Title: Enhancement of quality of polypropylene by optimisation of injection moulding parameters with genetic algorithm

Authors: Deepak Kumar; G.S. Dangayach; P.N. Rao

Addresses: Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur-302017, India ' Malaviya National Institute of Technology Jaipur, 302017, India ' Department of Technology, University of Northern Iowa, Cedar Falls, IA 50614-0178, USA

Abstract: Plastic injection moulding (PIM) represents one of the most important processes in the mass production of precise plastic parts with intricate geometries. Polypropylene (PP) is the widely used material related to plastic parts for automobile and packaging industry. It was observed that thermal shrinkage and warpage in plastic parts are the most prominent defects and affects the quality of plastic parts. In this paper, a methodology has been presented for reducing the thermal shrinkage and warpage along with the maximising the impact strength (IS) of virgin polypropylene (PP). To obtain the optimum values of injection moulding parameters, Taguchi orthogonal array (OA) was used. Overall, six parameters were chosen for the experiment. The linear graph was utilised to know the effectiveness and interactions of the parameters. Thus, with Taguchi method minimum thermal shrinkage of 4.67%, minimum warpage of 1.8 mm and maximum impact strength of 56.7 J/m were obtained in PP specimens. With this methodology, prediction equations and mathematical models for thermal shrinkage, warpage and IS of PP were developed which are useful for industrial applications. With multi objective genetic algorithm, these mathematical models were optimised.

Keywords: plastic injection moulding; Taguchi's orthogonal array; thermal shrinkage; warpage; impact strength; polypropylene; multi-objective genetic algorithm.

DOI: 10.1504/IJESD.2022.119389

International Journal of Environment and Sustainable Development, 2022 Vol.21 No.1/2, pp.206 - 217

Received: 02 Jan 2020
Accepted: 14 Oct 2020
Published online: 02 Dec 2021 *

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