[1]

CHAPTER 1: - COMPANY PROFILE

1.1 SKF Group

1.1.1 History and Present

SKF, Svenska Kullagerfabriken AB (Swedish: Swedish ball bearing factory AB), is

a Swedish bearing company founded in 1907, supplying bearings, seals, lubrication and

lubrication systems, maintenance products, Mechatronics products, power transmission

products and related services globally.

The company was founded on Sven Wingqvist's in 1907 Swedish patent No. 25406, a

multi-row self-aligning radial ball bearing. The Patent was granted on 6 June in Sweden

coinciding with patents in 10 other countries. The new ball bearing was successful from the

outset. By 1910, the company had 325 employees and a subsidiary in the United

Kingdom. Manufacturing operations were later established in multiple countries.

By 1912, SKF was represented in 32 countries and by 1930; a staff of over 21,000

were employed in 12 manufacturing facilities worldwide with the largest in Philadelphia, PA.

Today, SKF is the largest bearing manufacturer in the world and employs

approximately 46,775 people in 140 manufacturing sites that span 32 countries.

Turnover for FY 2012 was SEK 49,285 million, and total assets were SEK 40,349 million.

The SKF Group currently consists of approximately 150 companies.

SKF group has extended its roots all over the world. Its main plants are located in 24

countries. Around 56,000 employees are connected with SKF all over the world. 1 out of 5

bearings in the world must be of SKF’s.

1.1.2 Products

SKF sells products within five technology platforms:

Bearings and Units

Mechatronics

Lubrication Systems

Services

Seals

Mechatronics: Within Mechatronics, SKF is combining its strong mechanical experience

and electronic technology. This covers systems for precision multi-axis positioning,

intelligent monitoring and by-wire applications, as well as components such as ball and roller

screws, actuators, rail guides and sensor modules. A number of mechanical and electronic

products are combined into modules and sub-systems addressing unique needs where SKF

has specialist industrial-specific expertise.

Applications are:

• Linear motion assortment

• High efficiency screws and guides, Electromechanical actuators

• Magnetic bearing system and one sensitized bearing,

• Aeronautical throttle control + Electromechanical Parking Brake

[2]

Lubrication System: SKF offers products, solutions and vast support within areas such as

industrial lubricants, lubrication consultancy, lubricator equipment, lubrication assessment,

lubricant analysis, lubricant recommendations and automatic lubrication systems. Around

36% of premature bearing failures are caused by poor or inadequate lubrication. SKF helps

businesses to prevent these costly failures. SKF delivers the right lubricant, in the right

amount, at the right time, with the right lubrication system to the right lubrication point. SKF

is a global partner for lubrication systems and tribology knowledge, the combination of

friction, wear and lubrication sciences. SKF provides integrated lubrication solutions with

leading technologies, where and when SKF’s customers need them and across all industry

segments and applications.

Some of the Applications are:

• Lubricants [SKF Bearing Grease] – Manual Lubricators [SKF Grease Gun]

• Single-point Lubricators [SKF SYSTEM 24 LAGD, automatic gas driven single point

automatic lubricator suitable for many applications]

• Centralized Lubrication Systems – Grease Systems

• Centralized Lubrication Systems – Minimal Quantity Lubrication [MQL] Systems

• Centralized Lubrication Systems – Oil Circulating Systems

Services: The Service platform delivers value throughout the entire life cycle of an asset.

Some of the services offered are Engineering consultancy and engineering services in the

design phase, maintenance and logistics services in the operations stage and in the final stage

upgrading, refurbishment, bearing dismounting and mounting, alignment, balancing and post-

maintenance testing. SKF also offers a wide spectrum of training for customers, on and off

site, around the globe.

1.1.3 Customers

SKF has many customers in various fields like Automotive, Electrical, Industrial, Textile,

Service, Aero etc.

Automotive : Motor Cars, Trucks, Buses and Vehicle aftermarket

Electrical : Two-Wheelers, Household Appliances, Electrical Motors

Industrial : Equipment Manufacturers, Linear Motion, Precision Tools

Aero : Aero planes, Space Shuttles, Space Stations

SKF is expanding its area by introducing itself in other manufacturing services than

bearing manufacturing; e.g. fields like Mechatronics, lubrication system, textile, etc.

[3]

1.1.4 Vision, Mission and Commitment

The Motto of SKF is “The Power of Knowledge Engineering”.

SKF’s Vision:- To equip the world with SKF knowledge

SKF’s Mission:- To be the preferred company...

- For our customers, distributors and suppliers: delivering industry-leading, high value products, services and knowledge-engineered

solutions;

- For our employees: creating a satisfying work environment where efforts are recognized, ideas valued, and

individual rights respected;

- For our shareholders: delivering shareholder value through sustainable earnings growth.

SKF’s values :- High Ethics: Committed to conducting business responsibly towards the

environment, society and each other. We recognize equal rights of all individuals.

We constantly strive to maintain a good working climate.

Openness: Our success is much due to the policy of attentive openness, an open

line of communication between employees, customers, suppliers, partners and the

larger community.

Empowerment: Helping each other succeed in our job and where initiative is

encouraged.

Team work: Creating an environment of diversity, where people work in teams

not only within their own units, but also across organisational and geographical

borders.

Drivers :- Profitability: Profitability is what enables us to make both short and long-term

investments, maintain our commitment to research, development and innovation,

and drive overall growth and development of the organisation.

Quality: We will be the quality leader in everything we do. It is about doing our

very best, every single time.

Innovation: Staying ahead of the competition means being the industry innovator

– the first with customer-focused solutions.

Speed: The increasing speed of change in our market environments requires us to

be faster and more flexible in everything we do. Speed is about timely delivery of

products, solutions and offers. It’s about reducing the time span between having

an idea or making a decision and putting it into action.

Sustainability: We are committed to run and develop our business successfully,

with our responsibility to safeguard resources for future generations.

SKF’s Commitment:- SKF is committed to environmentally responsible growth. We are dedicated to

combine our responsibility to run and develop our business successfully, with our

responsibility to safeguard resources for future generations.

[4]

1.2 SKF India Limited

SKF started its operations in India in 1923 and today provides industry leading

automotive and industrial engineered solutions through its five technology-centric platforms:

bearings and units, seals, Mechatronics, lubrication solutions and services. Over the years the

company has evolved from being a pioneer ball bearing manufacturing company to a

knowledge-driven engineering company helping customers achieve sustainable and

competitive business excellence.

SKF's solutions provide sustainable ways for companies across the automotive and

industrial sectors to achieve breakthroughs in friction reduction, energy efficiency, and

equipment longevity and reliability. With a strong commitment to research-based innovation,

SKF India offers customized value added solutions that integrate all its five technology

platforms.

SKF has a pan India footprint consisting of 6 manufacturing facilities, 12 offices, a

supplier network of over 300 distributors and an employee base of more than 2600 dedicated

professionals. In India, SKF has consolidated its operations in three different companies -

SKF India Limited, SKF Technologies (India) Pvt. Ltd and Lincoln Helios India Ltd.

1.2.1 Company Details: -

CEO and President: Tom Johnston

Managing Director (SKF India): Shishir Joshipura

Website: www.skf.com / www.skfindia.com

Turnover 2011: INR 26 million

No of employees: 2,800

Year established: 1961

Number of manufacturing and operational sites: 6 manufacturing sites and 6 Regional

offices

Environment: Global ISO 14001 certification

OHSAS 18001 certification

Organization: The SKF business is organized into three business areas: Industrial Market, Strategic

Industries; Industrial Market, Regional Sales and Service; and Automotive. Each business

areas serves a global market, focusing on its specific customer segments. There are seven

staff units: Group Finance and Corporate Development; Group People and Business

Excellence; Group Communication; Group Legal and Sustainability; Group

Purchasing; Group Technology Development and Group Business Transformation.

Registered office address:

SKF India Limited

Mahatma Gandhi Memorial Building

Netaji Subhash Road, Mumbai 400002

[5]

1.2.2 Manufacturing Plants:

1. Pune Factory

Year of establishment: 1965

Certifications: ISO 9001 ISO 140041 OHSAS 18001

Segments: Automotive, Industrial Electrical

Product range: Bearings (Small DGBB, Medium DGBB TRB, THU, HBU, Thin section

BB)

2. Bangalore Factory

Year of Establishment : 1989

Certification : TS 16949 / ISO 14000

Segments : Automotive, Industrial

Product Range : DGBB, Value Added Solutions ( Rocker Arm Bearing, Rocker Arm

assembly, Clutch Lifter, Cam Follower, Cylindrical rollers, Solid oil ball cage, Steering

Column bearings, One Way clutch), Customized products and assemblies ( Postal Rollers,

Sheave assemblies, Pulley assemblies, Roller assemblies).

3. Haridwar Factory Year of Establishment : March 2010

Certification : Certified to ISO 14001 & OSHAS 18001

Segments : Two wheeler segment

Product Range : DGBB

4. Ahmedabad Factory Year of establishment: 2009

Certifications: Leadership in Energy & Environmental Design (LEED)

Segments: power generation, renewable energy, construction, mining and material

handling

Product range: Wide range of medium and large bearings.

5. SKF Sealing Solutions Factory - Pune

Year of Establishment : 1999

Certification : TS 16949 / ISO 14000, OSHAS 18001, M1003 AAR, ISO 9001: 2008

Segments: Automotive, Industrial.

Product Range : Engine seals, Transmission seals, Suspension seals, Wheel seals, Radial

and axial shaft seals, Wear sleeves, Hydraulic seals, Static seals, Integrated Compact Oil

Sealed Unit, Sensor-bearing Unit, Washing Machine Drum Bearing Unit, R-Safe

Seal(with Tone Wheel)

[6]

CHAPTER 2: PLANT IN FOCUS

PUNE FACTORY

The first plant in India was laid at Pune in 1965. Pune plant is the largest plant in

India. Here the two types of bearings are being manufactured and assembled viz. Taper

Roller Bearing (TRB) and Deep Groove Ball Bearing (DGBB). The cages, seals, rollers,

balls, inner rings and outer rings are forged in other industries and SKF purchases these

materials from other suppliers. Here raw inner and outer rings are brought from outside and

then heat treatment, Grinding, Honing operations are done on them. For raw balls, lapping

process is preferred to turn them into very finished, shining and smooth ones.

Picture:- Deep Groove Ball Bearing (DGBB) Picture:- Taper Roller Beraing (TRB)

Some other types of bearings which are manufactured by SKF are:

Angular contact ball bearing

Self aligning ball bearing

Cylindrical roller bearing

Spherical roller bearing

CARB toroidal roller bearing

Thrust ball bearing

Cylindrical roller thrust bearing

Spherical roller thrust bearing

Housings

Company has solution factory where various types of bearings from small up to large

sizes are repaired, serviced; which are brought from outside companies. It has global

laboratory which is the only in SKF Asia.

[7]

SKF INDIA (Pune) is also manufacturing textile components. Company established

the training college in its campus to teach and to give information about new arising

technologies, to deliver lectures on Six Sigma skills (Green Belt and Black Belt).

Here, in company there is a workers’ union where a worker can raise his problems

arising in company. The union leader tries to solve them.

SKF has excellent canteen facilities. The company provides good quality food. The

company pays attention very carefully on the food quality for employees. There is provision

for breakfast, lunch, evening refreshment and dinner for employees. Company provides fresh

and pure water to drink.

Production in company runs for 24 hours × 7 days. It is divided into three shifts per

day. Transport system is good. Sufficient buses are supplied to pick up and drop workers

place to place.

Various units working at Pune division are:-

Automotive Business Unit (ABU)

Electrical Business Unit (EBU)

Industrial Business Unit (IBU)

Service Business Unit (SBU)

SKF INDIA LTD: -A GLANCE AT PUNE FACTORY

1. Address : Dalvi Nagar, Chinchwad, Pune 33

2. Established : 1961

3. Site Area : 4,15,000 sq mts

4. Built up Area : 69,954 sq mts

5. No of Employees : 2600

6. Products : Deep Groove Ball Bearings, Taper Roller Bearings and

Hub Bearing Units.

7. Product size : 22 to 168 mm

8. Production Facilities : DGBB – 8 Channels, Product Size-24 to 140 mm

TRB- 9 Channels, Product size- 40to 168 mm

9. Customers’ : DGBB :- Delphi TVS dies, Toyota, Yamaha, Bajaj Auto,

Crompton Greaves, Ford, Fiat, Mahindra & Mahindra, etc

TRB : - Mahindra & Mahindra, Tata Motors, Spicer’s, John

Deere, Volkswagen, Ford, etc.

10. Divisions : Automotive Business Unit (ABU)

Electrical Business Unit (EBU)

Industrial Business Unit (IBU)

Service Business Unit (SBU)

Textile Business Unit (TBU)

11. Certifications : ISO 9001 ISO 140041 OHSAS 18001

[8]

Textile Business Unit (TBU)

Departments:

Six Sigma, Maintenance, Multi skill, Purchase, QA, Manufacturing, etc are the main

departments in the company. All departments are interrelated.

Six Sigma: In this department quality, tolerance limit of bearings are important factors.

Capability of outer rings and inner rings are found out by team of six sigma department.

Process capability charts are made. Then six sigma team tries to improve capability of rings.

There are two stages for the six sigma project; one is Green Belt & next is Black Belt.

Advanced project stage in it is Master Black Belt. Employees are working here according to

their project level.

Some points for Six Sigma project description:

Driven by customer focus and business results

Fact based, data driven decisions

Clearly defined and limited scope

Project duration limited to 4-6 months

Full time committed black belts

Part time committed green belts

The aim of the six sigma project is to achieve the zero-defect production.

Phases of six sigma project:

Figure: Six Sigma Phases

Problem identified

by line manager

i.e. project sponor

Project approved

i.e.by steering group

Black/Green belt project starts

End of project

and Belt leaves project

Implementaton

i.e. line manager responsible for

results

[9]

Multi skill: It is training department. Here the training is given to apprentices about the

grinding, honing operations and assembly operations. Operating processes i.e. ‘how to

operate machines’ are taught to apprentices. To comprehend the processes clearly the training

modules have been prepared. The modules explain the process, resetting of machine and

errors occurring while work.

Maintenance: There are two sections in maintenance department viz. TRB maintenance and

DGBB maintenance. In this there are subdivisions as Mechanical maintenance and Electrical

maintenance.

Manufacturing Excellence: Tool designing is held in this department. These tools are

required for machining processes. It also runs a sub division: application Engineering.

Production: There are various channels of grinding and assembly of bearings according to

different types. In TRB section there are eight channels; while in DGBB section there are

eight channels. TRB and DGBB sections include hub bearing channel.

Customers connected with TRB: Mahindra & Mahindra, Tata Motors, Spicer’s,

John Deer, Volkswagen, Ford, etc.

Customers connected with DGBB: Delphi TVS dies, Toyota, Yamaha, Bajaj Auto,

Crompton Greaves, Ford, Fiat, Mahindra & Mahindra, etc.

Heat treatment: In this department non finished outer and inner rings are heat treated. The

processes are done on rings as follows:

Heating & soaking Quenching Washing

Tempering Cooling

Figure: Heat Treatment Process

[10]

Purchase: Purchase Department follows the functions given below:

Procurement Planning as per Indents

Vendor Selection and Development

Tendering and Evaluation of Tenders

Follow‐up with suppliers on expediting delivery of materials / services.

Co‐ordination with Finance & Accounts department for timely payment to suppliers.

Performance evaluation of delivered material / services.

Vendor Evaluation and Rating.

Sales:

The main function of a sales department is to attract and to retain customers.

Sales managers decide prices of product and try to sell it in better value.

Quality Assurance: In this department quality of product is checked and controlled. If

product made is not as per scale then it does not allow going out for selling.

Resetting: It is divided in two sections, TRB resetting and DGBB resetting. This department

provides all types of tools required for setting of machines. Presetting is the specific section

where some fixtures, tools are set before let them use for actual setting.

Finance: This department manages the cash flow of whole company. It decides the cost of

products. It decides salary of employees.

Human Resource: HR department recruits the people as an employee in company. It does

workforce planning, personnel cost planning. It holds the training and development programs.

It takes performance appraisal.

SKF Reliability Maintenance Institute: SKF offers a comprehensive suite of reliability &

maintenance training courses designed to help plans reduce machinery problems and achieve

maximum reliability and productivity. The training covers most aspects of an Industrial

Requirement. It includes courses on Mechanical and Electrical Maintenance; Condition

Monitoring, Planning and Strategy, Business and Manufacturing Excellence etc.

[11]

2.1.1. Pune Factory Process Flow

FINAL PACKAGING STORES &LOGISTICS AND DISPATCH

ASSEMBLY= IR+OR

BALL FILLING CAGE FITTING LASER MARKING LUBRICATION FINAL INSPECTION

OR

FACE GRINDING IR BORE GRINDINGIR GROOVE GRINDING

IR GROOVE HONNING

DEMAGNETISING WASHING

IR

FACE GRINDING IR BORE GRINDINGIR GROOVE GRINDING

IR GROOVE HONNING

DEMAGNETISING WASHING

HEAT TREATMENT

RECEIVING STORES:- INWARD INSPECTION

[12]

CHAPTER 3:- LITRATURE REVIEW

3.1 Bearings

A bearing is a type of rolling-element bearing that uses balls to maintain the

separation between the bearing races. The purpose of a ball bearing is to reduce rotational

friction and support radial and axial load. It achieves this by using at least two races to

contain the balls and transmit the loads through the balls. In most applications, one race is

stationary and the other is attached to the rotating assembly (e.g., a hub or shaft). As one of

the bearing races rotates it causes the balls to rotate as well. Because the balls are rolling they

have a much lower coefficient of friction than if two flat surfaces were sliding against each

other.

Ball bearings tend to have lower load capacity for their size than other kinds of

rolling-element bearings due to the smaller contact area between the balls and races.

However, they can tolerate some misalignment of the inner and outer races. There are several

common designs of ball bearing, each offering various trade-offs. They can be made from

many different materials, including: stainless steel, chrome steel, etc

A bearing is a device to allow constrained relative motion between two or more parts,

typically rotation or linear movement. Bearings may be classified broadly according to the

motions they allow and according to their principle of operation as well as the direction of

applied loads they can handle.

However, there are many applications where a more suitable bearing can improve

efficiency, accuracy, intervals, reliability, and speed of operation, size, weight, and costs of

purchasing and operating machinery.

Thus, there are many types of bearings with varying shape, material, lubrication,

principle of operation and so on. For example, rolling –element bearings use spheres or

drums rolling between the parts to reduce friction ; reduced friction allows tighter tolerances

and thus higher precision than a plain bearing and reduced wear extents the time over which

the machine stays accurate. Plain bearing are commonly made of varying types of metal or

plastic depending on the load, how corrosive or dirty the environment is, and so on . in

addition, bearing friction and life may be altered dramatically by the type and application of

lubricants. For example, a lubricant may improve bearing friction and life, but for food

processing a bearing may be lubricated by an inferior food-safe lubricant to avoid food

contamination; in other situations a bearing may be run without a lubricant because

continuous lubrication is not feasible, and lubricants attract dirt that damages the bearings.

3.2 Types of Bearing

There are many types of bearings with varying shape, material, lubrication, principle

of operation and so on.

[13]

3.2.1 Angular Contact Ball Bearing

An angular contact ball bearing uses axially asymmetric races. An axial load passes

in a straight line through the bearing, whereas a radial load takes an oblique path that tends to

want to separate the races axially. So the angle of contact on the inner race is the same as that

on the outer race. Angular contact bearings better support "combined loads" (loading in both

the radial and axial directions) and the contact angle of the bearing should be matched to the

relative proportions of each. The larger the contact angle (typically in the range 10 to 45

degrees), the higher the axial load supported, but the lower the radial load. In high speed

applications, such as turbines, jet engines, and dentistry equipment, the centrifugal forces

generated by the balls changes the contact angle at the inner and outer race. Ceramics such

as silicon nitride are now regularly used in such applications due to their low density (40% of

steel). These materials significantly reduce centrifugal force and function well in high

temperature environments. They also tend to wear in a similar way to bearing steel—rather

than cracking or shattering like glass or porcelain. Most bicycles use angular-contact bearings

in the headsets because the forces on these bearings are in both the radial and axial direction.

3.2.2 Axial Ball Bearing

An axial ball bearing uses side-by-side races. An axial load is transmitted directly

through the bearing, while a radial load is poorly supported and tends to separate the races, so

that a larger radial load is likely to damage the bearing.

3.2.3 Deep-Groove Ball Bearing

These types of bearings are particularly versatile. They are simple in design and non-

separable, suitable for very high speeds in operation and require only little maintainence. In

a deep-groove radial bearing, the race dimensions are close to the dimensions of the balls that

run in it. Deep-groove bearings can support higher loads.

Fig: - Profile of a Bearing (DGBB)

[14]

3.2.4 Taper Roller Bearing [TRB]

The taper roller bearing is a type of contact bearing. It consists of rolling element in

the form of frustum of cone. They are arranged in such a way that the axes of individual

elements intersect at a common apex point on the axis of the bearing. The taper form of the

raceways make this bearing eminently suitable for combine radial and axial loads. The line of

resultant reaction through the rolling element makes an angle with the axis of the bearing.

Therefore, taper roller bearing carries both the radial and axial loads. Taper roller bearings

subjected to pure radial load induces thrust component and vice versa. Therefore, taper roller

bearings always used in pair to balance the components. Taper roller bearing has separable

construction. The outer ring is separable from the remainder of the bearing. It has three types

of profiles, straight, crown and logarithmic decrement. It has following advantages:-

It can take heavy radial and thrust load

Taper roller bearing has more rigidity

It can be easily assembled and disassembled due to separable construction

Taper roller bearings are used for cars and trucks, propelled shafts & differential,

railroad axle boxes and large size bearings in rolling mills.

3.2.5 Self-Aligning Ball Bearings

Self-aligning ball bearings, such as the Wingquist bearing shown above, are

constructed with the inner ring and ball assembly contained within an outer ring that has a

spherical raceway. This construction allows the bearing to tolerate a small angular

misalignment resulting from shaft or housing deflections or improper mounting. The bearing

was introduced by SKF in 1907.[6] The bearing was used mainly in bearing arrangements

with very long shafts, such as transmission shafts in textile factories.[7] One drawback of the

self-aligning ball bearings is a limited load rating, as the outer raceway has very low

osculation (radius is much larger than ball radius). This lead to the invention of the spherical

roller bearing, which has a similar design, but uses rollers instead of balls. Also the spherical

roller thrust bearing is an invention that derives from the findings by Wingquist.

Fig:- Self-aligning ball bearings

[15]

3.3 . Bearing Life

The calculated life for a bearing is based on the load it carries and its operating speed.

The industry standard usable bearing lifespan is inversely proportional to the bearing load

cubed. Nominal maximum load of a bearing (as specified for example in SKF datasheets), is

for a lifespan of 1 million rotations, which at 50 Hz (i.e., 3000 RPM) is a lifespan of 5.5

working hours. 90% of bearings of that type have at least that lifespan, and 50% of bearings

have a lifespan at least 5 times as long.

The industry standard life calculation is based upon the work of Lundberg and

Palmgren performed in 1947. The formula assumes the life to be limited bymetal fatigue and

that the life distribution can be described by a Weibull distribution. Many variations of the

formula exist that include factors for material properties, lubrication, and loading. Factoring

for loading may be viewed as a tacit admission that modern materials demonstrate a different

relationship between load and life than Lundberg and Palmgren determined .

3.4. Failure modes

If a bearing is not rotating, maximum load is determined by force that causes plastic

deformation of elements or raceways. The identations caused by the elements can concentrate

stresses and generate cracks at the components. Maximum load for not or very slowly

rotating bearings is called "static" maximum load. For a rotating bearing, the dynamic load

capacity indicates the load to which the bearing endures 1.000.000 cycles.

If a bearing is rotating, but experiences heavy load that lasts shorter than one

revolution, static max load must be used in computations, since the bearing does not rotate

during the maximum load.[8]

In general, maximum load on a ball bearing is proportional to outer diameter of the

bearing times width of bearing (where width is measured in direction of axle). Bearings have

static load ratings. These are based on not exceeding a certain amount of plastic deformation

in the raceway. These ratings may be exceeded by a large amount for certain applications.

3.5. Lubrication

For a bearing to operate properly, it needs to be lubricated. In most cases the lubricant

is based on elastohydrodynamic effect (by oil or grease) but working at extreme

temperatures dry lubricatedbearings are also available.

For a bearing to have its nominal lifespan at its nominal maximum load, it must be

lubricated with a lubricant (oil or grease) that has at least the minimum dynamic viscosity

(usually denoted with the Greek letter ) recommended for that bearing. The recommended

dynamic viscosity is inversely proportional to diameter of bearing.

The recommended dynamic viscosity decreases with rotating frequency. As a rough

indication: for less than 3000 RPM, recommended viscosity increases with factor 6 for a

[16]

factor 10 decrease in speed, and for more than 3000 RPM, recommended viscosity decreases

with factor 3 for a factor 10 increase in speed.

For a bearing where average of outer diameter of bearing and diameter of axle hole

is 50 mm, and that is rotating at 3000 RPM, recommended dynamic viscosity is 12 mm²/s.

Note that dynamic viscosity of oil varies strongly with temperature: a temperature increase

of 50–70 °C causes the viscosity to decrease by factor 10. If the viscosity of lubricant is

higher than recommended, lifespan of bearing increases, roughly proportional to square root

of viscosity. If the viscosity of the lubricant is lower than recommended, the lifespan of the

bearing decreases, and by how much depends on which type of oil being used. For oils with

EP ('extreme pressure') additives, the lifespan is proportional to the square root of dynamic

viscosity, just as it was for too high viscosity, while for ordinary oil's lifespan is proportional

to the square of the viscosity if a lower-than-recommended viscosity is used.[8]

Lubrication can be done with a grease, which has advantages that grease is normally

held within the bearing releasing the lubricant oil as it is compressed by the balls. It provides

a protective barrier for the bearing metal from the environment, but has disadvantages that

this grease must be replaced periodically, and maximum load of bearing decreases (because if

bearing gets too warm, grease melts and runs out of bearing). Time between grease

replacements decreases very strongly with diameter of bearing: for a 40 mm bearing, grease

should be replaced every 5000 working hours, while for a 100 mm bearing it should be

replaced every 500 working hours.[8]

Lubrication can also be done with an oil, which has advantage of higher maximum

load, but needs some way to keep oil in bearing, as it normally tends to run out of it. For oil

lubrication it is recommended that for applications where oil does not become warmer

than 50 °C, oil should be replaced once a year, while for applications where oil does not

become warmer than 100 °C, oil should be replaced 4 times per year. For car engines, oil

becomes 100 °C but the engine has an oil filter to continually improve oil quality; therefore,

the oil is usually changed less frequently than the oil in bearings.[8]

3.7. Direction of load

Most bearings are meant for supporting loads perpendicular to axle ("radial loads").

Whether they can also bear axial loads, and if so, how much, depends on the type of

bearing. Thrust bearings(commonly found on lazy susans) are specifically designed for axial

loads.

For single-row deep-groove ball bearings, SKF's documentation says that maximum

axial load is circa 50% of maximum radial load, but it also says that "light" and/or "small"

bearings can take axial loads that are 25% of maximum radial load.[8]

For single-row edge-contact ball bearings, axial load can be circa 2 times max radial load,

and for cone-bearings maximum axial load is between 1 and 2 times maximum radial load.[8]

Often Conrad style ball bearings will exhibit contact ellipse truncation under axial

load. What that means is that either the ID of the outer ring is large enough, or the OD of the

[17]

inner ring is small enough, so as to reduce the area of contact between the balls and raceway.

When this is the case, it can significantly increase the stresses in the bearing, often

invalidating general rules of thumb regarding relationships between radial and axial load

capacity. With construction types other than Conrad, one can further decrease the outer ring

ID and increase the inner ring OD to guard against this.

If both axial and radial loads are present, they can be added vectorially, to result in

total load on bearing, which in combination with nominal maximum load can be used to

predict lifespan.[8]However, in order to correctly predict the rating life of ball bearings the

ISO/TS 16281 should be used with the help of a calculation software.

The part of a bearing that rotates (either axle hole or outer circumference) must be fixed,

while for a part that does not rotate this is not necessary (so it can be allowed to slide). If a

bearing is loaded axially, both sides must be fixed.

If an axle has two bearings, and temperature varies, axle shrinks or expands, therefore

it is not admissible for both bearings to be fixed on both their sides, since expansion of axle

would exert axial forces that would destroy these bearings. Therefore, at least one of bearings

must be able to slide.[8]A 'freely sliding fit' is one where there is at least a 4 µm clearance,

presumably because surface-roughness of a surface made on a lathe is normally between 1.6

and 3.2 µm.[8]

3.8. Fit

Bearings can withstand their maximum load only if the mating parts are properly

sized. Bearing manufacturers supply tolerances for the fit of the shaft and the housing so that

this can be achieved. The material and hardness may also be specified.[8]

Fittings that are not allowed to slip are made to diameters that prevent slipping and

consequently the mating surfaces cannot be brought into position without force. For small

bearings this is best done with a press because tapping with a hammer damages both bearing

and shaft, while for large bearings the necessary forces are so great that there is no alternative

to heating one part before fitting, so that thermal expansion allows a temporary sliding fit.

If a shaft is supported by two bearings, and the center-lines of rotation of these

bearings are not the same, then large forces are exerted on the bearing that may destroy it.

Some very small amount of misalignment is acceptable, and how much depends on type of

bearing. For bearings that are specifically made to be 'self-aligning', acceptable misalignment

is between 1.5 and 3 degrees of arc. Bearings that are not designed to be self-aligning can

accept misalignment of only 2–10 minutes of arc.

[18]

CHAPTER 4:- DEPERTMENT IN FOCUS

SIX SIGMA

4.1. Six Sigma: - Overview

The term "six sigma process" comes from the notion that if one has six standard

deviations between the process mean and the nearest specification limit, as shown in the

graph, practically no items will fail to meet specifications. This is based on the calculation

method employed in process capability studies.

Capability studies measure the number of standard deviations between the process

mean and the nearest specification limit in sigma units, represented by the Greek letter σ

(sigma). As process standard deviation goes up, or the mean of the process moves away from

the center of the tolerance, fewer standard deviations will fit between the mean and the

nearest specification limit, decreasing the sigma number and increasing the likelihood of

items outside specification.

Six Sigma is a set of techniques and tools for process improvement. It was developed

by Motorola in 1986, coinciding with the Japanese asset price bubble which is reflected in its

terminology. Six Sigma became famous when Jack Welch made it central to his successful

business strategy at General Electric in 1995. Today, it is used in many industrial sectors.

Six Sigma seeks to improve the quality of process outputs by identifying and

removing the causes of defects (errors) and minimizing variability in

manufacturing and business processes. It uses a set of quality management methods,

including statistical methods, and creates a special infrastructure of people within the

organization ("Champions", "Black Belts", "Green Belts", "Yellow Belts", etc.) who are

experts in the methods. Each Six Sigma project carried out within an organization follows a

defined sequence of steps and has quantified value targets, for example: reduce process cycle

time, reduce pollution, reduce costs, increase customer satisfaction, and increase profits.

These are also core to principles of Total Quality Management (TQM) as described by Peter

Drucker and Tom Peters (particularly in his book "The Pursuit of Excellence" in which he

refers the Motorola six sigma principles).

The term Six Sigma originated from terminology associated with manufacturing,

specifically terms associated with statistical modeling of manufacturing processes. The

maturity of a manufacturing process can be described by a sigma rating indicating its yield or

the percentage of defect-free products it creates. A six sigma process is one in which

99.99966% of the products manufactured are statistically expected to be free of defects (3.4

defective parts/million), although, as discussed below, this defect level corresponds to only a

4.5 sigma level. Motorola set a goal of "six sigma" for all of its manufacturing operations,

and this goal became a by-word for the management and engineering practices used to

achieve it.

Six Sigma doctrines assert that:

Continuous efforts to achieve stable and predictable process results (i.e., reduce

process variation) are of vital importance to business success.

[19]

Manufacturing and business processes have characteristics that can be measured,

analyzed, controlled and improved.

Achieving sustained quality improvement requires commitment from the entire

organization, particularly from top-level management.

Features that set Six Sigma apart from previous quality improvement initiatives

include:

A clear focus on achieving measurable and quantifiable financial returns from any Six

Sigma project.

An increased emphasis on strong and passionate management leadership and support.

A clear commitment to making decisions on the basis of verifiable data and statistical

methods, rather than assumptions and guesswork.

The term "six sigma" comes from statistics and is used in statistical quality control,

which evaluates process capability. Originally, it referred to the ability of manufacturing

processes to produce a very high proportion of output within specification. Processes that

operate with "six sigma quality" over the short term are assumed to produce long-term defect

levels below 3.4 defects per million opportunities (DPMO). Six Sigma's implicit goal is to

improve all processes, but not to the 3.4 DPMO level necessarily. Organizations need to

determine an appropriate sigma level for each of their most important processes and strive to

achieve these. As a result of this goal, it is incumbent on management of the organization to

prioritize areas of improvement.

"Six Sigma" was registered June 11, 1991 as U.S. Service Mark 74,026,418. In 2005

Motorola attributed over US$17 billion in savings to Six Sigma. Other early adopters of Six

Sigma who achieved well-publicized success include Honeywell (previously known

as AlliedSignal) and General Electric, where Jack Welch introduced the method.[8] By the

late 1990s, about two-thirds of the Fortune 500 organizations had begun Six Sigma initiatives

with the aim of reducing costs and improving quality.[9]

In recent years, some practitioners have combined Six Sigma ideas with lean

manufacturing to create a methodology named Lean Six Sigma.[10] The Lean Six Sigma

methodology views lean manufacturing, which addresses process flow and waste issues, and

Six Sigma, with its focus on variation and design, as complementary disciplines aimed at

promoting "business and operational excellence".[10] Companies such as GE,[11] Verizon,

GENPACT, and IBM use Lean Six Sigma to focus transformation efforts not just on

efficiency but also on growth. It serves as a foundation for innovation throughout the

organization, from manufacturing and software development to sales and service delivery

functions.

The International Organization for Standardization (ISO) has published ISO

13053:2011 defining the six sigma process.

4.2. Six Sigma Methodology : - Six Sigma projects follow two project methodologies inspired by Deming's Plan-Do-

Check-Act Cycle. These methodologies, composed of five phases each, bear the acronyms

DMAIC and DMADV.

[20]

DMAIC is used for projects aimed at improving an existing business process.

DMADV is used for projects aimed at creating new product or process designs.

The DMAIC project methodology has five phases:

Define the system, the voice of the customer and their requirements, and the project

goals, specifically.

Measure key aspects of the current process and collect relevant data.

Analyze the data to investigate and verify cause-and-effect relationships. Determine

what the relationships are, and attempt to ensure that all factors have been considered.

Seek out root cause of the defect under investigation.

Improve or optimize the current process based upon data analysis using techniques

such as design of experiments, poka yoke or mistake proofing, and standard work to

create a new, future state process. Set up pilot runs to establish process capability.

Control the future state process to ensure that any deviations from target are corrected

before they result in defects. Implement control systems such as statistical process

control, production boards, visual workplaces, and continuously monitor the process.

Define: - The purpose of this step is to clearly articulate the business problem, goal, potential

resources, project scope and high-level project timeline. This information is typically

captured within project charter document. Write down what you currently know. Seek to

clarify facts, set objectives and form the project team. Define the following:

A problem

The customer(s)

Voice of the customer (VOC) and Critical to Quality (CTQs) — what are the critical

process outputs?

The target process subject to DMAIC and other related business processes

Project targets or goal

Project boundaries or scope

A project charter is often created and agreed upon during the Define step.

Measure: - The purpose of this step is to objectively establish current baselines as the basis

for improvement. This is a data collection step, the purpose of which is to establish process

performance baselines. The performance metric baseline(s) from the Measure phase will be

compared to the performance metric at the conclusion of the project to determine objectively

whether significant improvement has been made. The team decides on what should be

measured and how to measure it. It is usual for teams to invest a lot of effort into assessing

the suitability of the proposed measurement systems. Good data is at the heart of the DMAIC

process:

Identify the gap between current and required performance.

Collect data to create a process performance capability baseline for the project metric,

that is, the process Y(s) (there may be more than one output).

Assess the measurement system (for example, a gauge study) for adequate accuracy

and precision.

Establish a high level process flow baseline. Additional detail can be filled in later.

Analyze: - The purpose of this step is to identify, validate and select root cause for

elimination. A large number of potential root causes (process inputs, X) of the project

[21]

problem are identified via root cause analysis (for example a fishbone diagram). The top 3-4

potential root causes are selected using multi-voting or other consensus tool for further

validation. A data collection plan is created and data are collected to establish the relative

contribution of each root causes to the project metric, Y. This process is repeated until "valid"

root causes can be identified. Within Six Sigma, often complex analysis tools are used.

However, it is acceptable to use basic tools if these are appropriate. Of the "validated" root

causes, all or some can be

List and prioritize potential causes of the problem

Prioritize the root causes (key process inputs) to pursue in the Improve step

Identify how the process inputs (Xs) affect the process outputs (Ys). Data is analyzed

to understand the magnitude of contribution of each root cause, X, to the project

metric, Y. Statistical tests using p-values accompanied by Histograms, Pareto charts,

and line plots are often used to do this.

Detailed process maps can be created to help pin-point where in the process the root

causes reside, and what might be contributing to the occurrence.

Improve: - The purpose of this step is to identify, test and implement a solution to the

problem; in part or in whole. Identify creative solutions to eliminate the key root causes in

order to fix and prevent process problems. Use brainstorming or techniques like Six Thinking

Hats and Random Word. Some projects can utilize complex analysis tools like DOE (Design

of Experiments), but try to focus on obvious solutions if these are apparent.

Create innovative solutions

Focus on the simplest and easiest solutions

Test solutions using Plan-Do-Check-Act (PDCA) cycle

Based on PDCA results, attempt to anticipate any avoidable risks associated with the

"improvement" using FMEA

Create a detailed implementation plan

Deploy improvements

Control: - The purpose of this step is to sustain the gains. Monitor the improvements to

ensure continued and sustainable success. Create a control plan. Update documents, business

process and training records as required. A Control chart can be useful during the Control

stage to assess the stability of the improvements over time by serving as a guide to continue

monitoring the process and provide a response plan for each of the measures being monitored

in case the process becomes unstable.

[22]

4.3. Six Sigma Roadmap: -

SIX SIGMA ROADMAP Define Measure Analyze Improve Control

Project Charter

Process Map

Rolled Throughput Yield

Voice of Customer

Value Stream Map

Cause and Effect Matrix

Potential Failure Mode And Effect Analysis

Measurement System Analysis

Data Collection and Sampling

Statistical Process control

Capability Study

Multi-Vari Study

Hypothesis Testing/ Confidence Intervals

Design of Experiment

Control Plan

Celebrate

4.4. Roles In Six Sigma

The project team forms the core of six sigma. Team members are typically not

trained in team sigma approach and tools. However, they have good knowledge of the

processes involved. The teams are often cross-functional and cross- organizational.

Depending on the complexity of the project, a black belt or green belt leads the team

and guides it through the six sigma methodology. The belts are thoroughly trained in the six

sigma tools and approach.

A BLACK BELT is a full –time committed project leader. He /she can work on

projects throughout the company. Black belt assignments last between 2 to 3 years. After the

assignment the black belts returns to new position in the line organization. Taking with them

the knowledge gained during their six sigma project.

A GREEN BELT is a part time committed project leader who also maintains his / her

regular responsibilities .the green belt can also participate in projects as a team member . a

green belt typically works on projects within their own area. There are many green belts than

black belts.

[23]

THE PROJECT SPONSOR is the prime mover behind the project and is ultimately

responsible for the project success. This person is usually a manager who wants a specific

problem solved. The project sponsor supports the black and green belt in the team and can

allocate resource to the project. They monitor and review the project status and achievements.

THE PROCESS OWNER is responsible for the design of the specific process

when changes are suggested the process owner is the one to decide if and how this should be

carried out he/ she provides expertise and support to the project team. The process owner has

the authority and the holistic overview of the process to minimize the risk of sub

optimizations.

THE DEVELOPMENT CHAMPION leads the implementation and raises

awareness of six sigma in the organization he/ she coordinates’ and supports the training

programs and proposes candidates for training to become belts.

4.5 Project characteristics

The six sigma projects are:-

About making improvements and solving business problems where the root causes is

unknown and that we have not been able to solve before

Driven b y customer focus and business results

Linked directly to the strategic and operational objectives of organization

Fact- based

Cl+early defined and limited in scope

Time limited to 4-6 months for black belt projects and 3-4 months for green belt

projects.

Led by black belts who are full time committed specialist in six sigma tools and

approach, or green who are part time committed

Selected by the steering group at division/ business unit 0r equivalent

4.6. Six Sigma Tools :

Six Sigma methodology believes on data. The raw data is processed statistically in form of

charts and is then interpreted. Important Six sigma tools are as follows

4.6.1. Control Charts

Control charts are one of the most commonly used methods of Statistical Process

Control (SPC), which monitors the stability of a process. The main features of a control chart

include the data points, a centreline (mean value), and upper and lower limits (bounds to

indicate where a process output is considered "out of control").They visually display the

fluctuations of a particular process variable, such as temperature, in a way that lets the

engineer easily determine whether these variations fall within the specified process limits.

Control charts are also known as Shewhart charts after Walter Shewhart, who developed

them in the early 1900’s.

[24]

The main purpose of using a control chart is to monitor, control, and improve process

performance over time by studying variation and its source. There are several functions of a

control chart:

1. It centers attention on detecting and monitoring process variation over time.

2. It provides a tool for ongoing control of a process.

3. It differentiates special from common causes of variation in order to be a guide for local or

management action.

4. It helps improve a process to perform consistently and predictably to achieve higher

quality, lower cost, and higher effective capacity.

5. It serves as a common language for discussing process performance.

A process may either be classified as in control or out of control. The boundaries for

these classifications are set by calculating the mean, standard deviation, and range of a set of

process data collected when the process is under stable operation. Then, subsequent data can

be compared to this already calculated mean, standard deviation and range to determine

whether the new data fall within acceptable bounds. For good and safe control, subsequent

data collected should fall within three standard deviations of the mean. Control charts build

on this basic idea of statistical analysis by plotting the mean or range of subsequent data

against time. For example, if an engineer knows the mean (grand average) value, standard

deviation, and range of a process, this information can be displayed as a bell curve, or

population density function (PDF).

a.) I-MR Charts

The I-MR chart, as used in statistics and fields using applied statistics, serves its

purpose in analyzing a specific time-reliant process. The I-MR chart usually takes the form of

two charts put together, sometimes superimposed.

The first part of the chart - the “I” part shows the individual data points, whereas the

second part of the chart the “MR” part shows how much the data points vary per unit of time.

Understanding the I-MR chart is necessary when looking for patterns in the two charts.

Interpreting The I-MR Charts

The Individual range charts

1. Observe the data points with respect to the centre, horizontal line. This centre line, the

mean, should lie roughly in the middle of all the data points. If it does not, this implies that

the data is roughly biased in one direction, with a few outliers in the other direction. If this

is the case, then it is possible that the observations for certain times are special cases, and

should be individually analyzed.

2. Check if any data points lie outside the standard deviation borders. The horizontal lines at

the top and bottom of the “I” chart represent the numerical values three standard deviations

from the mean in the positive and negative directions, respectively. If any data points lie

above the top horizontal line or below the lower horizontal line, then they are definitely

special cases and should be individually analyzed.

3. Note the overall pattern of the “I” chart. It should appear random. If the chart does not

appear random if there is a pattern such as a linear or parabolic pattern then the individual

[25]

data points are time dependent, which means that the process being observed is not entirely

random.

4. Observes the spikes in the chart, These spikes indicated special causes in the process

5. Observe the pattern in the charts. it must be random neither incremental nor drecremental

The Moving Range Chart

6. Observe the data points with respect to the centre, horizontal line. This centre line, the

mean, should lie roughly in the middle of all the data points. If it does not, this implies that

there are occasionally large spikes in the values. These spikes may indicate sudden

improvements or deteriorations in the process. If you find such points, you should observe

the differences between those data points that correspond to the data points in the “MR”

chart.

7. Check if any data points lie outside the standard deviation borders. The horizontal lines at

the top and bottom of the “MR” chart represent the numerical values three standard

deviations from the mean in the positive and negative directions, respectively. Finding one

or two data points outside these lines may not indicate anything. However, if you find many

points outside these lines, there is a strong possibility that the process being observed is

unstable.

8. Note the overall pattern of the “MR” chart. It should appear random. If the chart does not

appear random if there is a pattern such as a linear or parabolic pattern then the differences

between measurements are time dependent, which means that the process being observed is

not entirely random. Usually a pattern in the “I” chart appears simultaneously with a pattern

in the “MR” chart.

b.) X-bar R Chats

As with the and s and individuals control charts, the chart is only valid if the

within-sample variability is constant. Thus, the R chart is examined before the chart; if the R

chart indicates the sample variability is in statistical control, then the chart is examined to

determine if the sample mean is also in statistical control. If on the other hand, the sample

variability is not in statistical control, then the entire process is judged to be not in statistical

control regardless of what the chart indicates.

4.6.2. Process Indices

The Process Capability is a measurable property of a process to the specification,

expressed as a process capability index or as a process performance index. The output of this

measurement is usually illustrated by a histogram and calculations that predict how many

parts will be produced out of specification (OOS). The process capability measures how well

the process performs to meet given specified outcome. It indicates the conformance of a

process to meet given requirements or specification. Capability analysis helps to better

understand the performance of the process with respect to meeting customers’ specifications

and identify the process improvement opportunities. Process capability can be presented

using various indices depending on the nature of the process and the goal of the analysis

[26]

Popular process capability indices:

•Cp- Process Capability Cp measures the process’s potential capability to meet the two-sided specifications. It doesn’t

take the process average into consideration. High Cp indicates the small spread of the process

with respect to the spread of the customer specifications. Cp is recommended when the

process is centered between the specification limits. It works when there are both upper and

lower specification limits.

•Cpk - Process Capability Index Cpk measures the process’s actual capability by taking both the variation and average of the

process into consideration. The process does not need to be centered between the

specification limits to make the index meaningful. Cpk is recommended when the process is

not in the center between the specification limits. When there is only a one-sided limit, Cpk is

calculated using Cpu or Cpl.

•Pp - Process Performance Pp measures the capability of the process to meet the two-sided specifications. It only focuses

on the spread and does not take the process centralization into consideration. It is

recommended when the process is centered between the specification limits. Cp considers the

within-subgroup standard deviation and Pp considers the total standard deviation from the

sample data.It works when there are both upper and lower specification limits.

•Ppk -- Process Performance Index Ppk measures the process capability by taking both the variation and the average of the

process into consideration. It solves the decentralization problem Pp cannot overcome. Cpk

considers the within-subgroup standard deviation, while Ppk considers the total standard

deviation from the sample data. When there is only a one-sided specification limit, Ppk is

calculated using Ppu or Ppl.

4.6.3 Design of experiments via Taguchi methods: orthogonal arrays

Introduction

The Taguchi method involves reducing the variation in a process through robust

design of experiments. The overall objective of the method is to produce high quality product

at low cost to the manufacturer. The Taguchi method was developed by Dr. Genichi Taguchi

of Japan who maintained that variation. Taguchi developed a method for designing

experiments to investigate how different parameters affect the mean and variance of a process

performance characteristic that defines how well the process is functioning. The experimental

design proposed by Taguchi involves using orthogonal arrays to organize the parameters

affecting the process and the levels at which they should be varies. Instead of having to test

all possible combinations like the factorial design, the Taguchi method tests pairs of

combinations. This allows for the collection of the necessary data to determine which factors

most affect product quality with a minimum amount of experimentation, thus saving time and

[27]

resources. The Taguchi method is best used when there is an intermediate number of

variables (3 to 50), few interactions between variables, and when only a few variables

contribute significantly.

The Taguchi arrays can be derived or looked up. Small arrays can be drawn out

manually; large arrays can be derived from deterministic algorithms. Generally, arrays can be

found online. The arrays are selected by the number of parameters (variables) and the number

of levels (states). This is further explained later in this article. Analysis of variance on the

collected data from the Taguchi design of experiments can be used to select new parameter

values to optimize the performance characteristic. The data from the arrays can be analyzed

by plotting the data and performing a visual analysis, ANOVA, bin yield and Fisher's exact

test, or Chi-squared test to test significance.

Philosophy of the Taguchi Method

1. Quality should be designed into a product, not inspected into it. Quality is designed

into a process through system design, parameter design, and tolerance design. Parameter

design, which will be the focus of this article, is performed by determining what process

parameters most affect the product and then designing them to give a specified target quality

of product. Quality "inspected into" a product means that the product is produced at random

quality levels and those too far from the mean are simply thrown out.

2. Quality is best achieved by minimizing the deviation from a target. The product

should be designed so that it is immune to uncontrollable environmental factors. In other

words, the signal (product quality) to noise (uncontrollable factors) ratio should be high.

3. The cost of quality should be measured as a function of deviation from the standard

and the losses should be measured system wide. This is the concept of the loss function, or

the overall loss incurred upon the customer and society from a product of poor quality.

Because the producer is also a member of society and because customer dissatisfaction will

discourage future patronage, this cost to customer and society will come back to the producer.

Taguchi Method Design of Experiments

The general steps involved in the Taguchi Method are as follows:

1. Define the process objective, or more specifically, a target value for a performance

measure of the process. This may be a flow rate, temperature, etc. The target of a process

may also be a minimum or maximum; for example, the goal may be to maximize the output

flow rate. The deviation in the performance characteristic from the target value is used to

define the loss function for the process.

2. Determine the design parameters affecting the process. Parameters are variables within the

process that affect the performance measure such as temperatures, pressures, etc. that can be

easily controlled. The number of levels that the parameters should be varied at must be

specified. For example, a temperature might be varied to a low and high value of 40 C and 80

[28]

C. Increasing the number of levels to vary a parameter at increases the number of

experiments to be conducted.

3. Create orthogonal arrays for the parameter design indicating the number of and conditions

for each experiment. The selection of orthogonal arrays is based on the number of parameters

and the levels of variation for each parameter, and will be expounded below.

4. Conduct the experiments indicated in the completed array to collect data on the effect on

the performance measure.

5. Complete data analysis to determine the effect of the different parameters on the

performance measure.

See below for a pictorial depiction of these and additional possible steps, depending on the

complexity of the analysis.

[29]

Taguchi Loss Function

The goal of the Taguchi method is to reduce costs to the manufacturer and to society

from variability in manufacturing processes. Taguchi defines the difference between the

target value of the performance characteristic of a process, τ, and the measured value, y, as a

loss function as shown below.

The constant, kc, in the loss function can be determined by considering the specification

limits or the acceptable interval, delta.

The difficulty in determining kc is that τ and C are sometimes difficult to define.

If the goal is for the performance characteristic value to be minimized, the loss function is

defined as follows:

If the goal is for the performance characteristic value to maximized, the loss function is

defined as follows:

The loss functions described here are the loss to a customer from one product. By computing

these loss functions, the overall loss to society can also be calculated.

Determining Parameter Design Orthogonal Array

The effect of many different parameters on the performance characteristic in a

condensed set of experiments can be examined by using the orthogonal array experimental

design proposed by Taguchi. Once the parameters affecting a process that can be controlled

have been determined, the levels at which these parameters should be varied must be

determined. Determining what levels of a variable to test requires an in-depth understanding

of the process, including the minimum, maximum, and current value of the parameter. If the

difference between the minimum and maximum value of a parameter is large, the values

being tested can be further apart or more values can be tested. If the range of a parameter is

small, then less values can be tested or the values tested can be closer together. For example,

if the temperature of a reactor jacket can be varied between 20 and 80 degrees C and it is

known that the current operating jacket temperature is 50 degrees C, three levels might be

chosen at 20, 50, and 80 degrees C. Also, the cost of conducting experiments must be

considered when determining the number of levels of a parameter to include in the

[30]

experimental design. In the previous example of jacket temperature, it would be cost

prohibitive to do 60 levels at 1 degree intervals. Typically, the number of levels for all

parameters in the experimental design is chosen to be the same to aid in the selection of the

proper orthogonal array.

Knowing the number of parameters and the number of levels, the proper orthogonal

array can be selected. Using the array selector table shown below, the name of the

appropriate array can be found by looking at the column and row corresponding to the

number of parameters and number of levels. Once the name has been determined (the

subscript represents the number of experiments that must be completed), the predefined array

can be looked up. Links are provided to many of the predefined arrays given in the array

selector table. These arrays were created using an algorithm Taguchi developed, and allows

for each variable and setting to be tested equally. For example, if we have three parameters

(voltage, temperature, pressure) and two levels (high, low), it can be seen the proper array is

L4. Clicking on the link L4 to view the L4 array, it can be seen four different experiments are

given in the array. The levels designated as 1, 2, 3 etc. should be replaced in the array with

the actual level values to be varied and P1, P2, P3 should be replaced with the actual

parameters (i.e. voltage, temperature, etc.)

Array Selector

Important Notes Regarding Selection + Use of Orthogonal Arrays

Note 1

The array selector assumes that each parameter has the same number of levels.

Sometimes this is not the case. Generally, the highest value will be taken or the difference

will be split.

[31]

The following examples offer insight on choosing and properly using an orthogonal array.

Examples 1 and 2 focus on array choice, while Example 3 will demonstrate how to use an

orthogonal array in one of these situations.

Example 1:

# parameter: A, B, C, D = 4

# levels: 3, 3, 3, 2 = ~3

array: L9

Example 2:

# parameter: A, B, C, D, E, F = 6

# levels: 4, 5, 3, 2, 2, 2 = ~3

array: modified L16

Example 3:

A reactor's behavior is dependent upon impeller model, mixer speed, the control algorithm

employed, and the cooling water valve type. The possible values for each are as follows:

Impeller model: A, B, or C

Mixer speed: 300, 350, or 400 RPM

Control algorithm: PID, PI, or P

Valve type: butterfly or globe

There are 4 parameters, and each one has 3 levels with the exception of valve type. The

highest number of levels is 3, so we will use a value of 3 when choosing our orthogonal

array.

Using the array selector above, we find that the appropriate orthogonal array is L9:

[32]

When we replace P1, P2, P3, and P4 with our parameters and begin filling in the parameter

values, we find that the L9 array includes 3 levels for valve type, while our system only has

2. The appropriate strategy is to fill in the entries for P4=3 with 1 or 2 in a random, balanced

way. For example:

Here, the third value was chosen twice as butterfly and once as global.

Note 2

If the array selected based on the number of parameters and levels includes more parameters

than are used in the experimental design, ignore the additional parameter columns. For

example, if a process has 8 parameters with 2 levels each, the L12 array should be selected

according to the array selector. As can be seen below, the L12 Array has columns for 11

parameters (P1-P11). The right 3 columns should be ignored.

Analyzing Experimental Data

Once the experimental design has been determined and the trials have been carried

out, the measured performance characteristic from each trial can be used to analyze the

relative effect of the different parameters. To demonstrate the data analysis procedure, the

following L9 array will be used, but the principles can be transferred to any type of array.

[33]

In this array, it can be seen that any number of repeated observations (trials) may be

used. Ti,j represents the different trials with i = experiment number and j = trial number. It

should be noted that the Taguchi method allows for the use of a noise matrix including

external factors affecting the process outcome rather than repeated trials, but this is outside of

the scope of this article.

To determine the effect each variable has on the output, the signal-to-noise ratio, or

the SN number, needs to be calculated for each experiment conducted. The calculation of the

SN for the first experiment in the array above is shown below for the case of a specific target

value of the performance characteristic. In the equations below, yi is the mean value and si is

the variance. yi is the value of the performance characteristic for a given experiment.

[34]

For the case of minimizing the performance characteristic, the following definition of the SN

ratio should be calculated:

For the case of maximizing the performance characteristic, the following definition of the SN

ratio should be calculated:

After calculating the SN ratio for each experiment, the average SN value is calculated for

each factor and level. This is done as shown below for Parameter 3 (P3) in the array:

Once these SN ratio values are calculated for each factor and level, they are tabulated as

shown below and the range R (R = high SN - low SN)of the SN for each parameter is

calculated and entered into the table. The larger the R value for a parameter, the larger the

effect the variable has on the process. This is because the same change in signal causes a

larger effect on the output variable being measured.

[35]

Please refer to the Worked Out Example for a numeric example of how the data analysis

procedure described here is applied.

Advantages and Disadvantages

An advantage of the Taguchi method is that it emphasizes a mean performance

characteristic value close to the target value rather than a value within certain specification

limits, thus improving the product quality. Additionally, Taguchi's method for experimental

design is straightforward and easy to apply to many engineering situations, making it a

powerful yet simple tool. It can be used to quickly narrow down the scope of a research

project or to identify problems in a manufacturing process from data already in existence.

Also, the Taguchi method allows for the analysis of many different parameters without a

prohibitively high amount of experimentation. For example, a process with 8 variables, each

with 3 states, would require 6561 (38) experiments to test all variables. However using

Taguchi's orthogonal arrays, only 18 experiments are necessary, or less than .3% of the

original number of experiments. In this way, it allows for the identification of key parameters

that have the most effect on the performance characteristic value so that further

experimentation on these parameters can be performed and the parameters that have little

effect can be ignored.

The main disadvantage of the Taguchi method is that the results obtained are only

relative and do not exactly indicate what parameter has the highest effect on the performance

characteristic value. Also, since orthogonal arrays do not test all variable combinations, this

method should not be used with all relationships between all variables are needed. The

Taguchi method has been criticized in the literature for difficulty in accounting for

interactions between parameters. Another limitation is that the Taguchi methods are offline,

and therefore inappropriate for a dynamically changing process such as a simulation study.

Furthermore, since Taguchi methods deal with designing quality in rather than correcting for

poor quality, they are applied most effectively at early stages of process development. After

design variables are specified, use of experimental design may be less cost effective.

4.6.4 Pareto analysis

Pareto analysis is a formal technique useful where many possible courses of action

are competing for attention. In essence, the problem-solver estimates the benefit delivered by

each action, then selects a number of the most effective actions that deliver a total benefit

reasonably close to the maximal possible one.

[36]

Pareto analysis is a creative way of looking at causes of problems because it helps

stimulate thinking and organize thoughts. However, it can be limited by its exclusion of

possibly important problems which may be small initially, but which grow with time. It

should be combined with other analytical tools such as failure mode and effects

analysis and fault tree analysis for example.

This technique helps to identify the top portion of causes that need to be addressed to

resolve the majority of problems. Once the predominant causes are identified, then tools like

the Ishikawa diagram or Fish-bone Analysis can be used to identify the root causes of the

problems. While it is common to refer to pareto as "80/20" rule, under the assumption that, in

all situations, 20% of causes determine 80% of problems, this ratio is merely a convenient

rule of thumb and is not nor should it be considered immutable law of nature.

The application of the Pareto analysis in risk management allows management to

focus on those risks that have the most impact on the project.

Fig : - paret

[37]

CHAPTER 5 : PROJECT

PROCESS CAPABILITY IMPROVEMENT ON SSA/557

MACHINE

5.1 The Problem Statement

Problem statement of my project is Process Capability Improvement on SSA/ 557

machine. The machine is Outer Ring Groove Grinding machine on DGBB channel 5. DGBB

Channel 5 is most costly channel At Pune SKF w-ith high customer demand and prestigious

Customers such as TAFE, John Deree, Bharat Bijlee, Laxmi Hydraulics And Escorts

Tractors. The major issues with the machine are high size variation on output. Other issues

are burning on rings with inconsistent quality.

The functional requirement of a deep groove ball Bearing is that the three basic parts

of a Deep Groove Ball Bearing i.e. inner race, outer race and the balls must attain close

interference fit. In Order to fulfill this requirement the Balls at ball filling station are made

available in 9 different diameter in Range of – 8,-6,-4,-2 ,0, 2, 4, 6, 8 um than the boundary

ball dimensions. Yet the size variation is so high that cause either frequent rework or scrap as

ring fails to fulfill its functional requirement for the bearing. This also causes great gambling

in ball filling operations.

Thus reducing the size variation of the Groove Grinding process will also reduce

rework quantity as well as scrap. It will also reduce gambling in ball filling operations.

5.2 Understanding the Process

The OR Groove Grinding operation is done on heat treated i.e. hard rings. This

operation is intermediate to face and OD grinding and Honing (super finishing Process). The

Machining alliances are generally in range of 200um to 80um.

Fig : OR Groove Grinding

The important output parameters required in this operation are:-

i. Proper size

ii. Minimum Ovality

iii. Perfect groove form

iv. Good surface finish

v. Grinding burns within limits (VKL)

[38]

The Grinding Cycle:-

Fig :- The Grinding Process

The Grinding machine has a cross slide with the workpeice chucking mounted on it.

The grinding wheel is Mounted on grinding spindle. The infeed of the cutting tool is

controlled by movement of cross slide. The Process for a ring can be understood by grinding

cycle. The Grinding Cycle can split up into factors for detailed explanation as follows.

1. Workpeice Chucking:- The workpeice i.e. Outter Ring is mounted on an shoe chuck of a

cross Slide of a machine with reference to its face and OD using pneumatic jacks.

2. The Grinding Spindle moves in the chuck. The Position of the Groove is controlled by this

movement of grinding spindle along its machine bed

3. Air Grinding:- this is the part of grinding cycle eventually where no grinding takes place

i.e. the ideal tool travel of cutting tool just before it is about to touch the workpeice. Hence

infeed is kept maximum possible i.e. in range of (100um/sec to 150um/sec) to avoid the

unproductive cycle time.

4. Rough Grinding:- The actual grinding process starts here. The infeed is lowered to avoid

jumping of Workpeice in chucks just before the tool makes contact with ring. the feed Rate is

in Range of 20um/sec to 30um/sec. initially In primary stage the ovality and burs within the

ring is removed and then ring is grinded to maintain its Groove profile

5. Fine Grinding:- Fine Grinding is done to maintain Surface Finish of the rings as prescribed

by VKR limits. the Feed rates are lowered again in range of8um/sec to 12um/sec

[39]

6. Sparkout- This is a detachment time parameter provided to avoid steps and Burns in

Grinded Rings

7. Declamping and ejection

8. Dressing :- Dressing is an activity apart from grinding cycle. Dressing of Grinding wheel

is done after a particular no. of rings are grinded and the dressing interval depends on cutting

parameters and size of a ring. This dressing helps to regain the cutting geometry of the

grinding wheel which if delayed ir not done would cause only rubbing action between the

tool and workpeice and eventfully make the grinding wheel blunt. Dressing is accompanied

with help of a single point diamond Dresser operated by a hydraulic mechanism tht generates

the core profile of the Groove On Grinding wheel upto certain depth(generally 10um to

20um) in Presence of Proper Coolant.

Figure :- Grinding cycle

Factors Affecting Grindings Operation

[40]

5.3. Grinding Process parameters

the Grinding process parameter areas follows

R Value As per New DOE

R value Setup- Data Description Value

R101 GRINDING STARTING POSITION 375

R102 GAP ELEMINATOR SAFETY POSITION 300

R104 SIZEMATIC KNOCK-OFF 1 POSITION 80

R110 INCREMENTAL RETREAT1 0

R111 INCREMENTAL RETREAT2 0

R115 DRESSING COMPENSATION 12

R117 GRINDING COMPENSATION 5

R124 ROUGH 1 GRINIDING DISTANCE 60

R127 AIR GRIND FEED RATE 150

R128 ROUGH-1 FEED RATE 30

R129 ROUGH-2 FEED RATE 20

R130 FINE FEED RATE 10

R131 SPARK OUT FEED RATE 3

R136 SPARK OUT TIME 2

R142 WORK HEAD RPM 450

R143 DRESSING INTERVAL 2

R144 GRINDING COMPENSATION INTERVAL 6

R153 GRINDING WHEEL SPEED 4500

R117 Grinding compensation. 5

R144 Grinding compensation Interval 6

The other important parameters are

Grinding Wheel Cutting Speed= pie*D*N/60………………….not to Exceed 60m/sec

where D- Dia Of Gringing Wheel

N- Actual Rpm Of grinding Wheel

Workhead Cutting Speed= Pie *D' *N'/60

Where D'- Dia of Outter Ring

N'- actual RPm of work head spinde

Q ratio= Grinding Wheel Cutting Speed/ Workhead Cutting Speed

…………………………………………not to exceed 25

[41]

5.4. Monitoring the process

The grinding parameter to be controlled to improve the process capability is the OR Groove

diameter. The parameter Recorded for the study is the deviation of the process beyony its boundry

diameter on both higher and lower sides. the Sample data collected is for 125 ring is shown Below.

this data is processed with help of MINITAB-16_to generate control charts and process capability

indices.

Date 18-01-14

DGBB Ch 5

M/C- SSA-557

Bearing Type- 6309

Dressing Interval 2

Dressing

Compensation 15 um

Operation : OR Grovee grinding

Remark

Readings with Grinding comensation 5 after Grinding compensation

interval 6

Study Conducted by:- Mohit Chitlange

Sr. No Min Max Average Dressing Remark

1 1 2 1.5 D

2 -5 -3 -4

3 1 2 1.5 D

4 -1 0 -0.5

5 0 2 1 D

6 -2 0 -1

7 3 4 3.5 D

8 0 1 0.5

9 5 6 5.5 D

10 0 3 1.5

11 6 7 6.5 D

12 2 3 2.5

13 6 8 7 D

14 5 7 6

15 9 10 9.5 D

16 6 7 6.5

17 10 11 10.5 D

18 7 8 7.5

19 18 20 19 D

20 10 12 11

21 15 17 16 D

22 10 11 10.5

[42]

23 12 13 12.5 D

24 8 10 9

25 5 6 5.5 D

26 4 5 4.5

27 10 11 10.5 D

28 3 5 4

29 15 17 16 D

30 13 14 13.5

31 5 7 6 D

32 3 4 3.5

33 12 13 12.5 D

34 10 12 11

35 11 12 11.5 D

36 5 6 5.5

37 14 15 14.5 D

38 9 10 9.5

39 15 16 15.5 D

40 12 13 12.5

41 18 19 18.5 D

42 19 20 19.5

43 22 23 22.5 D

44 17 18 17.5

45 25 27 26 D

46 18 20 19

47 23 25 24 D

48 12 15 13.5

49 13 15 14 D

50 16 17 16.5

51 10 12 11 D

52 8 10 9

53 20 21 20.5 D

54 16 17 16.5

55 17 18 17.5 D

56 13 15 14

57 13 15 14 D

58 15 16 15.5

59 23 24 23.5 D

60 17 18 17.5

61 20 22 21 D

62 15 17 16

63 21 23 22 D

64 17 18 17.5

[43]

65 18 20 19 D

66 15 16 15.5

67 23 24 23.5 D

68 24 25 24.5

69 21 23 22 D

70 15 16 15.5

71 25 26 25.5 D

72 20 21 20.5

73 29 30 29.5 D

74 21 23 22

75 29 30 29.5 D

76 21 22 21.5

77 20 22 21 D

78 15 16 15.5

79 18 20 19 D

80 16 17 16.5

81 23 24 23.5 D

82 17 19 18

83 20 22 21 D

84 16 17 16.5

85 23 24 23.5 D

86 16 17 16.5

87 20 22 21 D

88 15 16 15.5

89 18 20 19 D

90 13 14 13.5

91 13 14 13.5 D

92 18 20 19

93 20 22 21 D

94 15 17 16

95 20 22 21 D

96 17 19 18

97 22 23 22.5 D

98 16 17 16.5

99 27 29 28 D

100 16 18 17

101 18 19 18.5 D

102 12 13 12.5

103 18 20 19 D

104 15 16 15.5

105 13 15 14 D

106 25 26 25.5

[44]

107 20 21 20.5 D

108 24 25 24.5

109 20 22 21 D

110 20 23 21.5

111 24 25 24.5 D

112 20 22 21

113 24 25 24.5 D

114 18 20 19

115 15 17 16 D

116 18 19 18.5

117 18 19 18.5 D

118 24 25 24.5

119 22 24 23 D

120 28 30 29

121 24 26 25 D

122 27 29 28

123 22 23 22.5 D

124 18 20 19

125 21 23 22 D

Fig :- I-MR Chart

12110997857361493725131

30

20

10

0

Observation

Indi

vid

ual

Valu

e

_X=15.80

UCL=28.12

LCL=3.47

12110997857361493725131

15

10

5

0

Observation

Mov

ing R

ange

__MR=4.63

UCL=15.14

LCL=0

111

11

11

11

1

1

1

Ch.05, SSA 557, Type: 6309, Date:18-01-14Base line Readings with GC=5 & GCI=6

Project: Minitab.MPJ; 5/20/2014

I-MR Variation=24.65 micronmin max difference=33.5 micron

[45]

Fig : Process Capability Bell Curves Diagram For Process Mapping

[46]

5.5 Process Mapping, Interpretations and Actions and Remedies

The Process Output was monitored almost after every event and type change over.

Control Charts generated where interpreted and root cause of variation was mined. actions

were taken. The Process capability indices are irrational to observe until and unless the

process operates in a normal way i.e. present normal data distribution. Hence all

Interpretations are made using IM-R Charts Only

The Short date wise summary of work is represented below :-

1. Date -16 Jan 2014

Control Chart

Process reading Conclusion

IM-R Chart Conclusion

Min Max Average

UCL 37.39

Min 6 8 7

LCL 17.54

Max 44 45 44.5

Variation 19.85

difference 38 37 37.5

-

Interpretations:- Special cause exixt due to sudden jump in IM-R pattern

Action: - Cross slide movement checked and found faulty. this was done using continuous

mapping for cross slide movement during grinding as well as dry run by mounting a digital

dial gauge on Cross slide. The Cross slide Showed disturbances in its Movement in Between

two constitutive dressings. The fault was reported to maintenance department and was

rectified.

[47]

2. Date :-28 Jan 2014

Control Chart

Process reading Conclusion

IM-R Chart Conclusion

Min Max Average

UCL 15.73

Min -10 -8 -9

LCL -4.82

Max 19 21 20

Variation 20.55

Difference 29 29 29

Interpretations:- The process found to Behave in a erratic pattern witnh more varitaion than

before. Special Causes still reported.

Action :- To provide process with grinding compensation and perform a RSM DOE to

determine optimum dressing Compensation and Interval. Fault reported to Manufacturing

Excellence dept.

[48]

3. Date: 18 Feb 2014

Control Chart

Process reading Conclusion

I-MR Chart Conclusion

Min Max Average

UCL 21.46

Min 4 5 4.5

LCL 10.78

Max 29 30 29.5

Variation 10.68

Difference 25 25 25

Interpretations:- Dressing variation now found much stable but process shows a steep

incremental pattern

Actions :- Machine Set For preventive maintenance and micro centric allignment. Machine

handed over to maintenance Dept.

[49]

4. Date: 1 March 2014

Control Charts

Prosses Reading Conclusion

IM-R Chart Conclusion

Min Max Average

UCL 6.26

Min -13 -12 -12.5

LCL -8.18

Max 12 13 12.5

Variation 14.44

Difference 25 25 25

Interpretations:- Process spread reduced but a same steep incremental pattern reported.

Actions:- Plan and Perform a Energy DOE on machine. Collect all data about machine

specification and process parameters of the machine

[50]

Setup

- Data

Descr

iption

Param

eters

AB

CD

EF

GH

IJ

KL

MN

OP

QR

SPARK

OUT T

IMER13

61

11

11

11

11

22

22

22

22

2AIR

GRIND

FEED R

ATER12

7125

125125

150150

150175

175175

125125

125150

150150

175175

175RO

UGH-1

FEED R

ATER12

812

1416

1214

1612

1416

1214

1612

1416

1214

16RO

UGH-2

FEED R

ATER12

911

1213

1112

1312

1311

1311

1212

1311

1311

12FIN

E FEED

RATE

R130

89

109

108

89

1010

89

108

99

108

SPARK

OUT FE

ED RAT

ER13

11

23

23

13

12

23

11

23

31

2

DRESSIN

G COM

PENSAT

IONR11

59

1215

159

1212

159

1215

915

912

912

15

GRIND

ING WH

EEL SP

EEDR15

3390

0400

0410

0410

0390

0400

0410

0390

0400

0390

0400

0410

0400

0410

0390

0400

0410

0390

0

AB

CD

EF

GH

IJ

KL

MN

OP

QR

Sr.No.

GAP E

LEMINA

TIONSIZE

MATIC

KNOC

K-

OFF 1 P

OSITIO

N

INCREM

ENTAL

RETREA

T1

INCREM

ENTAL

RETREA

T2

ROUG

H 1 GR

INIDING

DISTAN

CEWO

RK HEA

D RPM

DRESSIN

G

INTERV

AL

R102

R104

R110

R111

R124

R142

R143

1260

803

240

3001

26.17

23.73

22.03

25.37

22.30

22.57

24.47

22.87

24.67

23.83

26.77

24.43

25.00

23.87

25.43

23.53

25.97

24.03

2260

804

350

3502

25.70

23.80

21.50

25.97

23.30

22.67

24.43

22.63

24.07

24.03

26.60

24.87

25.27

24.00

25.23

24.40

25.63

23.70

3260

805

460

4003

26.27

24.13

21.53

26.10

23.60

22.47

24.97

21.80

23.83

23.93

27.10

24.07

25.10

23.03

26.03

24.50

26.43

24.10

4260

903

250

3503

26.33

24.10

22.30

26.03

23.47

23.10

24.27

22.70

23.93

23.97

27.03

24.80

24.90

24.73

25.33

24.87

24.97

25.00

5260

904

360

4001

25.27

23.13

21.43

24.97

22.80

21.37

23.07

21.80

24.23

23.23

26.03

22.83

24.73

22.67

28.40

23.93

25.73

25.10

6260

905

440

3002

26.53

24.50

22.03

26.37

23.73

23.07

25.27

22.70

24.40

24.27

27.37

25.10

24.43

24.67

26.20

23.67

25.07

25.63

7260

1003

340

4002

29.77

27.00

24.23

29.63

26.73

25.87

28.60

25.30

27.23

27.50

30.60

28.23

27.63

27.37

29.83

27.27

28.17

28.07

8260

1004

450

3003

30.40

27.07

25.30

28.47

25.43

26.63

27.87

26.40

28.10

27.17

30.17

27.90

29.67

27.83

29.37

27.80

28.90

28.33

9260

1005

260

3501

28.73

25.33

22.83

25.73

26.03

23.60

24.33

23.60

24.90

25.17

24.90

21.50

24.10

22.00

25.13

25.23

29.57

25.67

10290

803

460

3502

30.19

27.87

25.97

29.03

26.50

25.17

29.23

24.07

26.27

27.93

30.17

26.53

28.03

27.67

27.00

25.47

27.20

25.50

11290

804

240

4003

28.90

26.23

25.50

28.10

25.40

25.90

28.40

22.77

25.93

25.40

30.93

30.30

28.43

27.77

27.17

25.07

26.40

25.37

12290

805

350

3001

29.10

26.27

24.77

27.60

24.90

24.23

25.27

23.63

25.03

25.87

29.87

26.83

26.73

26.00

26.50

25.07

27.77

25.63

13290

903

360

3003

29.07

25.87

24.23

29.87

24.10

24.90

26.27

23.90

28.67

26.87

29.47

26.87

24.93

25.83

26.13

24.80

25.33

25.87

14290

904

440

3501

29.70

26.80

29.80

26.23

24.33

23.33

25.73

22.43

23.90

25.33

30.23

27.40

26.30

28.70

28.57

26.60

31.80

31.07

15290

905

250

4002

30.73

27.53

26.37

28.67

26.13

26.00

27.20

28.57

29.77

27.17

31.37

28.10

27.20

27.33

28.93

25.67

28.23

26.67

16290

1003

450

4001

33.50

30.40

28.60

32.87

29.53

28.80

31.80

27.63

29.77

30.40

35.13

31.80

31.50

29.93

30.80

29.67

31.37

33.40

17290

1004

260

3002

38.53

34.37

32.07

37.73

34.27

33.43

36.20

32.97

35.47

34.20

38.70

35.57

35.90

34.80

37.40

34.80

37.97

35.90

18290

1005

340

3503

38.70

35.20

32.30

40.83

34.60

33.70

36.40

33.33

36.50

34.20

39.20

35.93

36.10

35.00

38.10

34.63

37.83

36.60

PRACTI

CAL CY

CLE TIM

E

ENERG

Y DOE

FOR SSA

-557 m

achine

date 2

0-02-1

4. FOR

6313

Type

FEED RATE

FEED P

OSITIO

NS

[51]

5. Date : 24 March 2014 and 8 April 2014

Control Charts

Process reading conclusions

IM-R Chart Conclusions

Min Max Average

UCL 15.47

Min -13 -9 -11

LCL -14.1

Max 15 16 15.5

Variation 29.57

Difference 28 25 26.5

Interpratation :- As the dressing interval is increased the pattern shows decreased cutting.

This indicates more rubbing action and undercutting .

Action: - Dressing Interval Reduced and dressing Process mapped. The Dressing pressure

altered in low , medium and high range with support of maintenance Dept. Trail Taken with

Valves full Open, Half open, and low opening. Variation Found min with full opening i.e.

maximum pressure indication the wheel abrasive particles become sharp when open with

more pressure and speed.

[52]

6. Date 15 April 2014

Control Charts

Intrpretation: - Steep Incremental Patter indicates overcutting .

Action: - Grindiong Wheel Found to be faulty with this cutting Speed Cutting speeds and q

ratio o be optimised

Sr.No. Bearing Type

As per Chart Practical As per Chart Practical

1 Grinding Wheel RPM as per Set up Chart 10000 8474 9500 7658

2 Cutting speed at New Wheel(m/s) 42.39 35.921286 46.24 37.271486

3 Cutting speed at worn out wheel(m/s) 33.912 28.7370288 36.99 29.8171888

4 Work head RPM 350 450 350 400

5 Work head speed(m/s) 1.83 2.36 2.01 2.3

NEW WORNOUT NEW WORNOUT

7 Grinding Wheel Diameter(mm) 81 64.8 93 74.4

8 Outer Ring Diameter(mm)

9 Q-ratio 15.22 12.18 16.20 12.96

Cutting Speed As Per Set Up Chart & Actual , SSA-557, Ch-05 (OR groove Grinding)

6309 6212

110100

[53]

7. Date 19 April 2014, 30 April 2014 and 10 May 2014

Control Charts

[54]

Interpretation:- the pattern shows a steep incremental Patter

Action:- New Grinding wheel to be developed for the process as this wheel always overcuts

[55]

5.6 Grinding Wheel Development Activity

As per the above conclusion the grinding wheel was found incompatible with the

process the team began to work on idea of developing a new grinding wheel . The suppliers

were called on for the followup with the tean , Application and Manufactring Excellence

Departments.

The data needed for activity was to be supplied. lead time For wheel development was

2 months. we4 stated woring on Grinding Power Calcuations and further machine

Specification.

Table:- Sample workout For calculating Grinding power

GlosarryHEADINGS

INPUT VALUES

CALCULATED VALUES

Final Calculated Value

Grinding Wheel Specification

38A 120 L8 VBE TR22- GNL

Units Type 6012 6211 6212µm/s f = Radial feed rate (Rough1) 38 28 28

mm ds = Grinding Wheel Dia (mm) 81 85 93

m/s Vs = Cutting Speed (m/s) 60 60 60

rpm ns = Grinding Wheel rpm 14147.10605 13481.35989 12321.67301

mm de = OR Groove Dia 87.819 91.788 100.875

µm heq = Equivalent Grinding Thickness 0.174731299 0.134568236 0.147890474

N/mm F1= Specific Tangential Force at heq = 1 µm 24 24 24

d = Constant (0.6 < d < 0.9) 0.72 0.72 0.72

N/mm F't = F1 * (heq)d 6.834679443 5.66304334 6.061333594

mm dk = OR Land Dia 83.7 85.8 94.6

mm de = OR Groove Dia 87.819 91.788 100.875

mm r = Groove Radius 5.47 7.55 8.39

x = (De-Dk)/2 2.0595 2.994 3.1375

y = (r - x) / r 0.623491773 0.603443709 0.626042908

mm bri = raceway length = 2 * r * cos-1(y) 9.819693451 13.93705232 15.00682529

mm bd = Active Grinding Wheel Width (mm) 9.819693451 13.93705232 15.00682529

N Ft = F't * bd 67.11445697 78.92613134 90.96137428

kW P = Ft * Vs 4.026867418 4.735567881 5.457682457

[56]

CHAPTER 6: - CONCLUSION

The process capability of process is the measure of the correctness of the process to

deliver its output within a constraint of specified limits of the tolerance zone . The Process

Capability of a process adversesly affects the end term quality i.e. Customer satisfaction and

bad process capability makes the manufacutring process a threat for profits.

The process capability indices must never be considered until and unless the process

behaves in a normal way since the calculations assume data presented to be distributed in a

normal way. The objective should to to make the process stable within the process spread

variation below than the tolerance range.

The quest must be directed to dig out the root cause of a problem rather than healing it

by superficial Solutions

[57]

CHAPTER 7: - REFERENCES AND BIBLIOGRAPHY

1. http://www.skf.com/group/splash/index.html

2. http://en.wikipedia.org/wiki/SKF

3. http://en.wikipedia.org/wiki/SPC

4. http://www.goleansixsigma.com/wp-content/uploads/2012/02/The-Basics-of-Lean-Six-

Sigma-

5. www.GoLeanSixSigma.com_.pdf

6. http://www.ge.com/sixsigma/SixSigma.pdf

7. http://www.apo-tokyo.org/publications/files/ind-09-ss.pdf

8. An Introduction to Statistical Process Control

(By:- P. Lyonnet, Publisher: Springer-verlag Gmbh)

9. Design for Six Sigma Statistics, Chapter 6 - Measuring Process Capability

(By: - Sleeper, Andrew, Publisher: Mcgraw-hill)

10. The Six Sigma Handbook 3rd Edition

(By- Paul Keller, Thomas Pyzdek, Publisher: Tata McGraw - Hill Education)