Fracture Mechanics 3

Ammar Grous is Teacher of Mechanical Engineering at CéGEP de l'Outaouais (Academic College), Gatineau, Quebec, Canada.

• Preface ixGlossary xviiChapter 1. Quality Control 11.1. Introduction to statistical process control 11.2. Classical tolerancing and quality control 41.2.1. ISO norms in quality control 51.2.2. QS9000 approach 61.2.3. Capabilities of CNOMO E41.36.110.N control means 81.2.4. Some capability indicators according to ISO/TS 16949, ISO 8258 and FORD 101.2.5. Metrological audits 111.3. The Pareto law – ABC method 141.3.1. Practical application 171.3.2. Malfunction spread over 16 so-called “nevralgic” points 181.4. Lot inspection by attributes 181.4.1. Areas on which lot inspection operates 181.4.2. (np) chart for controlling the number of defects 201.4.3. (c) card to control the number of defects 201.4.4. Average lot quality after control 231.4.5. Choosing a sample plan 321.4.6. Wald plan case study 341.4.7. Average long-term controlled quantity 371.4.8. Case study of a simple sampling plan 421.4.9. Poisson distribution of cumulative probabilities 431.5. Theoretical basics of control by measurement 461.5.1. Choosing a simple sampling plan 471.5.2. Choosing a simple or double sampling plan 481.6. Analysis of process capability 491.6.1. Capability indices for a normally distributed variable: X (μ, σ) 511.6.2. Machine capability Cm and Cmk 551.7. Capability for a non-normal distribution 561.8. Control by measurement charts 571.8.1. Size of the samples to be taken and the frequency of control 601.8.2. Factor for control of X (mean) and range (R) 631.9. Production and reception control 641.9.1. Machine adaptation with respect to production tolerances 641.9.2. Proportion of faulty units 651.10. Control charts 681.10.1. Control by measurements charts for the mean and the range R 691.10.2. Calculating control limits and case study 701.10.3. Study of /R control charts – quality control lab 721.10.4. Graphical representation of the rejections due to non-conformity to TI 781.10.5. Performance case study: Capabilities 801.10.6. Calculating machine capability indices: Cm and Cmk 841.11. Conclusion 851.12. Bibliography 85Chapter 2. Quality Control Case Studies 872.1. The tools of quality, as per W. Deming 872.2. Failure modes, effects and criticality analysis 882.3. Total productive maintenance method 882.4. The LMMEM “5M” process method 932.5. Estimations of times in mechanical productions (machining) 942.5.1. Optimizing times and costs in mechanical production 982.6. Stock management and supply methods 1112.6.1. Hypothesis of a general method to improve stock management 1132.7. Short summary of control charts 1162.7.1. The various control charts 1172.7.2. Measurement control charts – stability control charts (Shewhart) 1182.7.3. Estimating μ0 and σ0 1192.7.4. Efficiency – chart of the average 1202.7.5. Control chart by attributes – determining the limits 1212.8. CUSUM charts 1232.8.1. EWM charts 1242.8.2. Shewhart charts for the average, the range R and the standard deviation (s) 1262.8.3. Control charts for the average and the range R 1272.9. Individual control charts 1302.9.1. Average lengths or CUSUM-Run 1322.10. EWMA statistics – comparison between Shewhart graph control and the EWMA chart control techniques 1332.10.1. Poisson normal distribution approximation 1382.10.2. Decision limits of the X / R control chart through the SPC method 1412.11. Main statistical tests used in quality control 1442.11.1. So-called “Henry Line” or probability diagram test 1442.12. Partial conclusion 1492.13. Bibliography 149Chapter 3. Case Studies 1533.1. Quality control case study: calculating and plotting efficiency curves in simple/double control 1533.1.1. Simple binomial distribution 1533.1.2. Calculation results 1543.2. Calculating the efficiency curves of simple/double control 1563.2.1. Calculation results 1573.3. Calculating efficiency curves in double control: binomial distribution, double efficiency plan (Excel version) 1593.3.1. Calculation results 1593.4. Progressive control (Excel) 1603.4.1. Calculating the acceptance and refusal curves in progressive control 1603.4.2. Calculation results 1613.4.3. Graph 1623.4.4. Measurement acceptance test when sigma (σ*) is known 1633.4.5. Determining the efficiency curve and the acceptance limits 1633.4.6. Acceptance: known standard deviation, sigma = 0.01 1643.4.7. Measurement acceptance test when sigma* (SD) is known 1653.4.8. Choosing a plan 1663.4.9. Plot (Excel) 1673.5. R&R study in quality control and dimensional metrology 1683.5.1. R&R (EV Repeatability and AR reproducibility) 1683.5.2. R&R study 1693.6. X/S control chart study (average and standard deviation, σ ) 1733.7. Case study: capability of a specific control method 1783.7.1. Measurements from a digital micrometer 1783.7.2. Definition drawing or clear picture 1783.7.3. Table of the measurements taken using a digital micrometer (directly read the value from Caliper)   1793.7.4. Table of calculation results (Excel and MathCAD) 1803.8. Case study on type A and B uncertainties 1813.8.1. Uncertainties guide to the expression of uncertainties in measurements (GUM) 1813.8.2. Estimating the compound uncertainty UC 1843.9. Case study: uncertainties 1843.9.1. Uncertainty study as per GUM 1853.9.2. Graph 2003.10. Conclusion 2003.11. Bibliography 201Appendix 203Index 257