【文章內容簡介】 le requirement ? A defective unit is a unit that contains one or more defects 26 174。 Materials Quality PCS Training Rev 2 Feb 23 2023 Control Charts For Attributes C on trol C ha rt Sy m bo l D es criptionp C ha rt p % De fectivenp cha rt np de fective27 174。 Materials Quality PCS Training Rev 2 Feb 23 2023 p Chart Concept ? It plots proportion of defective units in a sample ? The proportion of defective units in a sample can be in terms of fraction, percent or dpm ? It allows us to chart production processes where sample size cannot be equal 28 174。 Materials Quality PCS Training Rev 2 Feb 23 2023 Computing Control Limits for p Chart with MRMethod ? Obtain at least k = 30 subgroups or lots. Data collected in of units inspected of units rejected. ? Compute the defective rate from the ith lot (i =1,2,...,k), pi = of units rejected / of units inspected ? Compute the control limits using: 187。UCL (p) = p + 187。CL (p) = p 187。LCL (p) = p ? When LCL 0, put LCL = 0 or N/A ? Draw the control limits on p chart 29 174。 Materials Quality PCS Training Rev 2 Feb 23 2023 Notes : ? The MRMethod describes how the control limits are calculated assuming equal (or nearequal) sample sizes. If the sample sizes vary by more than 50 % of each other, you should consult a statistician. ? np Chart is applicable when all subgroups have constant sample sizes. In terms of practicality, p Chart can/should be used when sample sizes are equal as p carry more meaning than of rejected units (np) Computing Control Limits for p Chart 30 174。 Materials Quality PCS Training Rev 2 Feb 23 2023 Open the dataset . ? Select Control Chart from the Graph menu. ? Select % Defectives as the Process variable. ? Select Lot as the Sample Label variable. Verify option settings. ? Chart Type is “IR”. ? Individual Measurement box is selected. ? Moving Range box is not selected. ? Ksigma is selected, and K = 3. ? Range Span = 2. Click on OK. Example of Computing Control Limits for p Chart Indiv idual M e a s ure me nt of % D e f e c t iv e s0 . 0 0 02 . 5 0 05 . 0 0 07 . 5 0 01 0 . 0 0 01 2 . 5 0 0% Defectives1 2 3 4 5 6 7 8 9 11 13 15 17 19 21 23 25 27 29A v g = 5 . 9 9 7 0 1L C L = 0 . 2 1 4 7 3U C L = 1 1 . 7 7 9 2 931 174。 Materials Quality PCS Training Rev 2 Feb 23 2023 Exercise 2 The dataset contains defect levels for undissolved flux. The number of units inspected the number of units containing undissolved flux were recorded over several lots. Make a p Chart for Undissolved Flux Interpret the control chart 32 174。 Materials Quality PCS Training Rev 2 Feb 23 2023 Interpretation of p Chart Some special causes affecting the p Chart: ? Changes in variable data specifications ? Changes in inspection procedures ? Changes in technician skills, . new technicians ? Changes in pieceparts quality 33 174。 Materials Quality PCS Training Rev 2 Feb 23 2023 Ti m eDataC o ns e c u t iv e l ot sa re v e ry s imi l a rLo t s far a par t ma ybe v e ry dif f e re nt? Timerelated condition where consecutive data values are correlated (. dependent) ? Data values collected nearby in time are very similar ? Data values collected far apart in time may be very different ? Tend to drift over time。 some drift gradually, others may have occasional sudden changes in direction between periods of relative stability Autocorrelation 34 174。 Materials Quality PCS Training Rev 2 Feb 23 2023 Caution When Using MR Method ? If there is autocorrelation, MR(Summary Stat) will underestimate the true process variation the control limits will be too narrow ? If autocorrelation is evident, use Sigma (Std Dev) Method for control limits putation (Refer to Appendix B) 35 174。 Materials Quality PCS Training Rev 2 Feb 23 2023 Control Chart Trend Rules Purpose: ? Improve the responsiveness of the control chart ? Detect more subtle shifts in the process more quickly ? Detect irregularities beyond normal 3? that indicate nonrandomness in process 36 174。 Materials Quality PCS Training Rev 2 Feb 23 2023 How to Interpret a Control Chart? It is based on the Normal Distribution. 0 . 1 3 5 % 2 . 1 4 5 % 1 3 . 5 9 0 % 3 4 . 1 3 0 % 3 4 . 1 3 0 % 1 3 . 5 9 0 % 2 . 1 4 5 % 0 . 1 3 5 %4 3 2 1 0 1 2 3 46 8 . 2 6 %9 5 . 4 4 %9 9 . 7 3 %37 174。 Materials Quality PCS Training Rev 2 Feb 23 2023 SPC Trend Rules Rule 1: A single point beyond either control limit Uses: Detects very large/sudden shifts False alarm rate: % Example: UCL CL LCL 38 174。 Materials Quality PCS Training Rev 2 Feb 23 2023 SPC Trend Rules Rule 2: 9 consecutive points on the same side of the centerline Uses: Detects small shifts or trends False alarm rate: % Example: UCL CL LCL 39 174。 Materials Quality PCS Training Rev 2 Feb 23 2023 SPC Trend Rules Rule 3: 6 consecutive points steadily increasing or decreasing Uses: Detects strong trends Example: UCL CL LCL 40 174。 Materials Quality PCS Training Rev 2 Feb 23 2023 SPC Trend Rules Rule 4: 14 (or more) consecutive points are alternating up and down. Uses: Detects systematic effects, such as alternating machines, operators, suppliers, etc. Example: 41 174。 Materials Quality PCS Training Rev 2 Feb 23 2023 SPC Trend Rules Rule 5: 2 out of 3 consecutive points at least 2 std dev beyond the centerline, on the same side Uses: Detects large changes False alarm rate: % Example: UCL CL LCL 42 174。 Materials Quality PCS Training Rev 2 Feb 23 2023 SPC Trend Rules Rule 6: 4 out of 5 consecutive points on the chart are more than 1 std dev away from the CL Uses: Dete