Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computers in Industry - Special issue: Application of genetics algorithms in industry
An integrated method of parameter design and tolerance design for multiple criteria systems
International Journal of Computer Applications in Technology
| Hi-index | 0.00 |
Traditional practice to tolerance design has been a part of a three-step sequential approach to the overall product design process involving (i) conceptual design, (ii) parameter design, and (iii) tolerance design, in isolation. This practice works well for linear assemblies, as the sensitivities of tolerances are fixed, i.e. independent of the nominal dimensions. However, for nonlinear assemblies after the second step, an integrated approach involving minor adjustment of nominal dimensions and selection of tolerances in the third step, can be better to control the variability in the assembly output characteristic. The latter case has been addressed in this study. Simultaneous selection of design and manufacturing tolerances, and choice of a machine from amongst the alternatives, frequently encountered in different stages of realization of individual dimensions, are important issues in product development. Optimal design problem with focus on these issues has been attempted here. The resulting optimization problem involving a combinatorial and nonlinear search space cannot be effectively solved for the global solution using conventional optimization techniques. The genetic algorithm, a nontraditional optimization technique, has been proposed in this research. The solution of the aforementioned concurrent design problem has been demonstrated with the help of a simple case study.