Title: Near-optimal prediction of geometrical requirements of injection moulded parts using Mamdani-based fuzzy logic controller

Authors: Pandu Ranga Vundavilli; J. Phani Kumar; Benguluri Surekha

Addresses: School of Mechanical Sciences, IIT Bhubaneswar, Bhubaneswar, Odisha, 751013, India ' Department of Mechanical Engineering, DVR and Dr. HS MIC College of Technology, Kanchikacherla, AP, 521180, India ' Department of Mechanical Engineering, DVR and Dr. HS MIC College of Technology, Kanchikacherla, AP, 521180, India

Abstract: Injection moulding process is popularly used to fabricate complex and intricate parts with thermoplastic and composite materials. In this paper, Mamdani-based fuzzy logic (FL) controller has been developed to predict the quality of the parts produced using plastic injection moulding machine. It is to be noted that the quality of the parts produced depends on various geometrical requirements, such as global warpage, lower edge surface planarity and hole circularity of the manufactured part. However, these geometrical requirements depend on various input process parameters, namely melting temperature, mould temperature, packing time and packing pressure. As the input-output relationship of the injection moulding process is highly non-linear, FL technique is considered to model the process. It is important to note that the performance of the FL system depends on its knowledge base (that is, rule base and database) developed by the human expertise. In the present paper, genetic algorithm (GA) is used to optimise the optimal knowledge base of the FL system. Further, the prediction accuracy of the developed models has been tested with the help of 20 test cases and found reasonably good accuracy. [Received 1 January 2013; Revised 17 May 2013; Accepted 18 December 2013]

Keywords: injection moulding; Mamdani FLCs; fuzzy logic controllers; genetic algorithms; geometric requirements; fuzzy control; global warpage; lower edge surface planarity; hole circularity; melting temperature; mould temperature; packing time; packing pressure; prediction accuracy; thermoplastics; composites.

DOI: 10.1504/IJMR.2014.064438

International Journal of Manufacturing Research, 2014 Vol.9 No.3, pp.276 - 293

Published online: 30 Aug 2014 *

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