Optimization of the Micro-Injection Molding Process using Grey Relational Analysis and MoldFlow Analysis

Abstract

Microsystem technology is a compound technology including optic, mechanism, electricity, material, control and chemistry, etc. This technology can miniaturize products and increase their function, quality, trustworthiness and addendum. Micro-injection molding is a branch of the micro-electro mechanical system. The size of the product is in nanometers. It is important to produce the product for micro-injection molding technology today. The research of micro-injection molding is still in the early stages. In this study, numerical simulations of three-dimensional micro-injection moldings were performed. The governing differential equations were described by using control volume finite element method. The analysis with different polymers (such as PP, PC, PS, POM) and process parameters (injection time, mold temperature, injection temperature, injection pressure) are used to simulate the microgear for example. In order to obtain optimum results, the simulation introduces the Taguchi method and grey relational analysis to discuss the influence of each parameter in microinjection molding. The results show that the PS material is the most suitable material used in this study. The results for Mold Flow analysis and grey relational analysis are the same on this study.

Micro-injection moulding of polymer microfluidic devices

“…Several other simulation experiments were also reported in the literature (Yu et al 2004;Shen and Wu 2002;Young 2005;Mehta et al 2003;Shen et al 2004;Majmundar et al 2005;Sammoura et al 2007).…”

Cavity pressure based grey prediction of the filling-to-packing switchover point for injection molding

“…The volumetric-filling point exhibits a significant abrupt rise in pressure, which can be easily observed by the first derivative of cavity pressure with respect to time. The grey predictor design in this research is based on a GM(1,1) grey model, featured by its light computational burden and common use in grey forecasting [13][14][15].…”

Practical Applications of Taguchi Method for Optimization of Processing Parameters for Plastic Injection Moulding: A Retrospective Review

“…The findings showed that the product quality has been improved by 20% at the optimal parameter setting. With the similar integration method, [54] performed experiments on four common polymers (PP, PC, PS, and POM), comparing the effects that moulding process parameters had on their degree of warpage formation, pressure distribution, temperature distribution, and fill time of the parts. PS was found to have the smallest temperature difference which causes smaller warpage and shrinkage.…”