The reliability optimization of mechanical systems using metaheuristic approach

The multiobjective Ant Colony System (ACS) meta-heuristic has been developed successfully to provide a solution for the reliability optimization problems of series-parallel system and has been demonstrated its application to the reliability design of gearbox. The problems involve the selection of components with multiple choice and redundancy levels that produce maximum benefit, and are subject to the cost and weight constraints at the system level. These are very common and realistic problems involving conception design of engineering system and reliability engineering. It is becoming increasingly important to develop efficient solutions to these problems because many mechanical and electrical systems are becoming more complex, even as development schedules get shorter and reliability requirements become very stringent. The multiobjective Ant Colony System algorithm offers distinct advantages to these problems compared to alternative optimization methods, and can be applied to a more diverse problem domain with respect to the type or size of the problems. Through the combination of probabilistic search, multi-objective formulation of local moves and the dynamic penalty method, the multi-objective ACSRAP, which performs very well on the redundancy apportionment problems (RAP) of the series-parallel k-out-of-n : G subsystem and reliability design of gear box, allows us to obtain an optimal design solution very frequently and more quickly than with other heuristic approaches. Therefore, the use of these techniques to the reliability optimization problems of mechanical systems announces great potential and makes it possible to develop a powerful and economic tool for which the designers always seek

Optimisation of the squeeze forming process.

This thesis presents the optimisation of the squeeze forming process, considering both the thermal and mechanical aspects. The Finite Element Method has been used to simulate the process and a Genetic Algorithm was used as an optimisation tool. The thermal optimisation has been applied to the squeeze forming process to achieve near simultaneous solidification in the cast part. The positions of the coolant channels were considered as design variables in order to achieve such an objective. The formulation of the objective functions involved two points and also considered the whole domain. The validation aspects of the optimisation of the casting processes for 2D and axi-symmetric problems were presented. The influence of the interfacial heat transfer coefficient related to optimisation of the process was explored. For the multi-objective optimisation problem, the objective was to achieve near simultaneous solidification in the cast part and also near uniform von Mises stress distribution in the die for the first and also tenth cycles. This is because it has been found that the process starts to reach cyclic stabilisation after the tenth cycle. The comparison between the design obtained from the practical solution derived from the optimisation process and also the design which has been applied in industry was also discussed. The Design Sensitivity Analysis and Design Element Concept have been applied to the squeeze forming process. For parameter sensitivity analysis, the Youngs Modulus was considered as a design variable. A few design element subdivisions have been employed to explore its application to the process. For shape sensitivities involving the coolant channels, the parameterisation was required in order to consider the coolant channel as an entity. The extent to which the tendency to move the coolant channel either in the X or Y-direction with respect to the particular von Mises stress constraint in the die was also discussed

Nouvelle approche hybride d'optimisation multiobjective basée sur la méthode des surfaces de réponse et le système de colonies de fourmis

L'environnement industriel est devenu très compétitif et exige des délais de fabrication de plus en plus courts, des coûts réduits, ainsi que des produits de bonne qualité. Ces besoins conduisent à des problèmes d'ingénierie complexes, caractérisés par de nombreux objectifs ainsi que des contraintes plus complexes. De par le grand nombre de variables mises en jeu et la nécessité d'utiliser des logiciels pour les calculs des contraintes, ce processus d'optimisation est coûteux en temps de calcul et en expérimentation. Une des possibilités de réduction des coûts vient de l'introduction de la méthode des surfaces de réponse dans le processus d'optimisation. L'objectif principal de notre recherche est le développement d'un nouvel outil efficace d'optimisation et d'analyse. Nous avons développé une méthodologie souple et robuste, capable de résoudre des problèmes complexes. Le terme « optimisation » est très répandu, mais beaucoup de ceux qui l'emploient ne disposent pas d'outils spécifiques à cette fin. Ainsi, l'ingénieur cherche-t-il toujours la performance maximale, sans renoncer aux contraintes de coût minimum du projet. Pour ce faire, nous proposons une nouvelle approche multiobjective combinant un outil de simulation à la modélisation avec la méthode des surfaces de réponse et aux algorithmes des colonies de fourmis (ACO). Le modèle d'optimisation développée est appliqué à l'optimisation d'un procédé de dessalement de l'eau de mer et à l'optimisation d'un procédé d'usinage cinq axes. Ces applications ont conduit à de grandes améliorations des résultats, de l'ordre de 30% pour le problème d'usinage, comparativement à l'usuelle fonction de désirabilité. L'approche hybride développée constitue une technique puissante et flexible pour la recherche de solution optimale pour différents problèmes

Computing system reliability modeling, analysis, and optimization

Ph.DDOCTOR OF PHILOSOPH

Evolutionary Algorithms in Engineering Design Optimization

Evolutionary algorithms (EAs) are population-based global optimizers, which, due to their characteristics, have allowed us to solve, in a straightforward way, many real world optimization problems in the last three decades, particularly in engineering fields. Their main advantages are the following: they do not require any requisite to the objective/fitness evaluation function (continuity, derivability, convexity, etc.); they are not limited by the appearance of discrete and/or mixed variables or by the requirement of uncertainty quantification in the search. Moreover, they can deal with more than one objective function simultaneously through the use of evolutionary multi-objective optimization algorithms. This set of advantages, and the continuously increased computing capability of modern computers, has enhanced their application in research and industry. From the application point of view, in this Special Issue, all engineering fields are welcomed, such as aerospace and aeronautical, biomedical, civil, chemical and materials science, electronic and telecommunications, energy and electrical, manufacturing, logistics and transportation, mechanical, naval architecture, reliability, robotics, structural, etc. Within the EA field, the integration of innovative and improvement aspects in the algorithms for solving real world engineering design problems, in the abovementioned application fields, are welcomed and encouraged, such as the following: parallel EAs, surrogate modelling, hybridization with other optimization techniques, multi-objective and many-objective optimization, etc