SPC%20Lecture1 - Methods and Philosophy of Statistical...

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Unformatted text preview: Methods and Philosophy of Statistical Process Control Statistical process control is a collection of tools that when used together can result in process stability and variability reduction Introduction to Statistical Quality Control, 4th Edition The seven major tools are 1) Histogram or Stern and Leaf plot 2) Check Sheet 3) Pareto Chart 4) Cause and Effect Diagram 5) Defect Concentration Diagram 6) Scatter Diagram 7) Control Chart Introduction to Statistical Quality Control, 4th Edition Chance and Assignable Causes of Quality Variation A process that is operating with only chance causes of variation present is said to be in statistical control. A process that is operating in the presence of assignable causes is said to be out of control. The eventual goal of SPC is reduction or elimination of variability in the process by identification of assignable causes. Introduction to Statistical Quality Control, ' 4th Edition 4—2 CHANCE AND ASSIGNABLE CAUSES OF QUALITY VARIATION Assignable cause three /’6 > 5 IS present; process \ 4/ out-of-control / I” \‘A /.._ x Assignabie cause two / i is present; process is //" i i out—of—control ‘ ' I2 w-/ Assrgnabie cause one ,/ ' ’ I e " is present; process is ,,,, / out-of-control ;’/1’ Only chance causes of variation present; /”/ . process is in A/ ‘70 l ,/,. control ,v/ / ,. . ,f/f ,/ x / ./ , ,r' Process quality characteristic, ,\' Figure 4-1 Chance and assignable causes of variation, Statistical Basis of the Control Chart Basic Principles A typical control chart has control limits set at values such that if the process is in control, nearly all points will lie between the upper control limit (UCL) and the lower control limit (LCL). l Ce'nterline p.‘ [I 7—“ ‘3 Lower control limit " Sample quality characteristic -__i_J__1_L._.J_..r_ I L-_._l._J._l__L__.L .... ,J .... ..J.._i_ Sample number or time Figure 4'2 A tl’PlCal control chart. Out-of-Control Situations 0 If at least one point plots beyond the control limits, the process is out of control °_ If the points behave in a systematic or nonrandom manner, then the process could be out of control. Relationship between hypothesis testing and control charts ° We have a process that we assume the true process mean is u .= 74 and the process standard deviation is o = 0.01. Samples of size 5 are taken giving a standard deviation of the sample average, as — = 0.0045 J5 Introduction to Statistical Quality Control, 4th Edition Relationship between hypothesis testing and control charts ° Control limits can be set at 3 standard deviations from the mean. ° This results in “3-Sigma Control Limits” UCL = 74 + 3(0.0045) = 74.0135 CL= 74 LCL = 74 - 3(0.0045) = 73.9865 - Choosing the control limits is equivalent to setting up the critical region for testing hypothesis- }%:n==75 Hfip¢75 Introduction to Statistical Quality Control, 4th Edition Relationshiy between the Eroeess and the control chart Distribution of individual measurements x: i i , Normal I D'Stgicbg'on ________ _ IIIIIIIIIIII _._ _________ m Wilyfllifij " Normal with “a: O 01 / mean u = 74 ' ’ and - ............... __.___ ................ ._.___ ....................... .__._ UCL = 740135 i ai = 0.0045 ,/ / t. r _/-’ 2‘- ..«L ............ _. / __1__ -_;___ .... ......... _. Ce"ter = 74.0000 \ \\ I f,» / Line \\ N \ l \[ i ‘\ Sample: \ i \ a ,, = 5 x! ------------ —~ - - - ~ - t ~ - - - v ~ - - —-' LCL = 73.9865 \\ l Figure 4-4 How the control chart works. Important uses of the control chart Most processes do not operate in a state of statistical control. Consequently, the routine and attentive use of control charts will identify assignable causes. If these causes can be eliminated from the process, variability will be reduced and the process will be improved. The control chart only detects assignable causes. Management, operator, and engineering action will be neceSSary to eliminate the assignable causes. Out-of-control action plans (OCAPs) are an important aspect of successful control chart usage . Introduction to Statistical Quality Control, 4th Edition Types the control chart Variables Control Charts — These charts are applied to data that fellow a continuous distribution (measurement data). Attributes Control Charts ~ These charts are applied to data that follow a discrete distribution. Type of Process Variability - Stationary behavior, uncorrelated data Stationary behavior, autocorrelated data - Nonstationary behavior Introduction to Statistical Quality Control, 4th Edition 40 _ _ , . . _ i . . i . . . . . m- 30 A“. u 10 50 100 1 50 200 (c) Figure 4-6 Data from three different processes. (a) Sta- tionary and uncorrelated (white noise). (1:) Stationary and autocorrelated. (c) Nonstationary. Type of Variability ° Shewhart control charts are most effective when the in- control process data is stationary and uncorrelated. Popularity of control charts 1) Control charts are a proven technique for improving product1v1ty. 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. Introduction to Statistical Quality Control, 4th Edition Choice of Control Limits General model of a control chart UCL = pw + Low Center Line = uw LCL = uw — Low where L = distance ‘of the control limit from the center line H w = mean of the sample statistic, w. c7w = standard deviation of the statistic, w. Introduction to Statistical Quality Control, 4th Edition By moving the control limits farther from the center line, we decrease the risk of a Type I error or a false alarm (i.e., the risk of a point falling beyond the control limits, indicating an out-of—control condition when no assignable cause is present) 9 Widening the control limits will also increase the risk of a Type 11 error (i.e., the risk of a point falling between the control limits when the process is really out of control. 74.0180 ‘ 0.001 UCL = 74.0139 74.0135 MHW 3-sigma UCL = 74.0135 74.0090 ‘ HI 74.0045 ‘ Center line = 74.0000 74.0000 W 73.9955 — 73.9910 ~ . - ' LCL = 73.9865 73.9865 b 0.001 LCL = 73.9861 73.9820 " o | | | I 1 I ! l__l__i_1. LJ LJ \ I 1-..L_3_.|.l..‘ Sample number Figure 4-7 Comparison of three-sigma and 0.001 probability limits for the X chart. Three-Sigma Limits The use of 3—sigma limits generally gives good results in practice. If the distribution of the quality characteristic is reasonably well approximated by the normal distribution, then the use of 3—sigma limits is applicable. 4 These limits are often referred to as action limits. Warning Limits on Control Charts Warning limits (if used) are typically set at 2 standard deviations from the mean. . If one or more points fall between the warning limits and the control limits, or close to the warning limits the process may not be operating properly. Introduction to Statistical Quality Control, 4th Edition Warning Limits on Control Charts 0 Good thing: warning limits often increase the sensitivity of the control chart. Bad thing: warning limits could result in an increased risk of false alarms. 74.0180 74.0135 74.0090 74.0045 . 74.0000 CenteLllne _ 74.0000 739955 _ LWL — 73 9910 73.9910 -—-——-:-—w'-—--——-—~—-—-—~~~—«. 73.9865 LCL= 73.9865 73.9820 — UCL = 74.0135 1 1 1 Sample number I Figure 4—8 An E chart with two-sigma warning limits. Sample Size and Sampling Frequency In designing a control chart, both the sample size to be selected and the frequency of selection must be specified. Larger samples make it easier to detect small shifts in the process. Current practice tends to favor smaller, more frequent samples. Introduction to Statistical Quality Control, 4th Edition Average Run Length _ - The average run length (ARL) is a very important way of determining the appropriate sample size and sampling frequency. Let p = probability that any point exceeds the control limits. Then, Illustration Consider a problem with control limits set at 3 standard deviations from the mean. The probability that a point plots beyond the control limits is again, 0.0027 (i.e., p = 0.0027). Then the average run length is ARL =1/0.0027 =3 70 Introduction to Statistical Quality Control, 4th Edition What does the ARLgtell us? 0 The average run length gives us the length of .time (or number of samples) that should plot in control before a point plots outside the control limits. 0 For our problem, even if the process remains in control, an out—of—control signal will be generated every 370 samples, on average. Average Time to Signal - Sometimes it is more appropriate to express the performance of the control chart in terms of the average time to signal (ATS). Say that samples are taken at fixed intervals, h hours apart. ATS = ARL (h) Introduction to Statistical Quality Control, 4th Edition Rational Subgroups Subgroups or samples should be selected so that if assignable causes are present, the chance for differences between subgroups will be maximized, while the chance for differences due to these assignable causes Within a subgroup Will be minimized. Selection of Rational Subgroups 0 Select consecutive units of production. — Provides a “snapshot” of the process. — Effective at detecting process shifts. - Select a random sample over the entire sampling interval. . —- Can be effective at detecting if the mean has wandered out-of-control and then back in-control. Innoduction to Statistical Quality Control, 4th Edition 10 c. .1 r} [a .\‘ , C .-vs—~;:.—_—;I~ 45 I g. g: = Process 1 i C mean 5-; f :3 5 ..._..1‘... :——-——-‘ 5. I... (i six 1 (g U “MI ..._L ...I__._LH_ L. I . I I .... .. I._._J ._J_.__J___.._ 1 2 3 4 5 6 7 8 9 10 11 12 Time (LI) | 123456789101112 Time (12) Figure 4-10 The “snapshot” approach to rational subgroups. (a) Behavior of the process mean. (12) Corresponding E and R control charts. l x I Process 9 mean —>‘ . [,i. f l l (a) UCL'F CLrIw “C; “1/04.. {LA “J \G/ w V‘ \ I.) \O LCL'F F— --~-- — - A —- UCLR . .' CL,H - R..- _ J \/‘/ \ I I I I I i __l__i ___l___ ’ l 123456789101112 Time (/2) Figure 4-11 The random sample approach to rational sub- groups. (a) Behavior of the process mean. ([7) Corresponding E and R control chairs. Analysis of Patterns on Control Charts Nonrandom patterns can indicate out-of—control conditions ‘ Patterns such as cycles, trends, are often of considerable diagnostic value - Look for “runs” - this is a sequence of observations of the same type (all above the center line, or all below the center line) - Runs of say 8 observations or more could indicate an out—of-control situation. —— Run up: a series of observations are increasing — Run down: a series of observations are decreasing I t L 1 3 5 7 9 11 13 15 17 Sample number 19 21 Figure 4-12 An X control chart. Center line LCL i i l l | L_.L. ..... .L .... .J ...... .L.._.._J-_._..I.~..._L "3-; """" "EM—éwr—Ws 9 10 11 12 13 14 15 Sample number Figure 4-13 An 3 chart with a cyclic pattern. Western Electric Handbook Rules Should be used carefully because of the increased risk of false alarms! A process is considered out of control if any of the following occur: V 1) One point plots outside the 3-sigma control limits. 2) Two out of three consecutive points plot beyond the 2—sigma warning limits. 3) Four out of five consecutive points plot at a distance of 1- sigma or beyond from the center line. 4) Eight consecutive points plot on one side of the center line. Introduction to Statistical Quality Control, 4th Edition 11 ...
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SPC%20Lecture1 - Methods and Philosophy of Statistical...

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