Faculty Publications
Enhancement Of Quality Of Polypropylene By Optimisation Of Injection Moulding Parameters With Genetic Algorithm
Document Type
Conference
Keywords
Impact strength, Multi-objective genetic algorithm, Plastic injection moulding, Polypropylene, Taguchi's orthogonal array, Thermal shrinkage, Warpage
Journal/Book/Conference Title
International Journal of Environment and Sustainable Development
Volume
21
Issue
1-2
First Page
206
Last Page
217
Abstract
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.
Department
Department of Applied Engineering and Technical Management
Original Publication Date
1-1-2022
DOI of published version
10.1504/IJESD.2022.119389
Recommended Citation
Kumar, Deepak; Dangayach, G. S.; and Rao, P. N., "Enhancement Of Quality Of Polypropylene By Optimisation Of Injection Moulding Parameters With Genetic Algorithm" (2022). Faculty Publications. 5178.
https://scholarworks.uni.edu/facpub/5178