SPC Project Report

A REPORT ON

STATISTICAL PROCESS CONTROL ON ROTARY COMPRESSOR BY

VISHWANI. M

04951A2144

ANAND BABU .D

04951A2103

SRINIVASAN .V.K

04951A2134

SRIKANTH.P

04951A0345

TECUMSEH INDIA PVT. LTD. (HYDERABAD) A PROJECT WORK station of INSTITUTE OF AERONAUTICAL ENGINEERING,DUNDIGAL

(JUNE, 2007)

ACKNOWLEDGEMENTS

Our experience in Tecumseh India Pvt. Ltd has been a wonderful exposure to professional world of manufacturing. Firstly, I thank Tecumseh for giving us an opportunity to work on this project. We are grateful to Prof. Harinath Prasad, HOD (MECH), Prof. Subbaraju for giving us an opportunity to work in this organization. We thank project mentors, Mr. Nagabushanam, Mr. Balaji chander,

Mr.

Pradeep, Mr. Krishna Rao for their continuous feedback and suggestions for the progress of the project. Thank Mr. Vishnu, Mr. Joseph, Mr. Naresh, Mr. Vivek, Mr. Ravi, Mr. Sanjeevi, Mr. Rajesh and Mr. Naveen for their valuable co-operation and suggestions in team work.

.

INSTITUTE OF AERONAUTICAL ENGINEERING,DUNDIGAL

Station:

Tecumseh India Pvt. Ltd

Centre: Hyderabad

Duration:

1 month

Date of Start: June 23rd, 2007

Date of Submission:

26th July

Title of the project:

Statistical process control

Students name(s):

Vishwani. M (AERO) D. Anand babu (AERO) V.K. Srinivasan (AERO) P. Srikanth (MECH).

Project Areas: PRODUCTION TECHNOLOGY Abstract: To improve the quality of a rotary compressor by optimizing various parameters which contribute to performance of compressor, so as to minimize the rejection rate. A set of process parameters responsible for the variation in performance of compressor to be found out and to be optimized in order to control the rejections by applying 7 QC Tools and SPC Techniques.

Signature(s) of student(s) Date:

Signature of PS Faculty

CONTENTS Acknowledgements Abstract

iii

iv

1. Introduction to Organization 2. Introduction

to 7QC tools

3. statistical process control 3.1 Definition 3.2

1

6

1. Introduction to the Organization Tecumseh Products Company is a full line independent global manufacturing of hermit compressors for air conditioning and refrigeration products, gasoline engines and power train components for lawn and garden application, and pumps. Their products are cool in over 100 countries around the world. The company has proposed by providing high quality competitively priced product on an expanding global basis. They have a sixty-year history of growth through successful development and application of new technologies and through acquisitions. Compressors products include a broad range of a conditioning and refrigeration compressors and compressors parts as well as refrigeration condensing units. A compressor is a device, which compress a refrigerant gas. When the gas is later allowed to expand, it absorbs and transfers heat and produces a cooling effect, which forms the basis for a wide variety of refrigeration products. Their compressors range in size from fractional horsepower units used in small refrigerators and dehumidifiers to large units used in commercial air conditioning applications. The company sells compressors in four major market segments: household ref and freezers, room air conditioners; commercial and residential unitary central air conditioning systems and commercial devices including freezers, dehumidifiers, water coolers and vending machines. The company sells compressors to original equipment manufactures and cool products distributors. Tecumseh Products Company (TPC) global visions of providing comfort, health and convenience to millions worldwide, gives an impetus for the company’s steady diversification into new frontiers. And today, this cooling giant’s products are available in over a 100 countries across the globe. TTC entered India through a dual acquisition of seal compressor limited. Hyderabad and the compressor division of whirlpool India limited (TPIL) is a fully subsidiary of TPC. TPIL is the largest independent manufacturer of compressors in the country.

2. Introduction to 7QC tools Production environments that utilize modern quality control methods are dependant upon statistical literacy. The tools used therein are called the seven quality control tools. These include:

1.

Check sheet

2.

Pareto Chart

3.

Flow Chart

4.

Cause and Effect Diagram

5.

Histogram

6.

Scatter Diagram

7.

Control Chart

2.1 Check sheet The function of a check sheet is to present information in an efficient, graphical format. This may be accomplished with a simple listing of items. However, the utility of the check sheet may be significantly enhanced, in some instances, by incorporating a depiction of the system under analysis into the form.

Ch Che eck ckS Sh he ee ett

Defect Type

Shifts

√√√

√√√√

√√

√√√

√

√√√√ √√

√√√

√√√ √ 7 Quality Tools

Quality Improvement: Problem Solving

Fig no. 2.1: Check sheet 2.2 Pareto Chart Pareto charts are extremely useful because they can be used to identify those factors that have the greatest cumulative effect on the system, and thus screen out the less significant factors in an analysis. Ideally, this allows the user to focus attention on a few important factors in a process. They are created by plotting the cumulative frequencies of the relative frequency data (event count data), in descending order. When this is done, the most essential factors for the analysis are graphically apparent, and in an orderly format.

For example, A Pareto charts are shown as:

% Complaints

Pareto chart 30

28

25 20 16

15

12

12

10 6

5 0

Loose T hreads

Stitching flaws

4

3

Button problems

Material flaws

7 Quality Tools Quality Improvement: Problem Solving

70

(64)

60 50 40 30

(6)

a l ib p ra e ti ra o to n s r e rr D o e r s fe c ti ve m S a u te rf a ri c a e ls a b ra s io n s

s

n s

p a rt

io

ve M a c

h

in

e

ti

e n s

e fe c D

ig n

d im

ro n g

P o

o

r

D

e s

0

(10)

(3)

O

(13)

10

c

20

W

Pareto Chart

Percent from each cause

Fig 2.2 Pareto chart 1

Causes of poor quality Quality Improvement: Problem Solving

Fig 2.2(a) Pareto chart 2

(2)

(2)

2.3 Flowchart Flowcharts are pictorial representations of a process. By breaking the process down into its constituent steps, flowcharts can be useful in identifying where errors are likely to be found in the system. “Draw a flowchart for whatever you do. Until you do, you do not know what you are doing, you just have a job.” -- Dr. W. Edwards Deming

The symbols used in flowcharts can be explained as:

F ch Flo low w cha arrtt Activity

Decision

Yes

No 7 Quality Tools Quality Improvement: Problem Solving

Fig no. 2.3 Flow chart symbols

A typical Flowchart can be shown as:

Flowchart

Quality Improvement: Problem Solving

Fig no. 2.4 Flow chart 2.4 Cause and Effect Diagram This diagram, also called an Ishikawa diagram (or fish bone diagram), is used to associate multiple possible causes with a single effect. Thus, given a particular effect, the diagram is constructed to identify and organize possible causes for it. The primary branch represents the effect (the quality characteristic that is intended to be improved and controlled) and is typically labeled on the right side of the diagram. Each major branch of the diagram corresponds to a major cause (or class of causes) that directly relates to the effect. Minor branches correspond to more detailed causal factors. This type of diagram is useful in any analysis, as it illustrates the relationship between cause and effect in a rational manner.

MACHINE

MAN

QUALITY PROBLEM

MATERIAL

METHOD

Fig no. 2.5 Ishikawa diagram An example of Ishikawa diagram in solving a Quality problem is shown here:

FishboneDiagram Measurement Faulty testing equipment Incorrect specifications Improper methods

Inaccurate temperature control Dust and Dirt

Environment

Human

Poor supervision

Machines Out of adjustment

Lack of concentration

Tooling problems Old / worn

Inadequate training

Quality Problem Defective from vendor Not to specifications Materialhandling problems

Materials

Poor process design Ineffective quality management Deficiencies in product design

Process

Quality Improvement: Problem Solving

Fig no. 2.6 Fishbone diagram

2.5 Histogram Histograms provide a simple, graphical view of accumulated data, including its dispersion and central tendency. In addition to the ease with which they can be constructed, histograms provide the easiest way to evaluate the distribution of data. For example, Histograms for some data to understand are as depicted:

Histogram 25

15 10 5

8. 9 9. 9 10 .9 11 .9 12 .9 13 .9 14 .9 15 .9 M or e

6. 9 7. 9

5. 9

4. 9

3. 9

2. 9

0 1. 9

Frequency

20

Category

7 Quality Tools Quality Improvement: Problem Solving

Fig no. 2.7 Histogram

2.6 Scatter Diagram Scatter diagrams are graphical tools that attempt to depict the influence that one variable has on another. A common diagram of this type usually displays points representing the observed value of one variable corresponding to the value of another variable.

Scatter Diagram

.

Quality Improvement: Problem Solving

Fig no. 2.8 Scatter diagram

2.7 Control Chart The control chart is the fundamental tool of statistical process control, as it indicates the range of variability that is built into a system (known as common cause variation). Thus, it helps determine whether or not a process is operating consistently or if a special cause has occurred to change the process mean or variance. The bounds of the control chart are marked by upper and lower control limits that are calculated by applying statistical formulas to data from the process. Data points that fall outside these bounds represent variations due to special causes, which can typically be found and eliminated. On the other hand, improvements in common cause variation require fundamental changes in the process.

Control rol Chart 27

Number of defects

24 UCL = 23.35

21

c = 12.67

18 15 12 9 6

LCL = 1.99

3 2

4

6

8

10

12

Sample number

Quality Improvement: Problem Solving

Fig no. 2.9 Control chart

14

16

2.8 Keys to Successfully Using the Seven Q.C. Tools Mental Attitudes •

Keen awareness to the actual problem.

•

Eagerness to solve problem.

•

Be highly motivated for the challenge

Four Specific Keys



•

Understand the problem

•

Select the right tool for the job

•

Obtain appropriate verbal data

•

Interpret analytical results

Understand the problem

Stage 1 - problem is unclear and not obvious what exact issue should be addressed. Stage 2 - problem is obvious, but causes unknown explore causes and single out valid ones. Stage 3 - problem and causes are known required action is unknown strategies and plan must be developed.



Selecting Right tool for the Job

Stage 1 - Collect verbal information on events (Brain storming). Stage 2 - Choose tool to identify causes (Pareto Diagram). Stage 3 - List strategies and activities (Fishbone Diagram) Stage 4 - Now plan actual activities (Flow charts).



Obtaining appropriate verbal data

Three types of verbal data: •

Facts; factual observations expressed in words.

•

Opinions; factual information colored by opinion.

•

Ideas; New concepts created by analyzing facts.

.Group Discussions:



•

Ensures common understanding.

•

All data should be without bias or distortion.

•

Data should fit objective of the analysis

Interpreting Analytical Results

Information must be obtained for accomplishing objectives from: - Completed diagrams. - Process of completing diagrams. Analyze actual information obtained: - Prepare summarized report with findings, conclusions and processes used. - Check if necessary data has been obtained, if not Discover the cause and take appropriate action. 2.9 Summary The tools listed above are ideally utilized in a particular methodology, which typically involves either reducing the process variability or identifying specific problems in the process. However, other methodologies may need to be developed to allow for sufficient customization to a certain specific process. In any case, the tools should be utilized to ensure that all attempts at process improvement include: •

Discovery

•

Analysis

•

Improvement

•

Monitoring

•

Implementation

•

Verification

3. STATISTICAL PROCESS CONTROL

3.1 Definition Statistical process control (SPC) is a method of visually monitoring manufacturing processes. With the use of control charts and collecting few but frequent samples, this method can effectively detect changes in the process that may affect its quality. Under the assumption that a manufactured product has variation and this variation is affected by several process parameters, when SPC is applied to "control" each parameter the final result trend to be a more controlled product. SPC can be very cost efficient, as it usually requires collection and charting data already available, while "product control" requires accepting, rejecting, reworking and scrapping products that already went through the whole process

3.2 General Information on SPC Classical quality control was achieved by inspecting 100% of the finished product and accepting or rejecting each item based on how well the item met specifications. In contrast, statistical process control uses statistical tools to observe the performance of the production line to predict significant deviations that may result in rejected products. The underlying assumption is that there is variability in any production process: The process produces products whose properties vary slightly from their designed values, even when the production line is running normally, and these variances can be analyzed statistically to control the process. For example, a breakfast cereal packaging line may be designed to fill each cereal box with 500 grams of product, but some boxes will have slightly more than 500 grams, and some will have slightly less, in accordance with a distribution of net weights. If the production process, its inputs, or its environment changes (for example, the machines doing the manufacture begin to wear) this distribution can change. For example, as its cams and pulleys wear out, the cereal filling machine may start putting more cereal into each box than specified. If this change is allowed to continue unchecked, more and more product will be produced that fall outside the tolerances of the

manufacturer or consumer, resulting in waste. While in this case, the waste is in the form of "free" product for the consumer, typically waste consists of rework or scrap. By observing at the right time what happened in the process that let to a change, the quality engineer or any member of the team responsible for the production line can troubleshoot the root cause of the variation that has crept in to the process and correct the problem. SPC indicates when an action should be taken in a process, but it also indicates when NO action should be taken. An example is a person who would like to maintain a constant body weight and takes weight measurements weekly. A person who does not understand SPC concepts might start dieting every time his or her weight increased, or eat more every time his or her weight decreased. This type of action could be harmful and possibly generate even more variation in body weight. SPC would account for normal weight variation and better indicate when the person is in fact gaining or losing weight. 3.3 Introduction TO SPC Techniques Quality of Design and Quality of Conformance: “Quality of Design” is the level of quality, a company plans to achieve for its product. In general, costs rise as this level is raised. “Quality of Conformance” is the difference between the actual quality of a product and its designed quality (i.e., the quality for which the company aims).

The Relationship between Quality, Cost and Productivity

Quality of Design Quality

of

Conformance Manufacturability

Quality

Cost

Productivity

↑ ↑

↑ ↓

↑

↑

↓

↑

of the Design Relationship between Quality, Cost and Productivity

In quality (conformance) control, we set quality levels for groups of products and we control these levels company-wide. On the shop floor, we try to control the process in such a way that we will obtain product lots with specified statistical distributions. (present requirement Cp, Cpk ≥1.33). Statistical quality is not fixed; it always has a range of variation and is a living entity that changes according to the technical and economic conditions and advances in process capabilities. Earlier Cp, Cpk ≥1 was acceptable standard. Subsequently it was revised to Cp, Cpk ≥ 1.33. Now this standard is being revised to Cp, Cpk ≥ 2 as a part of six sigma implementation. Quality Standards: To follow up the Quality standards, a random sample of about 60 to 80 units produced during a period (which may be a day, a week, a month or even a year or sample from a lot produced) must be considered. •

Now draw a histogram for quality characteristics, which may be dimensions on a part, performance of a product, moisture % purity, finish, etc.

•

And now if the histogram looks as normal centered and within 75% of acceptance limits then only the performance is said to be as per the “Quality Standards”.

•

If the histogram has any other shape then the quality is not good enough for the customer satisfaction.

3.4 Statistical Concepts: Following are the concepts we need to know: •

Average (Symbol X bar)

•

Average (Symbol X bar)

•

Range (Symbol R)

•

Standard Deviation(Symbol σ)

•

Normal Distribution

S. no. Inspection Standard 01.

Quality Standard

Too many inspectors (5-10% of Very few inspectors (less than 1% of total employees)

total employees)

02.

High rejections and rework

Very low rejections and rework

03.

High Quality = High Cost

High Quality = Low Cost

(For the same design)

(For the same design)

04.

Difficult to introduce changes in Process changes can be introduced process

quickly

05.

QA/ QC responsible for Quality

Manufacturing is responsible for quality

06.

New

process/

product New process or product introduction is

introduction is slow

quick and economical

07.

High customer complaints rate

Low customer complaint rate

08.

CAPA

are

slow

and CAPA are quick and clear, effective/

confusing/recurring.

everlasting.

09.

Difficult to increase productivity

High productivity is possible

10.

Worker carries the burden of Process carries the burden of Quality Quality and Productivity

and Productivity

Inspection standard Vs Quality standard

Average: To find average add all the readings noted and divide the total by the number of readings. Range:

Range is the difference between the largest and the smallest reading. It shows the total spread of the readings that are noted during the experiment. Standard Deviation: •

Standard Deviation is a figure calculated from collected data.

•

It indicates the variation of production process.

•

It tells us about the dispersion or spread of the data around the average.

•

If the process variation is limited, then most of the individual readings will be near the average. In this case value of standard deviation will be small.

•

If the process variation is large, the data will be more spread or more dispersed around the average. In this case the value of standard deviation will be high.

•

Standard deviation for the sample taken is calculated using the following formula : σ = √ {[(Ҳ-x1)2 + (Ҳ-x2)2 +…….. (Ҳ-xn) 2]/(n-1)} Where Ҳ is Average of readings; xn is the nth reading; n is no. of readings.

Normal Distribution: •

As we have seen earlier, the data is distributed around the average. Some readings lie below the average and the others lie above the average.

•

If individual readings are distributed around the average on the both sides symmetrically and most of the readings are near the average and

very few lie away from the average, then the data is said to be ‘normally distributed. •

The figure looks like a bell and is called bell shaped curve.

Bell curve Process Capability (Cp): An important step in SPC is to establish process capability. A capable process is capable of maintaining variation with in specification limits required for the job. Process capability is expressed in terms of an index called process capability index (Cp). Cp = T / 6 σ Where, T = U.S.L – L.S.L. σ is process variation. Ex:

Suppose you take 100 readings on the shop floor for a particular

characteristic (which may be length, width, diameter, hardness, moisture percentage, etc.). Draw a Histogram from these readings. Find ‘X’ and ‘σ’ for these readings. If the distribution is normal we can talk about process capability.

Machine Capability Index (Cm): A machine capability index is the one which reveals the capability of the machine to meet the tolerance and it is expressed in terms of a ratio as follows: Cm =

(Tolerance)/ (6 σ for machine)

Since Process Capability index should be greater than 1.33 it may be preferable to achieve an index of 1.67 or even 2 for machine capability. However, if improvement in machine capability involves expensive modifications, check overall process variation before making such modifications. If variations from the other factors such as material, method and man are negligible, then machine capability index of 1.5 to 1.66 may be sufficient for the process. Machine Capability study: Since Cm