Aggregation functions for engineering design trade-offs
Fuzzy Sets and Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Soft computing in engineering design - A review
Advanced Engineering Informatics
Search heuristics for constraint-aided embodiment design
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Comparison among five evolutionary-based optimization algorithms
Advanced Engineering Informatics
A hybrid intelligent genetic algorithm
Advanced Engineering Informatics
Machining fixture locating and clamping position optimization using genetic algorithms
Computers in Industry
Linking objective and subjective modeling in engineering design through arc-elastic dominance
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
In most industrial design processes, the approaches used to obtain a design solution that best fits the specification requirements result in many iterations of the ''trial-and-error'' type, starting from an initial solution. In this paper, a method is proposed to formalize the decision process in order to automate it, and to provide optimal design solutions. Two types of knowledge are formalized. The first expresses the satisfaction of design objectives, relating to physical behaviors of candidate design solutions. This formalization uses three models, an observation one, an interpretation one and an aggregation one; every design solution is qualified through a single performance variable (a single objective function). The second model is related to modifications that may or may not be applicable to the pre-existing solution. The Designer is often able to define preferences concerning design variables. Some modifications related to this pre-existing solution, can be preferred to other ones. A hierarchy of design variables is proposed to formalize these preferences. The concept of arc-elasticity is introduced as a post-processing indicator to qualify candidate solutions through a trade-off between the performance improvement and their relative distances to the initial solution. The proposed method is used and applied to a riveted assembly, and a genetic algorithm is used to identify optimal solutions.