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Design of experiments - Investigating and understanding the effects of manufacturing variability

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Originally presented in October 2013 at the Romax European Summit 2013

Originally presented in October 2013 at the Romax European Summit 2013

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  • 1. Design of Experiments Investigating and Understanding the Effects of Variability on Driveline Performance Marceliano Fumero – Technical Account Manager 16th October 2013
  • 2. Agenda • Introduction to DoE • Methods to Design your Experiment • DoE in the Design and Development Process • Case Studies • Summary Slide 2 CONFIDENTIAL © Copyright 2013
  • 3. INTRODUCTION TO DOE Capability Overview and Benefits of DoE Slide 3 CONFIDENTIAL © Copyright 2013
  • 4. Introduction to DoE • DoE is a tool that provides capability to perform automated parametric investigations on Romax Designer models. Slide 4 CONFIDENTIAL © Copyright 2013
  • 5. Introduction to DoE Capability Overview: • Reduces experimentation time by automating iterative processes Time Saving • Allows you to study a large number of design candidates to identify optimum solutions • Allows you to simulate manufacturing variability and asses robustness of your design Cost Saving • Generate interpretable and meaningful results Improved quality Slide 5 CONFIDENTIAL © Copyright 2013
  • 6. METHODS TO DESIGN YOUR EXPERIMENT Simulation Methods Available and Typical Applications Slide 6 CONFIDENTIAL © Copyright 2013
  • 7. Methods to Design your Experiment Full Factorial Method: Application: • Method to used to investigate the whole design space (including all permutations of variables) • Understanding interaction between variables • Identifying optimum solution Variable 2 • Running complex sensitivity analysis Variable 1 Slide 7 CONFIDENTIAL © Copyright 2013
  • 8. Methods to Design your Experiment Sensitivity Analysis Method: Application: • Method used to identify the most dominant parameters on the design • Quickly identify parameters with large effect on performance (with no account for variables interaction) • Root of failure initial analysis • Reducing number of variables for a full factorial analysis Slide 8 CONFIDENTIAL © Copyright 2013
  • 9. Methods to Design your Experiment Monte Carlo Method: Application: • Method used to simulate manufacturing variability (tolerances) • Robustness analysis Variable 2 • Assessing optimum manufacturing methods Variable 1 Slide 9 CONFIDENTIAL © Copyright 2013
  • 10. Methods to Design your Experiment Optimisation Method: Application : • Method used to optimise a selection of parameters according to specific targets • Finding the most optimum design solution to achieve particular performance targets Variable 2 Target • Performance criteria can cover durability, NVH and Efficiency all together Variable 1 Slide 10 CONFIDENTIAL © Copyright 2013
  • 11. Methods to Design your Experiment Secondary Level Studies Method: Application Example: • This is a sub-study of the primary investigation. Provides a looping capability which can be used to combine different types of investigations • optimise the performance taking into account manufacturing variability o The secondary study uses a Monte Carlo simulation to investigate manufacturing variability Variable 2 o The primary study uses a Genetic Algorithm to drive the optimisation Variable 1 Slide 11 CONFIDENTIAL © Copyright 2013
  • 12. DOE IN THE DESIGN AND DEVELOPMENT PROCESS Where does DoE fit in the design and development process and application examples Slide 12 CONFIDENTIAL © Copyright 2013
  • 13. DoE in the design and development process Mass Cost Reactions Gearbox Layout Optimisation Product Planning Concept Design Detail Design Gear Optimisation Efficiency Pre-Load Optimisation Proto Manufacture Robustness Analysis NVH Root of Failure Analysis Proto Testing Testing Manufacturing Try Out Production Model Calibration Durability Slide 13 CONFIDENTIAL © Copyright 2013
  • 14. Application Examples Concept Optimisation • Assess the influence of design changes at concept stage Optimise gearbox layout and component size Slide 14 CONFIDENTIAL © Copyright 2013
  • 15. Application Examples Gear Macro Geometry Optimisation Explore feasible gear macro-geometry solutions using a genetic algorithm search Select the best gear design taking into account multiple objectives Slide 15 CONFIDENTIAL © Copyright 2013
  • 16. Application Examples Contact Pattern Optimisation Target value Optimised value Optimise gear contact patterns using evolutionary optimisation Slide 16 CONFIDENTIAL © Copyright 2013
  • 17. Application Examples Reduction of Gearbox Vibration Levels Advanced optimisation tools for reducing transmission error Reduce sensitivity to manufacturing tolerances Slide 17 CONFIDENTIAL © Copyright 2013
  • 18. Application Examples Test-Data Correlation for Model Calibration Calibrate complex models by comparing calculated responses with test data A correlated model provides a more powerful predictive tool Slide 18 CONFIDENTIAL © Copyright 2013
  • 19. CASE STUDIES Successful Projects Carried Through with DoE Slide 19 CONFIDENTIAL © Copyright 2013
  • 20. AT Correlation and Variability Analysis AT vibration simulation in RomaxDesigner Good level of correlation achieved Variability analysis performed 200 400 600 800 1000 Expected noise variance due to manufacturing evaluated Slide 20 CONFIDENTIAL © Copyright 2013
  • 21. Housing Vibration Correlation Client was investigating RomaxDesigner as a tool for NVH process improvement Romax fine-tuned their models to a standard suitable for correlation using Design of Experiments The result was good agreement between test and measured vibration Slide 21 CONFIDENTIAL © Copyright 2013
  • 22. Variability Simulation Normal Distirbution of TE 1st GM 3rd Gearset TE 30 50 2 40 25 Monte-Carlo simulation for original design 30 20 10 0 0 0.2 Normal Distirbution of TE 1st Optimized 3rd Gearset 0.4 0.6 0.8 1 1.2 1.4 20 15 10 5 1.6 1.8 0 2 0 TE [um] 60 500 1000 Harmonic Response Variation / 27th Order 1500 2000 2500 3000 Optimized 3rd Gearset 50 4000 4500 5000 30 40 3500 Input speed [rpm] 35 25 Monte-Carlo simulation for revised design Acceleration [m/s2] No. of Candidates Vibration 35 Acceleration [m/s ] No. of Candidates 60 30 20 10 Normal Distirbution of TE 1st Optimized 3rd Gearset - Grinding 0.2 0.4 0.6 0.8 1 1.2 1.4 0 0 20 15 10 5 1.6 1.8 0 2 0 500 1000 Harmonic Response Variation / 27th Order 1500Optimized 3rd Gearset - Grinding 2000 2500 3000 3500 35 TE [um] 120 4000 4500 5000 Engine speed [rpm] 30 100 Acceleration [m/s2] No. of Candidates Harmonic Response Variation / 27th Order GM 3rd Gearset 80 25 Monte-Carlo simulation for ground gear 60 40 20 20 15 10 5 0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 0 2 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 Engine speed [rpm] TE [um] Slide 22 CONFIDENTIAL © Copyright 2013
  • 23. SUMMARY Slide 23 CONFIDENTIAL © Copyright 2013
  • 24. Summary • Design of Experiments allows you to further explore the “All-in-One” capabilities of RomaxDesigner allowing you to perform automated parametric investigations. • It has applications in a large number of scenarios such as component optimisation, system robustness or model correlation • Reduces design time as there is less need for user interaction. Optimisation process less limited by available time • More successful design decisions can be made as more design candidates can be investigated. True optimal and robust solutions can be found, which means delivering better quality products • Can be used at all stages of the modelling process to enhance the system performance, combining multiple targets such as durability, NVH and/or efficiency Slide 24 CONFIDENTIAL © Copyright 2013