Injection Parameter Optimization Of Car Headlights On CAE Technology

With the increasing market demand, injection molding has been widely used in the fields of transportation, communication, health care, aerospace and etc. The requirements on product’s quality have been increasing recently. The entire production process of injection molding is very complicated. Solidification and fluid status of the melt in cavity, and product quality are closely dependent on the process parameters. It is possible to adjust the parameters based on real product samples by using trial-and-error method. However, this approach has limited controls on tested parameters and also increases the cost due to increased production cycle duration. Therefore, it is crucial to investigate on optimizing process parameters and setting the optimal set of parameter values in order to lower cost, reduce duration of production cycle and improve quality.This paper focused on process parameters of car headlights produced by a company via injection molding method. The optimal set of parameter values obtained via parametric optimization and warp distortion and sink mark depth occurring in the production of headlights were improved. The main contents in this paper are as following:1. The effects of different parameters on warp distortion and sink mark depths were investigated. Data regarding warp distortion and sink mark depths were obtained via establishing simulation model of car headlights, and using the software Moldflow to simulate the process by applying the tested ranges of parameters. The significance of parameter’s effects on target factors was evaluated based on variance analyses of the modeling results. It was concluded that when warp distortion and sink mark depths were set as target factors, the most important process parameters included packing pressure, pressure holding time, injection time, and V/P switch.2. Further investigations included expanding the study scope of important factors via Taguchi experiments, conducting signal-to-noise analyses and range analysis. The order of parameters, from high to low effects on warp distortion, were determined as packing pressure, pressure holding time, injection time, and V/P switch. The order of parameters, from high to low effects on sink mark depths, were determined as packing pressure, V/P switch, injection time, and pressure time. Preliminary optimization determined an optimal set of parameters, inducing a distortion value of 1.828 mm and a sink mark depth of 0.0412mm, meeting the initial objective of the study.3. An artificial neural network that used process parameters as input and target factors as output was established by using Matlab software. The Matlab-established model was based on the process parameters obtained from artificial neural network. The experimental data from Taguchi experiments served as testing samples. On the basis of preliminarily optimization parameters, a new orthogonal arrays was established based on trying values around the parameter values. The neural network was used to replace the Moldflow software to calculate warp distortion and sink mark depth. The order of effects on target factors was determined by signal-to-noise analyses. Therefore, a final optimal set of process parameters was obtained based on evaluating the effects on warp distortion and sink mark depth. By using Moldflow software to analyze the final optimal set of parameters, it was obtained that the distortion value is 1.279 mm and the sink mark depth of 0.0251mm.This paper details a study to obtain the optimal set of process parameters for injection molding process based on orthogonal experiments, numerical simulation and neural network. Besides insurance of calculation accuracy, the set of optimized parameters shortens production cycle duration, reduces costs, enhance efficiency and solve practical production problems.