadratic program solver in C

[10]. CVXGEN quickly solves convex optimization problems

by unrolling matrix multiplications, directly accounting for

sparsity, and taking advantage of problem structure at

generation time. The CVXGEN-generated solver found

solutions up to 25x faster than the previous Python

implementation, allowing the solver to avoid numerical issues

as a consequence of linearization. Once applied to the boat,

the responsiveness increased tenfold.

Prior to testing PropaGator 2 in the water, changes applied

were tested in a simulator (see Figure 5). While the simulator

had basic physics capabilities built in, it did not give a perfect

representation of how the boat would perform. However, the

simulator allowed for immediate feedback to changes made in

software. The simulator also provided a chance to practice

tuning the PD controller gains prior to in-water testing.

After testing the boat in simulation, PropaGator 2 was

transported to a local pool for testing. The pool provided a

controlled environment where gain changes to the PD

controller were more apparent due to the placidity of the water.

Another advantage of using the pool was that students could be

in the water with the boat to physically disturb the vessel in

order to observe its reactions to changes in heading and

position.

Because of the thruster configuration, tuning one of the

axes affected the other axes. This required all three axes to be

tuned simultaneously. The processing of tuning all three axis

continued until the boat was acceptably able to maintain its

position and heading.

Once the team was satisfied with the performance of

PropaGator 2 at the pool, the vessel was placed in a small lake.

The lake provided a large enough area to allow the boat to

travel long distances across the water. During the longer

movements, the team was able to observe how PropaGator 2

tracked across a straight line. If the boat tracked poorly, the

gains in the PD controller were slightly adjusted accordingly

until the performance of the boat was satisfactory.

RESULTS In order to prove that Azi-drive was working properly, the

actual wrenches were compared to the desired wrenches. To

find the actual wrench values produced by Azi-drive, the

following equations were used:

(11)

, (12)

, (13)

, (14)

(15)

(16)

Equation (11) is the actual torque, , experienced by the

boat. This is found by taking the sum of the torque created by

both thrusters. The torque created by either the port or

starboard thruster was found by multiplying the force in the x-

direction, , with the thruster’s y-distance from the center of

rotation, . Torque from the y-direction force, is found by

multiplying the force by the x-distance, . Summing the two

products produced the thrusters torque as shown in (12). In

equations (13) and (14), the sum of the forces along the x axis

and y axis are shown. Finally, in order to find the force in each

direction, equations (15) and (16) were.

Data from testing was recorded for post analysis of the

Azi-drive. The topics recorded were: desired wrench, thruster

force, and thruster direction. Since the rates of recording for

each movement were different and inconsistent, a script was

created. The script was created to find the actual wrench in real

time by subscribing to the topics that produced the thruster

Figure 5. Simulator used to test software prior to boat testing


force and thruster angle. In the following paragraphs, the

results of the script are discussed.

To show that Azi-drive was working, the actual forces and

torque were compared to the desired forces and torque. To

make the graphs easier to read, the actual forces and torque

were averaged with the previous five samples, shown in

Figures 6, 7, and 8, where the y-axis is the force or torque and

the x-axis is time:

As the graphs above show, Azi-drive was able to produce

forces and torques that were close to the desired results given

by the controller. The actual X-force varied slightly from the

desired X-force. This variation can be attributed to the

optimized solution, compensating with the X-force in order to

maintain a closer relation with the Y-force and torque in Figures

6 and 7. In Figure 7, the Y-forces created can be clearly seen to

match the desired forces. A small amount of latency can be

seen as the thruster mapper responds to wrench changes from

the controller. Finally, the actual torque is shown to be very

similar to the desired torque. Once again, latency is seen as the

Azi-drive attempts to match the desired torque request from the

controller. The above graphs prove that Azi-drive works well

as a thruster mapper and has potential for use on other

platforms.

CONCLUSION This paper covered the research, implementation, and

testing of a novel thruster mapper used to control the ASV,

PropaGator 2. Two azimuth thrusters were selected as the

propulsion system because of the maneuverability gained when

compared to a traditional differential drive system. A

constrained non-linear control allocation optimizer was

implemented in order to control the thrusters while constraining

them to a limited amount of rotation. Once Azi-drive was

tested and applied to the boat, PropaGator 2 was able to move

in all three dimensions (surge, sway, and yaw) when traversing

from point to point.

Having only two thrusters in the back of the boat made

controlling of PropaGator 2 difficult. Adding one or more

thrusters to the bow would simplify the control problem. Even

if the thrusters were fixed and could only produce a force along

one axis, this force would assist with swaying the boat, a

movement that proved difficult in the current configuration.

Having a dynamic two stage control system would help to

overcome the issue with the boat handling differently at higher

speeds due to planing. At higher speeds, the boat generally

Figure 8. The solid line is the desired torque and the dashed line is the averaged actual torque

Figure 7. The solid line is the desired Y force and the dashed line is the averaged actual Y force

Figure 6. The solid line is the desired X force and the dashed line is the averaged actual X force


does not need to strafe along the y-axis and could be

maneuvered using a differential drive behavior. A control

method that allowed movement along the y-axis would be

activated when operating at lower speeds. Finally, improving

the path planner by allowing it to factor in the velocity of the

boat would make transitioning between points smoother.

Currently, the path planner does not account for the momentum

of the boat. As the boat approaches the waypoint, it tends to

overshoot the point due to the boat inertia.

ACKNOWLEDGEMENTS The authors would like to thank the students and faculty

members who helped with the operation of the boat, the

University of Florida College of Electrical and Computer

Engineering and the College of Mechanical and Aerospace

Engineering departments. An extended thank you to Dr.

Schwartz and Dr. Arroyo and to the Machine Intelligence Lab.

Thank you Dr. Crane for transportation and the use of the lab

equipment. A special thank you to Jacob Mattingley for

generously providing an academic license for CVXGEN.

REFERENCES [1] Berge, S. P., & Fossen, T. I. (1997). Robust control

allocation of overactuated ships; experiments with a model

ship. In Preprints of IFAC conference on maneuvering and

control of marine craft, Brijuni, Croatia.

[2] Gray, A., Shahrestani, N., Frank, D., and Schwartz, E.,

2013, “PropaGator 2013: UF Autonomous Surface Vehicle,”

AUVSI Foundation’s 6th Annual RoboBoat Competition,

Virginia Beach, VA.

[3] Walters, P., Sauder, N., Nezvadovitz, J., Voight, F., Gray, A.,

and Schwartz, E., 2014, “SubjuGator 2014: Design and

Implementation of a Modular, Fault tolerant AUV,” AUVSI

Foundation’s 17th Annual RoboSub Competition, San Diego,

CA.

[4]: Frank, D., Gray, A., and Schwartz, E., 2014, “PropaGator

2014: UF Autonomous Surface Vehicle,” AUVSI Foundation’s

7th Annual RoboBoat Competition, Virginia Beach, VA.

[5] Nezvadovitz, J., Griessler, M., Voight, F., Walters, P., and

Schwartz, E., 2015, “SubjuGator 2015: Design and

Implementation of a Modular, High-Performance AUV,”

AUVSI Foundation’s 18th Annual RoboSub Competition, San

Diego, CA.

[6] Frank, D., Gray, A., and Schwartz, E., 2014, “PropaGator 2:

A Planing Autonomous Surface Vehicle With Azimuth Rim-

Driven Thrusters,” Proc. 14th Annual Early Career Technical

Conference, Birmingham, Alabama, pp. 185-190.

[7] Patel, D., Frank, D., & Crane, C. (2014, October).

Controlling an over-actuated vehicle with application to an

autonomous surface vehicle utilizing azimuth thrusters. In

Control, Automation and Systems (ICCAS), 2014 14th

International Conference on (pp. 745-750). IEEE.

[8] Lamb, T., 2004, Ship Design and Construction Vol. II,

Society of Naval Architects and Marine Engineers, Chap. 45.

[9] Johansen, T., Fossen, T., & Berge, S. P. (2004). Constrained

nonlinear control allocation with singularity avoidance using

sequential quadratic programming.Control Systems Technology,

IEEE Transactions on, 12(1), pp. 211-216.

[10] Mattingley, J., & Boyd, S. (2012). CVXGEN: a code

generator for embedded convex optimization. Optimization and

Engineering, 13(1), pp. 1-27.




ON-MACHINE MEASUREMENT OF CUTTING TOOL DAMAGE BASED ON OPTICAL KNIFE-EDGE DIFFRACTION

ChaBum Lee, Kwun-Lon Ting

Tennessee Technological University Cookeville, TN, USA

Joshua A. Tarbutton

University of South Carolina Columbia, SC, USA

Sun-Kyu Lee Gwangju Institute of Sci. & Tech.

Gwangju, South Korea

Hwan-Sik Yoon University of Alabama Tuscaloosa, AL, USA

Jonathan D. Ellis University of Rochester

Rochester, NY, USA

ABSTRACT

This research has been proposed to National Science

Foundation CMMI (The Civil, Mechanical and Manufacturing

Innovation) division for 2015. The objective of this research is

to create a new methodology for damage detection of cutting

tools used in machine tools based on the knife-edge diffraction

principle. A detection technique for cutting tool failures is

desperately needed for condition-based maintenance in

machining processes. Cutting tool wear, chipping, and/or

breakage can affect the dimensional accuracy and surface

roughness of products. Firstly, we will characterize the

geometry and edge morphology of various cutting tools for

turning by using high resolution microscopes, and we will

relate those properties with knife-edge diffraction

characteristics. Secondly, we will create a free-space knife-edge

interferometer and establish image correlation methods to

quantify the cutting tool wear, roughness, chipping, and even

breakage by relating edge diffraction fringe curves to cutting

tool conditions. Lastly, we will create an optical fiber-based on-

machine measurement system to enable effective measurement

of the cutting tool conditions and to integrate measurement and

instrument techniques with machine tools. For cutting tool

damage applications, this research will answer fundamental

questions regarding the performance boundaries of knife-edge

interferometry and on-machine measurement technologies, and

produce new knowledge on the measurement, instrumentation

and sensors.

INTRODUCTION Damage of cutting tools is influenced by the stress state

and temperature at the tool surface. This in turn depends on the

cutting mode, such as turning, milling, or drilling, the cutting

conditions and the presence or absence of cutting fluid and type

[1-4]. In machining, the tool damage mode and the rate of

damage are very sensitive to changes in the cutting operation

and the cutting conditions. Thus it is necessary not only to find

the most suitable cutting tool and work material combination

for a given machining operation, but also to reliably predict the

cutting tool life.

Figure 1. A damage classification of the cutting tools according to damage size [4].

Figure 2. Cutting tool edge morphology: new (left) and damaged (right) [5].

Mechanical damage is classified as wear or fracture

depending on its scale. Figure 1 illustrates the different modes,

from a scale of less than 0.1 pm to around 100 pm (much

greater than 100 pm becomes failure). Abrasive wear is

typically caused by sliding hard particles against the cutting

tools. The hard particles come from either the work material’s

microstructure, or they are broken away from the cutting edge.

Abrasive wear reduces as the tool hardness relative to particles

increases, and it generally depends on distance cut. Attrition

Rak

e

Surface

Rak

eS

urface

FlankS

urface

Flank

Surface

0.3 μm 0.3μm

Roundness withinRoundness within 50 nm

Cutting edge Micro Chipping


wear occurs on a scale larger than abrasion. Particles or grains

of the tool material are mechanically weakened by micro-

fracture as a result of sliding interaction with the work, before

being removed by wear. Next comes chipping (sometimes

called micro-chipping at its small-scale limit) as seen in Fig. 2.

This is caused by mechanical shock loading on a scale that

leads to large fluctuations in cutting force, as opposed to the

inherent local stress fluctuations that cause attrition. Finally,

fracture is larger than chipping, and is classified into three

types: early stage, unpredictable and final stage. The early stage

occurs immediately after beginning a cut, if the tool shape or

cutting condition is improper; or if there is some kind of defect

in the cutting tool or in its edge preparation. Unpredictable

fracture can occur at any time if the stress on the cutting edge

changes suddenly, for example by chattering or by an

irregularity in the workpiece hardness. Final stage fracture can

be observed frequently at the end of a tool’s life in milling, and

fatigue due to mechanical or thermal stresses on the cutting

edge is the main cause of this damage [1,2].

With the progress of micro/nanoscale wear, the surface

roughness and dimensional accuracy of the machined part

deteriorates, which consequently leads to changing the worn

cutting tool. In practice, the most important consideration in

selecting cutting tools is the tool life, which defines the time

corresponding to the prevailing tool-life criterion. At the end of

the cutting tool’s life, it must be replaced or reground to

maintain workpiece accuracy, surface roughness or integrity.

However, cutting tool life exhibits significant scatter and is

problematic in nature. The cutting tool conditions are visually

inspected by machine operators in the majority of cases. In

addition, the scanning electron microscope (SEM), atomic force

microscope (AFM), touch-triggered stylus probe (TSP), stereo

microscopes with digital camera attachment, computer vision

systems, and coordinate measuring machine (CMM) can be

used to measure cutting tool wear parameters [6-9]. For

nanoscale analysis [10-13], more advanced measuring

techniques using white light interferometry or confocal

microscopy can be applied. However, there is a lot of

discomfort with characterizing and quantifying cutting tool

conditions with these methods because machine operators have

to separate the cutting tools from the tool fixtures for

inspection.

The integration of metrology into the manufacturing

process is becoming increasingly important for production

technology. Measurement directly done on machine tools is

especially improving manufacturing strategies. So far, optical

or tactile measuring systems are the state-of-the-art for

dimensional measurements in process metrology [12-16].

Many studies related to on-machine measurement (OMM)

techniques have been focused on the machined surface

characterization. However, the machined surface topography

may be influenced by not only machining conditions, but also

cutting tool conditions such as wear. Unfortunately, the OMM

for cutting tools has not been reported, and operators inspect

the cutting tools visually to ensure that they are still within

specific geometry tolerances. However, micro-nanoscale

cutting tool damage cannot be clearly observed by visual

inspection. Alternatively, operators remove the cutting tool

from the fixture and inspect it for damage by using CMM or

high resolution microscopes such as SEM. Unfortunately, these

methods are not sufficient to provide the cutting tool

information such as morphology, wear, and chipping

conditions, in a quantitative way. In addition, operators suffer

from relocation of the cutting tools, and this error will also

badly affect the machining results. Thus the measurement

process must be integrated into the manufacturing process on

machine tools for production technology. This paper describes a

new methodology based on a knife-edge diffraction

interferometry (KEI) technique for OMM that can be used for

nonprobe-compatible CNC turning centers.

KNIFE-EDGE SENSING PRINCIPLE

Figure 3. Knife-edge diffraction by randomly rough edge surface morphology.

The knife-edge diffraction method was recently introduced,

and a deterministic model to characterize edge diffraction

phenomenon according to edge roughness was created [17,18].

Assuming that a Gaussian beam with the beam diameter α is

incident on the knife-edge, the total field to the front of the

knife-edge is simply the incident field, and the beam propagates

along the z axis as illustrated in Fig. 3. The incident field sE

can be defined as,

.),(),,(2

22

0

ss yx

Ajk

sssssss eeyxEzyxE

(1)

where, Es is the amplitude of the incident field, k0 is a wave

vector in air, α is the beam width, and A and B are the distances

between the optics components. The first and second terms are

an expression of the incident wave with Gaussian intensity

distribution, and the last term is for an aperture of the field. The

incident field sE

creates constructive and destructive

interference due to phase function with respect to the knife-

edge displacement. The superimposed wave can be derived by

using Fourier transform (FT) and the total field dE

(sum of

transmitted and diffracted field) at the detector, can be obtained

by applying an inverse FT of the superimposed wave [19-21].

The total field to be measured along the z axis can be defined

from the inverse FT relation of the incident field as,


.

)2(

1),,(

0

0

2

dydxdkydkxe

eEzyxE

d

s

rkj

spacek

rkj

saperture

dddd

(2)

where jir

is a vector from i to j coordinate system.

Figure 4. 2 axis flexure mechanism with knife edge sensor.

Figure 5. Calibration results of KES

Optical metrology systems can be extremely precise but

are fundamentally limited by the surface quality of optical

components. As an approximated model to a rough knife-edge

surface is presented, the phase difference between the

transmitted and diffracted fields is related to the roughness,

assuming that the roughness is randomly distributed in nature.

The roughness on the knife edge boundary is assumed to

comprise a nonzero mean with Gaussian probability density

function (pfd) [20]. Based on this assumption, knife edge

surface roughness is modeled in a pdf form as,

2

2

00

2

2

2

1)(,)(

.)2(

1),,(

h

spacek

rkjrkj

saperture

ddd

R

d

ehpdfkxdydxdhdkydhpdf

eeEzyxE ds

(3)

where R represents a rough surface and h is the random height

along the y0 axis and σ is roughness, standard deviation of h as

illustrated in Fig. 3. Here, only the roughness along the x0 axis

was considered, independent of the y0 axis.

*

0)( R

d

R

dd EEyI

(4)

where, the total power Id induced by the knife-edge at detector

will be calculated by multiplying the total field and the

conjugated total field at each detector plane. The asterisk (*)

denotes a complex conjugate.

Figure 6. Schematics of fiber-coupled OMM system.

This knife-edge technique has been recently applied to

displacement sensing. The displacement sensor [17,18], or so-

called, knife-edge sensor (KES), was implemented into the XY

nanopositioning system (150×150mm2, 20mm thickness) based

on parallel kinematics. The monolithic configuration was

designed and fabricated to yield a compact layout as seen in

Fig. 4 and to provide more than ±1.0mm range. The two KES

were embedded into the stage and measure XY displacement to

control the positioning of the stage in real time. The laser diode

(LD, λ 650nm, α 5.0mm) light is 50:50 separated at BS and

travels to each KES with surface roughness ~0.1µm. Voice coil

motors were used as actuators. Four photodetectors (PD,

3×3mm2) were installed at the center of the stage to receive the

transmitted and diffracted light behind the knife-edge. Figure 5

showed that the KES sensitivity is 8.99mV/µm and

9.02mV/µm within ±500µm with nonlinearity 0.60% and

0.73% along the X and Y axes, respectively.

The phase-locked loop (PPL) based on metrology concept

has also been introduced using lensed fibers for on-machine

surface topography measurement [12,13]. Methods for

measuring the machined surface topography using fiber-optics

have been widely employed because it is cheap and installation

is easy compared to others [16]. Furthermore, advances in

fiber-optic technology have allowed a single-mode fiber (SMF)

to become useful in evaluating the machined surface in an on-

machine condition.

Many kinds of SMF shapes have been reported, such as the

graded index lensed fiber, wedge shaped fiber, and photonic

crystal lensed fiber [22, 23]. However, they are not appropriate

XY

LD PrismBS

PD

OKE


for measuring the surface topography directly in a micrometer

scale because the beam spot size was relatively larger than that

of the surface topography.

Figure 7. Measurement results of machined Cu surface: (a) Zygo and (b) our OMM

The experimental setup of the proposed PLL-based

interferometer is shown in Fig. 6. The light emitted from the

pigtailed LD (λ 650 nm) is coupled into a 2 × 2 fiber coupler

and split into two beams; one goes to the lensed fiber and the

other is detected at PD2. The light reflected from the sample

surface is recollected by the lensed fiber. The whole system was

composed of all-SMF optic components and an SMF operating

within a wavelength range of 630–680nm was used. The

fluctuation of the light source could be monitored at PD2, and

the power fluctuation of an LD caused by the coupled cavity

may cause measurement errors. Therefore, the feedback loop

for the stabilization of an LD with a proportional-integral (PI)

controller was used. The end of the lensed fiber attached to the

mechanical modulator vibrates and is modulated with the

piezoelectric transducer (PZT). The interference signal is

detected at PD1. After phase demodulation by digital signal

processing in real time, the phase of the machined surface can

be extracted and the surface profile can be obtained.

Figure 8. Free-space KEI experiment setup of cutting tool damage detection

For comparison, the surface profile of the sample was

measured by a commercially available white-light

interferometer microscope (Zygo), which had an average

roughness Ra of 7nm. The surface profile was measured by the

proposed fiber-optic Fizeau interferometer and compared in

Fig. 7. This surface profile with a small dent in a narrow width

is normally difficult to measure, even for the commercially

available white-light interferometer. The proposed

measurement result showed results similar to those obtained

with a white light interferometer. In this experiment, it was seen

that the measured surface profile using the proposed phase-

locked interferometry showed a good agreement with that of

the white-light interferometer microscope and had an

observable flaw size depth of about 100nm.

CUTTING TOOL DAMAGE DETECTION METHOD Fundamental understanding of the cutting tool morphology

properties and its quantitative measurement and

characterization methods are not well-documented. A KEI

technique has recently been created for sharp knife-edge profile

characterization, but fundamental questions on the

interferometric effects of randomly-varying rough knife-edge

remain unanswered. This establishes a new paradigm for the

future study of damage detection and morphology

characterization of cutting tools. Unlike an amplitude-splitting

interferometer, such as a Michelson, the KEI utilizes

interference of a transmitted wavefront as a reference and a

diffracted (or scattered) wavefront under test at the knife-edge.

The interference fringes created by diffraction at the cutting

tool edge are highly sensitive not only to the cutting tool’s

geometry but also to its morphology.

In this study, KEI effects will be tested on various cutting

tools for turning, and then, we will quantify these edge

conditions by creating free-space and fiber-coupled OMM

systems for damage detection and morphology characterization

of the cutting tools. This research promises to create a new

transformative damage detection principle in cutting tools for

improving reliability and promoting automation of

manufacturing processes. The successful implementation of this

research will potentially lead to a fundamental shift in

measurement methodologies in structural health monitoring of

cutting tools such as morphology property characterization and

the life cycle expectancy of cutting tools. Additionally, this

research will lead to the widespread use of a new measurement

methodology that can be used for a variety of edge surface

sensing, monitoring, and inspection for AFM and STP probes,

grinding wheels, or milling tools. This work will focus on three

step-wise strategic aims:

1) Cutting tool edge morphology characterization: Characterize

the geometry of various cutting tools for turning by using

conventional microscopes. Determine the amplitude and power

of harmonic wave components for cutting tool edge

morphology modeling by a fast Fourier transform analysis.

Relate the cutting tool edge geometry model with knife-edge

diffraction characteristics according to various edge

morphology conditions.

2) Free-space KEI technique for cutting tool damage detection:

Create a KEI setup and calibrate the displacement of a knife-

edge with a capacitive type sensor. The displacement of the

knife edge can be considered as a wear effect of cutting tools.

Establish image correlation methods to quantify the cutting tool

wear, roughness, chipping, and even breakage by relating edge

diffraction fringe curves with cutting tool edge conditions.


Also, investigate knife-edge effects of cutting tools with

different shapes in the same way above.

We will build free-space KEI test bench on an optical

isolator to verify the proposed knife-edge model as seen in Fig.

8. The solid-state laser (Verdi G2, Ti:Sapphire pumping, λ

532nm, Coherent Inc.) and silicon photodiode detector (918D,

Newport Inc.) with pinholes (10, 50, 100, 200, 500, 1000μm)

will be used. The modulated Ti: Sapphire laser will be incident

on the cutting tool edge through a polarizing filter and spatial

filter, and the PD can detect the interferogram created by

superposition of the transmitted and diffracted lights. For a

preliminary study, we will monitor changes in the

interferogram by scanning the cutting tool with picomotor PZT

actuator (30nm resolution). Nanopositioning control will be

implemented with a capacitive sensor (CS, 10nm resolution,

Lion Precision Inc.). A lock-in amplifier (LIA, Stanford

Research Systems Inc.) will collect the signals from modulator

and PD and allow high accuracy measurement through a

demodulation process. All data produced in the experiment will

be monitored in real-time in a LabView environment.

Figure 9. Auto correlation of normalized total power curves: (a) normalized total power curves according to

the cutting tool conditions and (b) cross correlation (abbreviated to Corr) results of two sets of curves.

Spatial cross correlation statistics will be used to measure

and analyze the cutting tool wear, especially for edge

morphology. As studied earlier, the more the edge morphology

is rough and dull, the more incoherent the diffracted field

becomes because the phase between the transmitted and

diffracted fields changes according to edge morphology

conditions. The interference of the transmitted and edge-

diffracted field can theoretically explain the general oscillatory

behavior. Note that as the edge roughness increases, the

amplitude oscillation decreases due to the destruction of the

phase interference between the transmitted and diffracted fields

as seen in Fig. 9. Assuming a nanoscale edge morphologic

change results in decrease in the oscillation amplitude and is

too small to cause a shift of the curves, the cross-correlation

technique as a measure of similarity of two curves with no lag

can again be performed. In a preliminary study, the cross

correlation coefficient of two curves (new f v.s. used g1, g2, or

g3) decreased as the edge became rough. Firstly, three new

diamond cutting tools with various pinhole sizes will be

measured and the most sensitive condition will be chosen.

Secondly, each cutting tool will be used to cut the metal

(Cu/Al) and an interferogram of each cutting tool on every 1

mile basis will be measured. Lastly, a correlation between

signal processing and each interferogram will be related with

the cutting tool wear condition. For reference, we will use AFM

and SEM images. The next objective is to apply a correlation

method to cutting tool condition monitoring, to establish a

quantitative relationship between correlation and cutting tool

micro/nanoscale morphology, and to provide a sensing limit of

free-space KEI and its signal processing for the cutting tool

damage detection in terms of accuracy, resolution, repeatability,

stability, and uncertainty.

Figure 10. Fiber-coupled OMM system setup.

3) Fiber-coupled OMM system for cutting tool damage

detection: Create an optical fiber-based delivery method for an

OMM configuration, implement fiber-coupled OMM system,

characterize sensing performance of cutting tool damage in the

same way done above, and compare it with free-space KEI

results. Determine the viability for characterizing and

quantifying the cutting tool damage for fiber-based OMM

configurations using KEI technique.

The chosen optical fiber-based probe has a pigtailed laser

diode (PLD) module that has the same wavelength, 532nm, as

the Verdi G2. This system is highly flexible to adjust optical

offset distances, and it is reliable, because it utilizes fiber-based

light delivery as seen Fig. 10. The best optical offset distance

between the fiber-end and cutting tool will be determined by

not only properly adjusting the distance but also theoretically

calculating the distance. The light from the fiber-based probe

has a divergence angle along the propagation direction and will

show a different interferometric behavior with the Ti-Sapphire

laser system. The fiber-coupled measurement system will be

characterized in terms of resolution, accuracy, repeatability and

stability and these outputs will be compared with those of a

free-space measurement system.

CONCLUSION Until recently, there was no way to quantitatively inspect

damage and/or breakage of cutting tools other than visually, and

these morphology properties and characterization methods have

not been well-documented. This research promises to create a

new and transformative damage detection principle in cutting

tools for improving reliability and promoting automation of

manufacturing processes. This research will also answer


fundamental questions regarding the performance boundaries of

the knife-edge interferometer and on-machine measurement

technologies for cutting tool damage detection applications, and

it will produce new knowledge on the measurement,

instrumentation and sensors.

The fundamental questions on the interferometric effects of

randomly-rough knife-edge will be answered. In addition, we

will determine the fundamental sensing limits in free-space and

fiber-coupled on-machine measurement systems by testing

performance, compatibility, and reliability. The successful

implementation of this research will potentially lead to a

fundamental shift in measurement methodologies in structural

health monitoring of cutting tools, such as morphology property

characterization and life cycle expectancy of cutting tools. In

addition, this research will lead to the widespread use of a new

measurement methodology that can be used for a variety of

edge surface sensing, monitoring, and inspection tasks for

atomic force microscopy or touch-triggered stylus probes,

diamond cutting tools, grinding wheels, or milling tools.

ACKNOWLEDGEMENT The research was supported by NSF (Award Number:

CMMI 1463502/1463458) through the Tennessee

Technological University. Also, the authors gratefully

acknowledge use of facilities and instruments of the Gwangju

Institute of Science and Technology.

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