Professor of School of Engineering, Design and Built Environment, Western Sydney University, Australia. His research interests cover Industry 4.0, Additive Manufacturing, Advanced Engineering Materials and Structures (Metals and Composites), Multi-scale Modelling of Materials and Structures, Metal Forming and Metal Surface Treatment.
Abstract—This paper presents a framework of Metaheuristic evolutionary elements on Taguchi array based on Particle Swarm Optimisation (PSO) Algorithm, encompassing all major algorithmic sequences which include evolution, concept, design steps, vital considerations, analysis and its industrial application. In our approach we trace the evolution of Taguchi method with the original orthogonal array to identify the influential effects of main and some selected interaction of parameters. The evolutionary elements are then determined from PSO Algorithm and merged to generate the new array without performing the orthogonal array at the best so far design point. This concept contributes its present procedure, thereby stating the significance of this proposed method over other conventional techniques. The method is applied to reduce air bubbles formed during an epoxy casting process in the production of linear motors, commonly used in CNC, to reduce rework and unacceptable resistance value. The number of air bubbles per united area is reduced from 0.065 from the current operating condition to 0.009 with the optimal setting obtained from the proposed method. Furthermore the process variation is also significantly reduced. It thus reinforces the vitality of this proposed method as an efficient tool of robust design followed the conventional one.
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