Manufacturing Technology (ME461) Lecture20

8/12/2019 Manufacturing Technology (ME461) Lecture20

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Manufacturing Technology

(ME461)

Instructor: Shantanu Bhattacharya


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Review of previous lecture

Quality engineering.

Understanding the meaning of quality.

The dimensions of quality.

Quality costs.

1. Prevention cost2. Appraisal cost

3. Internal failure costs

4. External failure costs

A framework of quality improvement. Designing quality into processes and products (system design,

parameter design and tolerance design).

Average quality loss and Taguchis loss function .


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Average Quality Loss There is always a variation in the quality characteristic due to

noise factors from unit to unit from time to time during theusage of the product.

If yi ( i= 1,2,3------) is the ithrepresentative measurement of

quality characteristics y, then the average quality loss can be

computed as follows:


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Average Quality Loss


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Average Quality Loss


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Applications of Quality Loss Function:

The quality loss function has been used as a decision support tool in a number of situations .

Determine best factory Tolerances: The loss function can be used to determine economical

factory tolerances:

Example:

Consider the production of automatic transmissions for trucks. The transmission shift point is

one of the critical quality characteristics. Truck drivers would feel very uneasy if the

transmission shift point was farther than the tramsmission output speed on the first to

second gear shift by 35 rpm. Suppose it costs the manufacturer $200 to adjust the valve body

to fix the shift point problem. However, it may cost only $16.40 for labor charges to makeadjustments during the manufacturing and testing phase. Determine the factory tolerances.


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Product Selection :

The loss function can be used to select products asillustrated by the following example.

High-Tech Rotor Dynamics is planning to buy a couple ofthousand bolts to be used in their systems. The systemrequires highly reliable bolts. In case of bolt failure thesystem repair cost is estimated to be $15.00. Twocompanies that offer different kinds of alloys in theirproducts bid to supply the bolts. High-tech decides togo for destructive testing using 20 specimens. Thecriterion used for testing is the ultimate tensilestrength measured in Kgf/mm2. The lower

specification limit is 11Kgf/mm2The purchase quantityis 20,000. The unit costs of products A and B are $.14and $.13, respectively. Advise high tech rotor dynamicsfor its purchase decision.


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Data on Ultimate Tensile Strength of

BoltsProduct (Bolt) Ultimate Tensile Strength Data (Kgf/mm2)

A 15.5 13.8 15.1 15.3 13.7 15.5 13.8 15.1 15.2 13.6

14.2 14.1 14.9 14.8 15.5 14.2 14.5 14.6 14.4 15.4

B 15.5 10.8 15.1 16.3 13.7 10.5 13.8 15.1 12.2 17.6

11.2 14.1 11.9 14.8 17.5 14.2 17.5 14.6 18.4 13.4


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Robust Design of Products and Processes

Robust design is an approach to designing a product or process that emphasizes reduction

of performance variation through the use of design techniques that reduce sensitivity to

sources of variation.

Simply stated we want to achieve the target of quality characteristic but at the same time

we want to minimize the variation in a products functional characteristics to ensure

minimum quality loss.

The target and the variance of a products quality characteristic are affected by certainvariables which can be classified as controllable factors and uncontrollable factors, also

known as noise factors.

Engineered

System

Signal Factors

Noise Factors

Control Factors

Response


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Controllable FactorsControllable factors are those that can be easily controlled, such as choice of materials,

mold temperature, and cutting speed on a machine tool.

They can be separated into two major groups: factors controlled by the user/ operatorand factors controlled by designers.

Factors controlled by User/ Operator:

These are also known as signal factors. A signal factor carries the intent to the system from

a customers point of view to attain the target performance or to express the intendedoutput.

Consider the steering system of a car. A drivers intent is to change direction. For this

purpose the driver changes the steering wheel position, thus giving a signal to the

automobile to change directions. In this case the signal factor is the angular displacement

of the steering wheel. Other example of signal factors include setting a remote control

button of a television set to control volume and brightness and setting the temperaturecontrol knob of a refrigerator


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Factors controlled by Designers

There are three types of factors in this category:

1. Variability control factors: Those that affect the variability in a response are called

variability control factors or simply control factors. For example, the transistor gain

in an electrical power circuit is a variability control factor.

2. Target control factors: These factors can easily be adjusted to achieve the desired

functional relationship between the user input signal and the response. For example

the gear ratio in the steering mechanism of an automobile can be selected duringthe product design stage to get the required sensitivity of the turning radius to a

change in the steering angle.

3. Neutral factors: These are factors that do not affect either the mean response or the

variability in the response. They are also known as neutral factors. It is important to

know these neutral factors, since the cost savings can be obtained by setting themat their most economical levels.


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Noise FactorsNoise factors are, in general, responsible for the functional characteristics of a product

deviating from the target value results in quality loss. Noise factors can be classified

as:

1. Outer Noise: The variables external to a product that affect the product

performance are known as external noise factors. Examples include variations in

temperature, humidity, and dust.

2. Inner Noise: Inner noise is a result of variations due to the deterioration of partsand materials. Examples include loss of resilience of springs, wearing out of parts

due to friction, and increase in resistance of resistors with age.

3. Between Product Noise: Between product noise is due to the variation in the

product variables from unit to unit, which is inevitable in a manufacturing process.

Examples include material variations.

Noise factors, as the name suggests, are uncontrollable factors. Trying to control noise

factors may be a vary expensive proposition, if not impossible.

Taguchis approach to robust design of products and processes attempts to reduce

variability by changing the variability control factors while maintaining the required

average performance through appropriate adjustments in the target control factors.


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Electrical Power Circuit DesignAn electrical power circuit design example given by Taguchi is used to illustrate the

separation of variability control and target control variables. The quality characteristic of

interest is output voltage, y, with a target value of y0 . The output voltage is largely

determined by the gain of a transistor in the circuit, x, whose nominal value can be controlled.The input-output relationship is nonlinear as shown in Fig. 9.3 (a). The product design

engineer can select the nominal value of x to be x0 to achieve the target value of y0 .

However, in real life the transistor gain can deviate from the nominal value x0.because of

manufacturing imperfections in the transistor, deterioration during the circuits life span, and

environmental variations. This deviation of the actual transistor gain around its nominal value

is essentially due to internal noise. If the distribution of x is as shown in the figure, then we

can reduce the variance of the output by selecting higher values of the input. For example if

we select the nominal value of the transistor gain as x1instead of x0, the output variance is

reduced but this results in a higher mean y1 compared with the target y0 . Suppose we have a

resistor in the circuit that has a linear effect on the voltage at all levels of the transistor gain;

then we can easily change the mean y1

to the desired target value y0

by changing the

resistance of the resistor as shown in the figure ahead.

N Li I O l i hi d li


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Non-Linear Input-Output relationship and linear

relationship between output voltage and

resistance

We have achieved what we wanted: the output response is on target and the variability in

the output is minimized. We can therefore categorize the transistor gain as a variability

control factor and resistance as a target control factor.


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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.

F il d ff t l i


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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.

l if ll i f h f


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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 10

means 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.

I i d t lit d i


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Improving product quality duringthe production phase

As 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

S i i l P C l


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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. Histogram

2. Check Sheet

3. Pareto Chart

4. Cause and Effect Diagram

5. Defect Concentration Diagram

6. Scatter Diagram

7. Control Chart

Histogram


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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.

P t Ch t


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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.

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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 Diagram

A 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 mayindicate 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

Manufacturing Technology (ME461) Lecture20

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