A current state of art applied to injection moulding manufacturing process – A review

Abstract

Abstract Injection moulding (IM) appears to be one of the most important techniques for manufacturing plastics products worldwide. As IM is a complicated process, so it is technically very difficult to take into account all the changes taking place during the process. To enhance efficiency of the process and to have consistent product quality, proper input parameter’s setting is critically important. The numbers of input process parameters are: melt temperature, mold temperature, injection pressure, injection speed, injection time, holding pressure, holding time and cooling time. The responses/output may be mechanical properties, defects e.g., warpage, shrinkage, sink mark, residual thermal stress etc., and aesthetic requirement for example surface finish/roughness. Literature review shows that most of the researchers have optimized the process parameters and established a correlation between the parameters and the behavior of the material under investigation. Others performed optimization research regarding some specific areas of IM viz. cooling channel design, location of the gate, runner system design etc. Some of the studies were focused on monitoring control system to monitor the process during production to ensure quality and minimize the defects. Researchers have used many different techniques to model and optimize the IM manufacturing process. These techniques include mathematical modelling, simulation, design of experiments (DOE) and artificial intelligence (AI). Some of the examples of these techniques are Taguchi method, evolutionary algorithm (EA), artificial neural networks (ANN), genetic algorithms (GA), fuzzy logic, response surface methodology (RSM), particle swarm optimization (PSO), analysis of variance (ANOVA) etc. This paper aims to review and present the various studies concerning the process parameters and their influence over the quality of product. Throughout this review, the process parameters, response, material used and the technique applied to optimization research in the field of IM are highlighted.