2. is to influence and to control the quality at the time of manufacturing . Statistical Process Control (SPC) focuses on controlling the manufacturing process to prevent defects rather than detect them. Purpose and Application of SPC

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9. Average ( ) or Mean : It simply means sum of all the individual observed data divided by the no. of observations. X 1 + X 2 + X 3 + - - - - + Xn n n = no. of Observations. Example : Observations = 20, 24, 26, 28, 43, 18 n = 6 20+24+26+28+43+18 = = 6 = 26.5 X X X

10. Range (R) : Range is measure of the variation in a set of data. It is calculated by subtracting the lowest value in the data set from the highest value in that same set. R = X max – X min X max = 43, X min = 18 R = 43 – 18 = 25 Observations = 20, 24, 26, 28, 43, 18

11. Assuming Normal distribution as the pattern of variation for measured characteristics, we know that Average + 3 / - 3 comprises almost all (99.73%) of the observations. Hence 6 sigma is used as a measure of process capability. Lack of statistical control results in a variability larger than 6 sigma. Thus 6 Sigma is valid for statistically stable process and sigma is estimated as = / d 2 R

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13. Indices Tolerance Process Capability = Cp = Potential Capability The Capability Index is defined as the ratio of the Specification spread or Tolerance to the Process Variation = USL - LSL 6 / d 2 R

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15. Cpk = Achieved Capability 3 Min of Or - LSL 3 Cpk = USL – X X Where, = / d 2 and the actual average is R X

18. Inferences based on Cpk : 3 . Cpk = 1.00 represents a just capable process and the process can give good results only if it is perfectly centered. Even a small deviation from the mid of specification will result in non conformance. Here 100% of the tolerance is used. Not a very desirable solution. 4. Cpk < 1.00 means that the process not capable of meeting the specified tolerance. In this case, rework and rejection will be inevitable.