Quality control not only helps to inspect a product and give an acceptance/rejection decision, but also helps in reducing the number of rejections over time. For this, we need to analyse quality data using statistical methods and get required information on present quality. The information obtained should be used judiciously to improve the quality of a product. In this way, the decisions will not be biased by individual perceptions.
Total Quality Management (TQM) and Six Sigma processes to improve product quality are based on statistical methods. This article discusses some of the statistical methods used in quality control work. Emphasis is given on application and methodology rather than mathematics and related proofs.
The most popular statistical method amongst engineers is sampling technique. Other statistical methods include estimation of limits of a quality parameter with known confidence, statistical control charts, comparing quality parameters of two different lots of units (could be from different vendors), fitting quality data to a theoretical probability distribution function and proportion within specification limits.
Sampling is used to accept/reject a large quantity of goods by inspecting a small sample from it. Certain small quantity (as per sampling plan) is selected at random from the goods. Every piece of the sample has to pass or fail a test as per definition given in the test procedure. If failed number of units is less than a defined acceptance number, the entire lot is accepted. Else the entire lot is rejected.
Since acceptance/rejection of the goods is dependent on testing of only a few samples from the lot, risks are involved in this process. Sometimes, a good lot can get rejected and a bad lot can get accepted. The probability of rejecting a good lot is called producer’s risk. The probability of accepting a bad lot is called consumer’s risk. Graph of probability of acceptance versus percentage of defectives in a lot is shown in Fig. 1. It is called operating characteristic.
It can be observed that probability of acceptance decreases as percentage of defectives in a lot increases. Hence a supplier should minimise percentage of defectives in a lot so that the lot could be accepted with high probability. The supplier and consumer should agree on risks acceptable to both of them.
Many a times, it is necessary to estimate properties of goods from a sample data. For example, if a random sample of 20 capacitors is taken from a large lot of 1000µF bin, and their average is found to be 995 µF, the sample average 995 µF is called point estimate of lot average. This number can be used to estimate population (product)average with certain confidence, which is called confidence interval.
In case the population follows normal distribution, properties of normal distribution can be used to estimate average value of the lot with certain confidence. Graph of normal distribution with population average, say 995 µF, is shown in Fig. 2.
In case the population average is more than 995 µF, say 999 µF, normal distribution would be as shown in Fig. 3. We observe that probability to get observed value of 995 µF or less is very small. So we select an upper limit for population average such that the probability to get observed value or less is small, say 0.025.
In case the population average is less than 995 µF, say 990 µF, normal distribution would be as shown in Fig. 4. Since probability to get the observed value of 995 µF or more is very small, we select a lower limit for population average such that the probability to get observed value or more is small, say 0.025.
The gap between lower limit (990 µF) and upper limit (999 µF) is the 95 per cent confidence interval. In other words, the probability of a capacitor’s value being outside these limits is only 5 per cent.
Statistical control charts are used to determine whether a manufacturing process is under control or not. There is a random variation in many manufacturing process parameters, which causes output variations. A high number of causes exist, each contributing a small amount of variation to the process output, but none of them dominating. Under these conditions, the output varies within controlled limits. These causes are called common causes. Manufacturing process under such conditions is said to be under statistical control.
Any special, assignable or identifiable causes (for example, change of raw material, etc) will cause the output to vary beyond the established limits. Control charts method determines when a process goes out of control and action is required to investigate and eliminate assignable causes.
To draw the charts, characteristics of the product are obtained at regular intervals (every hour or half day, or after each 100th or 1000th output). Data collected for each interval is called subgroup. Each sub-group contains several observations. Average and range for each sub-group are calculated. Graph of sub-group average versus sub-group number is plotted. The plot will have a central line, which is a long-term average value of the process. Long-term average is obtained from process historical data. The chart will have two control limits on either side of the central line. These limits are called upper control limit (UCL) and lower control limit (LCL).
Each sub-group should be made such that variations amongst observations in each sub-group are only because of established process chance variations. Frequency of observations must be such that assignable causes, such as change of raw material or change of operator, or supplier of a part, can be detected between subgroups.
If sub-group average falls outside control limits, the process is said to be out of control. Assignable cause needs to be investigated and the process needs to be corrected. Control chart needs to be made again on corrected process.
Control limits are calculated based on the assumption that data follows normal distribution, and 99.73 per cent values of normally distributed data will be within ±3 standard deviations from the mean value.
Fig. 5 shows an example of control chart for resistors manufacturing process.
Analysis of variance
Analysis of variance is a methodology used to compare different lots or batches of goods, or different treatments in a manufacturing process. Data of different batches of goods, or the ones produced from different manufacturing process treatments, is subjected to a hypothesis testing. The hypothesis tested means ‘all batches or treatments have the same mean value.’ If the hypothesis is accepted, it means that different treatments have not affected manufacturing process, or different lots have the same mean value.
Let us take the example of an inductors manufacturing process, where it is decided to study the effect of four different manufacturing process treatments on the average values of inductors produced.
Samples are collected after each treatment. Individual samples’, average values and standard deviations are calculated. Overall average and standard deviations are then calculated for all treatments data put together.
This data is used to calculate variation of data within samples from their average value. The ratio of between-treatment variation to within-treatment variation is called F statistic.
If treatment means are the same, F statistic will not have a very high value. Hence, if the calculated F value is higher than a critical value, the hypothesis that treatment means have the same value is rejected. Critical value (Fc) is selected from F distribution tables such that probability of F ≥ Fc is very small, say 5 per cent, which is called significance of test.
The significance is called type I error and is the probability that we reject the hypothesis, when it is true. If calculated F is less than the critical value, hypothesis will not be rejected and it will be concluded that treatments have not affected manufacturing process and means are equal.
According to many statistical quality control methods, data follows normal distribution. This assumption is reasonable because, manufacturing process outputs are the result of a large number of independent random variables. According to Central Limit Theorem, the sum of a large number of independent random variables follows normal distribution.
The assumption of normal distribution can be varified by Chi-square Goodness-of-fit test. Data is divided into a number of intervals or classes. For each class, lower and upper boundaries are defined. Number of actual observations in each class is obtained. Expected number of observations in each class is then calculated, considering that data followed normal distribution. Chi-square statistics is calculated using the difference between observed and expected values in each class.
If the data follows normal distribution, difference between expected and observed values in each class will not be very high. Hence Chi-square statistics will not be very high.
If Chi-square statistics is higher than a critical value, the hypothesis that data follows normal distribution is rejected. Critical value is selected from Chi-square distribution tables such that probability ≥ critical value is very small, say 5 per cent, which is significant. Else hypothesis will not be rejected and we conclude that the data fits normal distribution.
The author is a Scientist ‘F’ in Reliability and Quality Assurance Directorate of Research Centre Imrat, DRDO, Hyderabad