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Air Bubble Reduction in Epoxy Resin Casting Process for CNC Industries

Pongchanun Luangpaiboon 1 and Sirirat Juttijudata 2
1. Thammasat University Research Unit in Industrial Statistics and Operational Research, Department of Industrial Engineering, Faculty of Engineering, Thammasat University, Pathumthani, 12120 Thailand
2. Applied Optimization Unit, Faculty of Engineering at Sriracha, Kasetsart Univeristy Sriracha Campus Chonburi 20230 Thailand

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.

Index Terms—CNC, epoxy resin casting process, metaheuristic, particle swarm optimisation, Taguchi method, signal to noise ratio

Cite: Pongchanun Luangpaiboon, Sirirat Juttijudata, "Air Bubble Reduction in Epoxy Resin Casting Process for CNC Industries," International Journal of Mechanical Engineering and Robotics Research, Vol. 10, No. 9, pp. 478-484, September 2021. DOI: 10.18178/ijmerr.10.9.478-484

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.