Decision making in engineering design process

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Table of contents

Chapter 1. General introduction
1.1. Research motivations:
1.1.1. General context
1.1.2. Design process challenges
1.2. Research Objectives
1.3. Structure of the thesis
Chapter 2. Design: State of the art
2.1. Introduction
2.1.1. Design science
2.1.2. Design Theory and Methodology (DTM)
2.1.3. Function-Behavior-Structure (FBS) ontology
2.2. Decision making in engineering design process
2.2.1. Pareto optimality
2.2.2. Morphogenesis, Observation, Interpretation, Aggregation (MOIA) ontology
2.2.2.1. Observation, Interpretation and Aggregation (OIA)
2.2.2.2. Observation model
2.2.2.3. Interpretation model
2.2.2.3.1. Simon’s function
2.2.2.3.2. Harrington’s function
2.2.2.3.3. Derringer’s desirability function
2.2.2.4. Aggregation model
2.2.2.5. About the modelling of the interpretation and aggregation
2.2.2.5.1. Kolmogorov complexity
2.2.2.5.2. Ordinal and cardinal ranking
2.2.2.5.3. Parametrization of interpretation functions
2.2.2.5.4. Parametrization of aggregation functions
2.2.2.6. Determination of the weighting parameters
2.2.2.6.1. Analytic Hierarchy Process (AHP)
2.2.2.6.2. Adapted Failure Mode Effects and Criticality Analysis (FMECA)
2.2.2.6.3. Delphi method
2.2.2.7. Morphogenesis (Optimization algorithm)
2.2.2.7.1. Morphogenesis definition
2.2.2.7.2. The targeted solutions
2.2.2.7.3. Optimization algorithm
2.2.2.8. Stopping criteria
Chapter 3. Integration of MOIA ontology into Systems Engineering
3.1. Introduction
3.2. Systems Engineering (SE)
3.2.1. Model Based Systems Engineering (MBSE)
3.2.2. Global V-model
3.2.3. Local V-model
3.2.4. SCTO method
3.3. Integration of MOIA into MBSE
3.4. Substitution models
3.5. The optimization of ELM
3.5.1. Optimized-ELM algorithm
3.5.2. Test functions
3.5.3. Optimized-ELM vs random-ELM
3.5.4. Optimized-ELM vs test functions for the minimum search
3.6. Integration of ELM into MOIA
3.6.1. Dynamic optimization process
3.6.2. The practical perspective
Chapter 4. Use cases
4.1. Studied cases
4.2. Introduction
4.3. Electric vehicle powertrain case study
4.3.1. Main objective
4.3.2. Powertrain system specifications
4.3.3. Powertrain system architecture
4.3.3.1. Battery
4.3.3.2. Inverter and electric motor
4.3.3.3. Gearbox
4.3.4. Global EV simulation model
4.3.5. Design variables
4.3.6. Interpretation parameters
4.3.7. Aggregation parameters
4.4. A comparison with the sequential approach
4.5. ELM models
4.6. Numerical results using ELM
4.7. User interface
Chapter 5. Acceptability of optimization
5.1. Introduction
5.2. Participants
5.3. Materials and methods
5.3.1. Initial presentation
5.3.2. Questionnaire
5.3.3. Final presentation
5.4. Questionnaire results
5.5. Conclusion
Chapter 6. Conclusion and perspectives
6.1. Conclusion
6.2. Perspectives: Towards Intelligence Augmentation
References
Appendix I. Drone taxi
Appendix II. Questionnaire