Some Options for Assembly Quality

1. Some Options for Assembly Quality Victor E. Kane Kennesaw State University International Conference on Present Practices and Future Trends in Quality and Reliability Indian Statistical Institute Jan 2008

2. 1. Overview 1. Introduction 2. Typical Situation – FMEA 3. Example Data 4. Modeling - Poisson 5. Alternative Approach – Geometric 6. Capability Discussion 7. Long-Term Case Study 8. Summary of Approach

3. 1. The Problem Manufacturing Consists of Many Types of Operations: • Machining • Batch Operations • Chemical Processes • Heat Treating • ASSEMBLY Each type of operation has a unique analysis approach.

4. 1. Assembly Example Typical Situation: • 200 units assembled per hour • 100 assembly stations • 150 line operators • All products final tested

5. Status 1. Introduction 2. Typical Situation – FMEA 3. Example Data 4. Modeling - Poisson 5. Alternative Approach –Geometric 6. Capability Discussion 7. Long-Term Case Study 8. Summary of Approach

6. 2. Typical Assembly Analysis Approach – FMEA Spreadsheet • Operation and Function – Part of Assembly Line Being Evaluated • Potential Failure Modes – Possible Failures • Possible Effects of Failure – Effects of Failure • Severity Potential Causes of Failure – Rating Scale on Consequence of Failure (1= No Customer Effect)…..(10=Failure could injure Customer) • Occurrence – Rating Scale on likelihood of Failures (1=Failure unlikely)…(10= Failure is Almost Certain) • Current Controls – Document Process • Detection – Rating Scale on Detection likelihood (1=Almost Certain)…(10=Absolute Uncertainty) • Risk Priority Number (RPN) = Severity X Occurrence X Detection

7. 2. Typical Analysis Approach FMEA Example

8. 3.Monthly Assembly Defects/10000 Units

9. 3. Monthly Defects

10. 3. Many Low Frequency Defects

11. 4. Poissson Model? • Let X denote the number of misassembly events in a month (12,000 units) for a certain type of defect. Assume X is Poisson λ. How good is this model?

12. 4. Defect Frequency of Occurrence

13. 4. Chi-Square • Occurrences Observed Probability Expected to Chi-Sq • <=1 19 0.397613 12.7236 3.09603 • 2 5 0.270605 8.6594 1.54641 • 3 2 0.183222 5.8631 2.54534 • >=4 6 0.148559 4.7539 0.32663 • N N* DF Chi-Sq P-Value • 32 0 2 7.51443 0.023 • Try Something Different?

14. 4. Defects: Typically Once per Year

15. 4. Poisson Fit

16. 4.Chi-Square Contribution

17. 1. Introduction 2. Typical Situation – FMEA 3. Example Data 4. Modeling - Poisson 5. Alternative Approach – Geometric 6. Capability Discussion 7. Long-Term Case Study 8. Summary of Approach

18. 4. Geometric Model?

19. 5. Defect Space - A Listing of all possible misassembly errors that can escaps all containment actions Strategy – Conduct experiments/trials to determine all elements of the Defect Space and then use Error Proofing.

20. 5. Mfg -> Assy What Can We Learn? • Mfg has many continuous measurements, Assy has a few PLUS Distance measures to fixed points that assess assembly station consistency. • Mfg has process Capability Studies to establish stability. Assy can use Accelerated Assembly Trials - increase line speed to generate errors • Calculate the station defect rates (mj j=1,…,s Stations). • Update the expanded Defect Space.

21. 5. Mistake Proofing Defect Space

22. 5. Defect Generating Process

23. 5. Containment Matrix

24. 6. Capability Discussion

25. 6. CPU Visual

26. 6.Cpk Index

27. 6. Cp Index

28. 6. Assembly Capability • In the Assembly application capability is the likelihood of the process performing as intended – with No Misassembly Defects. (I.e. all assemblies are within design intent). Thus, • P(X = 0) will be our “capability like” measure. For the Poisson Model P(X=0) = │x=0 = e

29. What is Good Assembly Capability?? • In our example l =0.00073 so 1 = 0.99927 P(X=0) = 1

30. 6.Alternative Assembly Measures • Usual Cpk for continuous measurements (e.g. oil fill quantity) • “Distance capabilities” Compute the Cp between fixed points on the product being assembled. Variation in critical distances can be due to slight movement within the assembly station. This is undesirable and should be eliminated.

31. 7.Defects Per Month

32. 7.Long-Term Results for Error Proofing

33. Status 1. Introduction 2. Typical Situation – FMEA 3. Example Data 4. Modeling - Poisson 5. Alternative Approach – Geometric 6. Capability Discussion 7. Long-Term Case Study 8. Summary of Approach

34. 8.Summary of Approach • Identify all known assembly defects (start defect space). • From above identify the defect operators (e.g. upside down). Us the Defect generating process to test for other defects. • Build product with reject conditions to test if they can escape. • For multiple product lines attempt to assemble with wrong parts. • Continually update Defect Space. • Conduct Accelerated assembly trials

35. 8. Summary (con’t) • 7. Record number of misassembly defects (mj) by station. • 8. From probability modelcalculate the probability of 0 assembly defects as a measure of overall line assembly capability. • 9. Form Error Proofing teams to eliminate items in the Defect Space. • 10. Calculate Cpk for continuous parameters. • 11. Calculate “Distance capability” for selected areas to assess station consistency.

36. Thank you for Arranging this Wonderful Meeting. • Drop me a note on your thoughts: vkane@kennesaw.edu • Questions??