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7/29/2019 Unit 3kljhjk,mnkl,

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Unit 2 Control charts

Introduction

Theory of control charts

Measurement range

Construction and analysis of R chart

Process capability study

Attributes of control charts

Defects

Construction and analysis of control charts

Variable sample size

Construction and analysis of C charts


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A control chart is a statistical technique for

controlling the quality of a product being

manufactured.

It was first devised by Dr. Walter A. shewart after

whose name these charts are also called Shewart

charts.

The main advantage of control chart is that it can

predict the rejects when they are likely to occur,

which enables corrective action to be taken

before a defective product is actually produced.


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Theory of control charts

Machine environment follows normal distribution

curve.

It is not possible to manufacture two exactly identical

products. So, variability does exists in all repetitive

processes.

Difference in dimensions or any other quality of theproduct are bound to happen if

1. Different machine tools are used.

2. Different cutting tools are used.

3. Working conditions are different.

4. Workers of different skills manufacture the products.

5. Improper jigs, tools and fixtures are used.


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The variability is due to chance causes as well asassignable causes.

A control chart accepts the normal variation due tochance causes but eliminates entirely the errors due toassignable causes.

Chance (usual) variations are normally of a lessermagnitude than assignable variations and occurrandomly.

Chance variations occurs randomly and can bedescribed by the normal probability distribution curves.So, control limits are defined within which variationsare acceptable and beyond which they are unacceptable.

A process is said to be incontrol if it produces itemswhose attributes or variable fall within the acceptablerange and is said to be out of control if it producesitems whose attributes or variables are beyond the

acceptable range.


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What Is a Control Chart?

A statistical tool used to distinguish between process

variation resulting from common causes and

variation resulting from special causes.

Popularity of control charts1. Control charts are a proven technique for

improving productivity.2. Control charts are effective in defect prevention.3. Control charts prevent unnecessary process

adjustment.

4. Control charts provide diagnostic information.5. Control charts provide information about process

capability.


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Use of control charts

Quality of a product (Process) can be controlled bythe analysis of control charts.

We can control the process with respect to range and

mean value with the help of R chart and chart. Period of sample checking depend upon two factors.

1. What is the process ?

2. How much time is taken for observation ?

If process is stable for long time, sample for

checking is taken at large intervals.

X


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Plotting of points

In actual, plotting of points for individual observationsdoes not properly reveal the trend of variations and the

chart gets clustered up. In actual control chart applications, measurements are

taken on sample comprising of 4 or 5 consecutivelyprocessed articles each, drawn from the production run

at regular intervals so one point is plotted for eachsample which represents the average value in thesample.

Measurement range When the quality of a product is dependent upon some

measurable physical quantity or variable that is to becontrolled, we make control charts called control

charts for variables.


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A control chart consists of:

1. A control or central line (CL): It indicates the desiredcontrol level of the process.

2. An upper control limit (UCL): It indicates the uppertolerance limit.

3. A lower control limit (LCL): It indicates the lowertolerance limit.

As long as the points fall within the control limits, theprocess is under statistical control and we do notquestion the quality of the product.

But, if a plotted point falls outside the control limits,

this alerts the production manager to the possibilitythat the quality of the product is unacceptable and thatthe process is not under statistical control. So, he triesto determine whether variation is due to chance causesor some assignable causes.


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If the control chart indicates that the observed variationis due to chance causes alone, the process is said to bein control. If on the other hand, the control chart

indicates that the observed variation is not likely due tochance, it can be said that the manufacturing process isout ofcontrol.

In that event the process is halted and effort is made to

seek and correct the possible cause.Two types of control charts are usually used:

1. Control charts for variables:

They are used to achieve and maintain an acceptablequality, level for a process whose product can besubjected to quantitative measurement such asdiameter of a hole, length of a bolt, thickness of a pipe,

specific resistance of a wire.


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2. Control charts for attributes:

They are used to achieve and maintain an

acceptable quality level for a process whose

product can be subjected to quantative

measurement but can be classified as good or

bad, as acceptable or not acceptable. For

instance surface finish of a table colour orbrightness of an article is either acceptable or not

acceptable.

Control charts for variables:

1. Control charts for sample means ( - charts)

2. Control charts for sample ranges (R - charts)

X


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Construction of charts.

Control charts for attributes

In inspection by variables, measurement of the dimensions is

done. However, this is sometimes difficult as well asuneconomical.

Inspection by attributes is just the other way of inspection. Inthis method actual measurements are not done, instead the

number of defective are counted. The products are inspected the same way as by GO and

NOGO gauges.

For instance, in case of electricity bulb, the most important

attribute is whether the bulb glows or not. In such situations,items are inspected to find out whether they possess aparticular attribute or not. Control charts prepared under thesesituations are termed as control charts for attributes.

An attribute is a quality characteristic for which a numericalvalue is not specified.


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Three most commonly used control charts for attributesare:

Control chart for fraction defective (P-Chart)

Control chart for number of defectives (np-Chart)Control chart for number of defects (C-Chart)

P-chart is normally used to plot and control fractiondefectives when the sample size does not remains

uniform. Also it is usually used to know the proportion of

defective items.

np-chart is used to plot and control the number ofdefectives when the sample size remains constant.

It is normally used to know the number of defectiveitems in a consignment.


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C-chart is used when the production process involvescomplex assembly of a large number of components,such as in the manufacture of an aircraft, an automobile,

a T.V., a computer, cloth, etc.Knowledge of the number defects per unit is useful in maintaining a

satisfactory level of quality.

Defects

A quality characteristic that does not meet certainprescribed standards (or specifications) is said to be anonconformity (or defect).

For example, if the length of steel bars is expected to be

40 1.0 cm, a length of 41.5 cm is not acceptable. A product with one or more nonconformities, such that

it is unable to meet the intended standards and is unableto function as required, is a nonconforming item (ordefective).

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