manufacturing technology (me461) lecture21

8/12/2019 Manufacturing Technology (ME461) Lecture21

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Manufacturing Technology

(ME461)

Instructor: Shantanu Bhattacharya


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Review of previous lecture

Average quality loss.

Determination of factory tolerances based on AQL.

Purchase decision based on AQL.

Robust design of products and processes. Controllable factors. (user based and designer based.)

Variability control factor, Target control factor, Neutral factors.

Noise factors (Internal, external and product to product

noise). Electrical power circuit design and variability/ target control

factors.


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Failure Mode Effect AnalysisThis is an important technique that is widely used in industry, including the big three

auto manufacturers, for continuous product quality improvement to satisfy the needs ofthe customer.

FMEA can be described as a systematized group of activities intended to:

1. Recognize and evaluate the potential failure of a product/ process and its effects.

2. Identify actions that could eliminate or reduce the chance of potential failureoccurring.

3. Document the process.

It is complementary to the design process of defining positively what a design process

must do to satisfy the customer.

FMEA is a generic approach that can be used to identify failure modes and analyze their

effects on the system performance with the objective of developing a preventive

strategy.


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Failure mode effect analysisProcess FMEA is a methodology for evaluating the process for possible ways in which the

failures can occur. The primary objective in process FMEA is to eliminate potential

production failure effects by identifying important characteristics that have to bemeasured, controlled and monitored.

The FMEA philosophy is based on the characterization of potential failures. Failures are

characterized by the following tuple: (Occurrence, severity and detection).

Occurrence: How often the failure occurs?

Severity: How serious the failure is?

Detection: How easy or difficult it is to detect the failure?

Examples of typical failure modes include cracked, dirty, deformed, bent and burred

components; worn tools and improper setup.


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Failure mode effect analysisTo implement process FMEA, the following steps may be taken:

Identify the problems for each operation using brainstorming and committee discussions.

Cause and effect diagrams can be used. For example, the potential causes of machine failurescould involve mechanical or electrical subsystem failure, tools, inspection equipments, operators

and so forth.

Use flow process charts as a basis for understanding the problem. This provides a common

basis for communication among the committee members.

Collect data. Data collection may be necessary if data are not already available.

Prioritize the problems to be studied. The ranking of priorities is based on the following:

RPN (risk priority number) = occurrence X severity X detection

Use appropriate tools to analyze the problems by making use of the data.

Implement the suggestions.

Confirm and evaluate the results by doing some experiments and ask whether you are better or

worse off or the same as before. Repeat the FMEA as often as necessary.


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Real Life Illustration of the Use of Process FMEA


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Process steps for FMEAThe process involves manual application of wax inside a car door, with the objective of retarding

corrosion.

In items 1 to 8 information such as part identification, names of team members, and date is provided.

Items 9 through 22 systematically describe the process FMEA approach. In this example the problemof corrosion in car door is considered.

To retard corrosion the manual application of wax is considered. The manner in which this process

could potentially fail to meet the process requirements or design intent is defined by potential failure

mode.

In our example, the failure mode is insufficient wax coverage over the specified surface. We have to

determine the effect of failure in terms of what the customer might experience.Here it would be the unsatisfactory appearance due to the rust and impaired function of the interior

door hardware.

The next step is to asses the seriousness of the effect based on a severity scale of 1-10. (Column 12)

We now have to define the potential cause of failure in terms of something that can be corrected or

controlled.

For every potential cause the frequency of occurrence should be estimated on a scale of 1-10. here 10means that the failure is inevitable.

We now have to access the probability of detection of the cause of failure by current practice.

This is also on a scale of 1-10 where 1 denotes a very low probability.

The next step is to calculate the risk priority no.


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Improving product quality during the

production phaseAs dicussed many times the very survival of companies depends on continuous improvement of

quality.

Quality can be designed into a product, as we have seen in the previous section, but then the

product must be manufactured.

During the manufacturing process assignable causes may occur, seemingly at random.

These assignable causes result in a shift in the process to an out of control state, resulting in an

output that may not confirm to requirements.

To produce quality output it is necessary to have a process that is stable or repeatable, a

process capable of operating with little variability around the target or nominal dimensions of

the products quality characteristics.

The idea behind improving quality is to reduce variability and eliminate waste.


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Quality improvement process


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Statistical Process ControlStatistical process control is very useful in monitoring process stability and improving

process capability by reducing variability.

It should be emphasized here that SPC alone cannot reduce variability. However, with the

aid of process improvement tools such as design of experiments, process variability can be

reduced.

The following are widely used as process improvement tools:

1. Histogram2. Check Sheet

3. Pareto Chart

4. Cause and Effect Diagram

5. Defect Concentration Diagram

6. Scatter Diagram

7. Control Chart


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HistogramSuppose it is needed to produce a shaft within 1+ 0.05 in. On a numerically controlled

turning machine.

The shaft diameters are plotted against frequency as shown in the figure below.

The plot is known as histogram, and it provides information on the central tendency,

spread, and shape.

We see that the distribution of the shaft diameter is symmetric with the mean around 1

in. and variability between 0.95 and 1.05 in.


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Check SheetA check sheet serves as a useful tool for collecting historical or current operating data for

the process under investigation.

In the early stages of implementation of the SPC, it is important to understand what causes failure of the system or

product performance.This could be due to a number of defects which even may not affect the product performance but certainly affects

the quality of the products.

For example common product such as spark plug used in a car. Over a period of 5 days a list of spark plug defects is

recorded on a check sheet.

Some defects are due to tool changeovers to different types, as for the raised stud defects.

This check sheet helps in identifying the sources of these defects with respect to time.

We notice that except for dirty cores the defects are not recorded everyday. Cores are supplied from outside

vendors therefore the problem lies in controlling the quality of the incoming part.


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Pareto ChartThe Pareto law states that on an average 80 % of the defects stem from 20% of the

causes.

In case of the spark plugs most of the quality problems come from only three out of nineor more problem areas.

A Pareto diagram is helpful in identifying the fact that taking care of these few problems

takes care of 80% of all causes of the problem situation.


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Cause and effect diagramA cause and effect

diagram is a technique

for systematically listingthe various causes of a

problem.

The CAE diagram, also

known as the fish bone

diagram or IshikawaDiagram (credited to Dr.

Kaoru Ishikawa), serves

as

a tool to indicate how various causes can operate independently as well assimultaneously to produce an eventual effect on the manufactured product.

For example consider the quality of the turned part such as a shaft. Poor quality may be

caused by several factors, such as works-manship, worn tool, or non optimal machining

conditions.

D f C i Di


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Defect Concentration DiagramA defect concentration diagram is a visual representation of the unit under study showing

all possible views with all possible defects identified on it. This type of representation is

useful in understanding the types of defects and their possible causes.

Scatter DiagramA scatter diagram is useful in establishing a relationship between two variables.

The shape of the scatter diagram is obtained by plotting the two variables. It may

indicate a positive or negative correlation between the variables or no correlation at all.

Such information helps in developing a control strategy for these variables.

Control Chart

The key to improving quality lies in reducing the variability of the quality characteristic of

interest.

Variation in the outcome of a process is principally due to 2 types of causes: (Chance

causes and assignable causes).The inherent variability in the process is due to the cumulative effect of many small but

essentially unavoidable random causes. A process is said to be in control only if the chance

causes are present.

However, there may be other causes of variability that may create a shift in the process to

an out of control state resulting in a significant portion of output not conforming to

specifications.

S Di


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Scatter Diagram


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Control ChartA typical control chart is a graphical display of a quality characteristic measured

on a sample versus the sample number. There are 3 essential elements of a

control chart:

1. Center line representing the average value of the quality characteristic

corresponding to the in control state.

2. Upper control limit (UCL) represented by the upper horizontal line of the

chart.3. Lower control limit (LCL) represented by the line below the central line.

The selection of the upper and the lower points also depend on the notion

that all the sample points should fall between them if the process is in

control.

Points lying outside these limits signal the presence of assignable causes.

The process should then be investigated to eliminate assignable causes.


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A general model of a control chart

Suppose y is a sample statistic that measures some quality characteristic ofinterest, and let ybe the mean ybe the standard deviation.

The CL, UCL and LCL are given by:

UCL = y+ ky

CL = y

LCL= yky

Where k is the distance of the control limits from the center line. If K =3, thecotrol limits are typically called three sigma control limits.

Control Charts are categorized into variable control chart and attribute control

chart.


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Variable Control ChartIf the quality characteristic can be measured and expressed as a continuous variable, then it

can be conveniently characterized by measures of the central tendency and variability.

Variable control charts can explain the process data in terms of both location (in terms of

average) and spread (in terms of piece to piece variability). For this reason the control charts

for variables are always developed and analyzed in pairs.

The X chart is widely used for controlling central tendency. Charts based on either the

sample range (R chart) or the sample standard deviation (S chart) are used to control the

process variability.

The range chart is relatively efficient for smaller subgroup sample sizes, especially below 8.

The S chart is used with larger sample sizes.


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The attribute control chartMany quality characteristics cannot be measured in a continuous scale.

This happens when the quality of an item is judged as either conforming or non

conforming to a specification.

For example, items which have cracks, missing components, appearance defects, or

other visual imperfections may be rendered as rejects, defective or non conforming

items.

Control charts to control such quality characteristics are called attribute control charts.

The different attribute control charts are the following:

1. The pchart for proportion of units non conforming (from samples not necessarily of

constant size).

2. The np chart for number of units non conforming (for samples of constant size).3. The c chart for number of non conformities or defects (from samples of constant

size).

4. The uchart for number of non conformities per unit ( from samples not necessarily

of constant size).

h b f f l h


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The benefits of control charts

Control Charts provide a large number of benefits. The compound effect of all

these benefits is an overall improvement in the quality.

Some of the distinct benefits are as follows:

1. Control charts are effective means of monitoring statistical control.

2. Control charts help predict the performance of a process when the process is

in a state of statistical control.3. They provide a common language about the process, such as between 2 or 3

shifts, between production and maintenance supervisors, or between

suppliers and producers.

4. Control charts help direct corrective measures in a logical manner by

identifying the occurrence of assignable causes. Some of the assignable causesmay require special resources and involvement of the management. This helps

in avoiding confusion, frustration and high cost of possible misdirected efforts

to solve the problem.


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Characteristics of Data

( )The notations are for the average and the standard deviation for a sample of data.

We think as a typical value of the Xs in a sample, or as a point around which the

numbers tend to cluster.

The standard deviation s is a typical deviation of an observation x from the average

It is a deviation descriptive of variation within the sample.

From a well organized frequency distribution as we have already seen above we can say

the following:

1. Between

Effi i t C l l ti f


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Efficient Calculation of

through the coded variable methodLet us consider the following dataset:

1921, 1919, 1924, 1924 and 1925. Suppose we subtract the lowest value (1919) fromeach and call the difference Y. The five Y values are 2, 0, 5, 5 and 6.


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The central tendency and the variance of the distribution is

unchanged irrespective of the nature of the code

There is also another short cut that we can apply to find out Sy from the ys as ys are much

simpler numbers.

Let us assume that Y = X-


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Alternate Formula for Mean

This formulae simplifies

calculations in a

frequency table.

Mean and Variance in a Freq enc


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Mean and Variance in a Frequency

Table


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Calculation of Mean and Variance using the frequency method


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Calculation of Mean and Variance using the frequency method


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Checklist necessary for X and R chartsIt is helpful to visualize the decisions and calculations that must be made and the

actions that need to be taken for plotting a X and a R chart. They are the following:

1. Decisions preparatory to the control charts Some possible objectives of the charts.

Choice of variables.

Decisions on the basis of subgroups.

Decisions on size and frequency of subgroups.

Setting up the forms for recording the data.

Determining the method of measurement.2. Starting the Control Charts

Making and recording measurements and recording other relevant data.

Calculating the average X and range R of each subgroup.

Plotting the X and R charts.

3. Determining the trial control limits

Decision on required number of subgroups before control limits are calculated. Calculation of trial control limits.

Plotting the central lines and limits on the charts.

4. Drawing preliminary conclusions from the charts

Indication of control or lack of control.

Interpretation of processes in control.

Relationships between processes out of control and specification limits.

Decisions preparatory to control charts


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Decisions preparatory to control charts

Some possible objectives of the charts are the following:

1. To analyze a process with a view to

(a) secure information to be used in changing specifications or in determining whether a

given process can meet specifications.(b)To secure information to be used in establishing or changing production procedures.

(c)To secure information to be used in establishing or changing the inspection procedures

or acceptance procedures.

2. To provide a basis for current decisions during production as to when to hunt for

causes of variation and take action intended to correct them, and when to leave a

process alone.3. To provide a basis for current decisions on acceptance or rejection of manufactured or

purchased product.

Choice of Variables

The variable chosen for control charts for X and R must be something that can bemeasured and expressed in numbers, such as dimension, hardness number, tensile

strength, weight etc.

From a standpoint of the possibility of reducing production costs, a candidate for a control

chart is any quality characteristics that is causing rejections or rework involving substantial

costs.

From an inspection standpoint destructive testing always suggests an opportunity to use the

control chart to reduce costs.

Decision on the basis of sub-grouping


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Decision on the basis of sub grouping

The key idea in Shewhart method is the division of observations into what

Shewhart called rational subgroups.

The subgroups should be selected in a way that makes each subgroup as

homogeneous as possible and that gives the maximum opportunity for variation

from one subgroup to another.As applied to control charts on production, this means that it is of vital importance

not to lose track of the order of production.

Particularly, if the purpose of the control chart is to keep detecting shifts in the

process average, one subgroup should consist of items produced as nearly as

possible at one time; the next subgroup should consist of items all produced at a

single time later; and so forth.

Decision on the size and frequency of subgroups

Shewhart suggested 4 as the ideal sub group size. In industrial uses 5 seems to

be a better alternative because of ease of calculations.

The essential idea of the control chart is to select subgroups in a way that gives

minimum opportunity for variation within a subgroup. It is therefore desirable that

the size be as small as possible.

Subgroups of two or three may often be used to good advantage, particularly

where the cost of measurements is so high as to veto the use of larger

subgroups.

Larger subgroups of 10 or 20 are sometimes appropriate if it is desired to make

the control chart sensitive to small changes.

Setting up the forms for recording the data


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Setting up the forms for recording the data

Layout of data should be as per convenience of calculation and analysis.

The forms should have a recording space for item of measurement, unit of

measurement and operator remarks about tool change operator change machine

manufacturing technology (me461) lecture21

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