Multi-respone injection moulding process parameters optimization using Taguchi method with grey relational analysis

• Main Author: Mohd Ali, Noorfa Idayu
• Format: Thesis
• Language: English; English
• Published: 2018
• Subjects: T Technology (General); TJ Mechanical engineering and machinery
• Online Access: http://eprints.utem.edu.my/id/eprint/23325/1/Multi-Response%20Injection%20Moulding%20Process%20Parameters%20Optimization%20Using%20Taguchi%20Method%20With%20Grey%20Relational%20Analysis.pdf; http://eprints.utem.edu.my/id/eprint/23325/2/Multi-respone%20injection%20moulding%20process%20parameters%20optimization%20using%20Taguchi%20method%20with%20grey%20relational%20analysis.pdf; http://eprints.utem.edu.my/id/eprint/23325/; https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=112294

Plastic injection moulding is one of the important processes to produce the plastic product with complex shape and high accuracy. The quality of the plastic product in plastic injection moulding process is affected by four factors, and that factorsare plastic materials, mould design, machine parameters and production operator. It is well known that in industry practice, machine parameters are changed by the skill of experienced operator using trial and error method. Therefore, process parameters should be optimized using the proper method such as the design of experiment (DOE). The purpose of this study is to examine the process parameters for injection moulding on material characteristics such as part weight, warpage, geometrical shrinkage and mechanical properties.In mechanical properties, ultimate tensile strength, tensile modulus and percentage of elongationwere studied. The parameters involved in this study weremould temperature, melt temperature, injection time and cooling time. Taguchi method was used where L9 with nineruns with three repetitions were conducted. The optimization is carried out in two ways by using single response and multi-response of Taguchi method based grey relational analysis (GRA)for all responses. Hence, the optimum result of the single responsefor part weight is the mould temperature which contributes 58.88%. Meanwhile, for war page, melt temperature contributes 38.96%. For shrinkage, mould temperature contributes 67.76%. For mechanical properties such as ultimate tensile strength and tensile modulus, mould temperature contributes 93.33% and 40.37%, respectively.For the percentage of elongation, melt temperature contribution is 51.41%.Multi-response optimization shows that a set of input parameters for all responses are mould temperature at 560 C,melt temperature at 250oC,injection time at 0.7s and cooling time at 15.4s.ANOVA result shows that cooling time contributes 86.76% for all responses.Therefore, the multi-response optimization can predict the quality of plastic product producedin plastic injection moulding process.