Control Charts in Quality Assurance

Control Charts in Quality Assurance 

Introduction

The purpose of control charts is to find if a process is stable and in-control, or unstable and out-of-control. When a process is stable, or “in control,” this suggests that it's predictable and affected only by normal random causes of variation. An unstable or “out-of-control” process is suffering from an equivalent common causes of variation, but it's also suffering from “special” or “assignable” causes. If you are successfully centralizing all important process variables, and your incoming raw materials are relatively consistent, then your process should be stable and in control.

Control Chart Basics

A control chart consists of a time trend of a crucial quantifiable product characteristic. In addition to individual data points for the characteristic, it also contains three lines that are calculated from historical data when the method was “in control”: the road at the centre corresponds to the mean average for the data, and the other two lines (the upper control limit and lower control limit) represent the average value plus and minus 3-sigma, where sigma is adequate to the quality deviation.
The standard deviation or sigma indicates how widely the info are dispersed or scattered around their mean average value. It was determined by Walter A.Shewhart that when a process is in-control, over 99% of process values fall in the 3-sigma control limits  If a worth were to fall outside of those limits, this is able to indicate the presence of an unusual or special cause, and a process adjustment or corrective action would be in order.
A special cause would even be indicated if the info were to exhibit another non-random pattern, including an upwards or downwards trend, or a cyclical pattern.
Several different “rules” are developed to work out when a process is “out-of-control” and a special cause is present. These are sometimes called the “Western Electric Rules” because they were first developed by Shewhart while he was performing at the Western power company .

What Does A Control Chart Look Like?

The process variable (the time to urge to work) is plotted over time. After sufficient points, the process average is calculated. Then the upper control limit (UCL) and therefore the lower control limit (LCL) are calculated. Nobody sets these values- they're determined by the method and the way you sample the method . The UCL is that the greatest value you'd expect from a process with similar causes of variation present. The LCL is that the smallest value you'd expect. As long because the all the points are within the bounds and there are not any patterns, only common causes of variation are present. The process is said to be "in control."

Types of Control Charts

There are various sorts of control charts which are somewhat similar and are developed so that they suit particular characteristics of the standard attribute being analyzed. Two categories of chart exist, which are “variable” or “attribute” in nature.

Variable Control Charts

X bar control chart

This type of chart graphs the means (or averages) of a group of samples, plotted so as to watch the mean of a variable, for instance the length of steel rods, the load of luggage of compound, the intensity of laser beams, etc.. In constructing this chart, samples of process outputs are taken at regular intervals, the means of every set of samples are calculated and graphed to the X bar control chart.  This chart can be used to work out the particular process mean, versus a nominal process mean and can demonstrate if the mean output of the method is changing over time.

Range R control chart

This chart demonstrates the variability within a process. It is suited to processes where the sample sizes are relatively small, for ex. < 10. Sets of sample data are recorded from a process for the particular quality characteristic being checked. For each set of date the difference between the tiniest and largest readings are recorded. This is the range R of the obtained set of data. The ranges are now recorded onto a control chart. The center line is that the averages of all the ranges.

Standard Deviation S control chart

The S chart are often applied when monitoring variable data. It is suited to situations where big nos. of samples are being recorded. The “S” relates to the standard deviation within the sample sets and is a good indication of variation within a large set versus the range calculation. An advantage of using the quality deviation is that each one data within a group are utilized to work out the variation, instead of just the minimum and maximum values.

Attribute Control Charts

Attribute control charts are utilized when observing count data. There are two categories of count data, namely data which arises from “pass/fail” type measurements, and data which arises where a count within the sort of 1,2,3,4,…. arises. Depending on the data  recorded, different forms of control charts should be applied.

u and c control charts

The u and c control charts are applied when monitoring and controlling count data within the sort of 1,2,3, …. i.e. specific numbers. An example of such data is that the number of defects during a batch of staple , or the amount of defects identified within a finished product.

The c chart is used where the no. of defects per sample unit and the no. of samples per sampling period remains constant.

In the u chart, again same as that to the c chart, the no. of defects per sample unit can be recorded, however, with the u chart, the no. of samples per sampling period may differ.

p and np control charts

P charts are utilized where there's a pass / fail determination on a unit inspected. The p chart shows if the proportion is defective within a process changes over the sampling period (the p shows the portion of successes). In the p chart the sample size varies over time. A similar chart to the p chart is that the np chart. However, with the np chart the sample size must stay constant over the sampling period. An advantage of the np chart is that the amount non-conforming is recorded onto the control instead of the fraction non-conforming. Some process operators are easier plotting the amount instead of the fraction of non-conformances.

 

Advantages of Control Charts:

(1) Control charts warn in time, if required rectification is completed , well in time the scrap and percentage rejection are often reduced.

(2) Thus ensures product quality level.

(3) an impact chart indicates whether the method is on top of things or out of control thus information about the choice of process and tolerance limits are provided.

(4) The inspection work is reduced.

(5) The control charts filter the prospect and assignable causes of variations within the observation thus substantial quality improvement is feasible .

(6) Determines process variability that and detects unusual variations happening . So reputation of the concern/firm are often built by application of those charts.

Conclusion

Knowing which control chart to use during a given situation will assure accurate monitoring of process stability. It will eliminate erroneous results and wasted effort, focusing attention on truth opportunities for meaningful improvement.

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Rutvik Dagadkhair – 11

Neel Doifode – 21

Omkar Gandhal – 25

Sarthak Shelke – 60