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ISO/TS 16355-6:2019 Applications of statistical and related methods to new technology and product development process - Part 6: Guidance for QFD-related approaches to optimization >
ISO/TS 16355-6:2019 Applications of statistical and related methods to new technology and product development process - Part 6: Guidance for QFD-related approaches to optimization, 2019
- Foreword
- Introduction
- 1 Scope
- 2 Normative references
- 3 Terms and definitions
- 4 Basic concepts of QFD
- 5 Integration of QFD and robust parameter design
- 5.1 Quality engineering
- 5.1.1 General
- 5.1.2 Loss function
- 5.1.3 Types of factors which affect variability
- 5.2 When to use quality engineering
- 5.3 Robust parameter design, QFD, and TRIZ
- 6 Types of QFD and robust design projects
- 7 QFD and robust parameter design team membership
- 7.1 QFD uses cross-functional teams
- 7.2 Core team membership
- 7.3 Subject matter experts
- 7.4 QFD team leadership
- 8 Robust parameter design
- 8.1 General
- 8.2 Signal-to-noise ratio
- 8.2.1 General
- 8.2.2 Signal
- 8.2.3 Noise
- 8.2.4 Three types of SN ratios
- 8.3 Assessing robustness
- 8.4 Two-step optimization
- 8.4.1 General
- 8.4.2 Design of experiments (DOE)
- 8.5 Steps to robust parameter designed experiments
- 8.5.1 General
- 8.5.2 Step 1. Clarify the system’s ideal function
- 8.5.3 Step 2. Select signal factor and its range
- 8.5.4 Step 3. Select measurement method of output response
- 8.5.5 Step 4. Develop a noise strategy, and select noise factors and levels
- 8.5.6 Step 5. Select control factors and their levels from design parameters
- 8.5.7 Step 6. Assign experimental factors to inner or outer array
- 8.5.8 Step 7. Conduct experiment and collect data
- 8.5.9 Step 8. Calculate the SN ratio (η) and sensitivity (S)
- 8.5.10 Step 9. Generate factorial effect diagrams on SN ratio and sensitivity
- 8.5.11 Step 10. Select the optimum condition
- 8.5.12 Step 11. Estimate the improvement in robustness by the gain
- 8.5.13 Step 12. Conduct a confirmation experiment and check the gain and reproducibility
- 8.5.14 Conclusions
- 8.6 Case studies in robust parameter design
- Annex A (informative) Integration of robust parameter design (RPD) with quality function deployment (QFD) and theory of inventive problem solving (TRIZ)
- Annex B (informative) Other optimization methods
- Bibliography [Go to Page]