The System-based Optimization Of The Injection Molding Process And Its Parallel Computing

Plastic injection molding (PIM) has been widely used in high-tech industries, such as the3C (Computer, Communication, and Consumer-Electronics), automotive and medicinal industries. The quality issues of the product (the shrinkage, the warpage, the residual stress and the weld lines) and the energy cost of the productive process are the main concerns in the design of the PIM process. The traditional ways of mold design and selecting the processing parameters are mainly depended on the experience of the designer and engineers. The designing process may be very expensive and time consuming. Optimizing the mold design and molding process with computer aided engineering (CAE) technology has been proved as an effective way to improve the quality of products. However, because of the complexity of the molding process and the design parameters, considering all the design parameters simultaneously in the optimization problem often involves huge computational effort and lead to a highly demand for the optimizing method.A system-based optimization model of injection process is proposed. The optimization of the mold and molding process is divided into4sub-systems:the filling sub-system, the cooling sub-system, the packing-subsystem and the system-level problem. Each sub-system has its own objective and design parameters, and objectives of the first three sub-systems can be evaluated by only one sub-model of the simulation program. A coupling optimization strategy is used to solve this system-based model. Comparing with the traditional models, the system-based model is able to consider more design parameters and objectives. The numerical results show that the system-based model has much higher computing efficiency.A modified Gaussian process surrogate-model based optimization method (GPSBOM) is developed to solve the optimization problem in each sub-system, in which a new infill sample criteria (ISC) and a Gaussian process (GP) model for the gate location optimization problem are also involved.The balancing between the global exploration and local exploitation has been a main concern in GPSBOM. A weighting function is introduced to improve the "expected improvement (EI)" method, and a new ISC named weighting-integral expected improvement (WIEI) is proposed. The WIEI provides a high flexibility to control the search scope. By defining an equivalent optimization problem, the WIEI functions with different search scopes are condensed into one ISC which is able to select a proper search scope based on the complexity of the optimization problem.For the gate location optimization problem, the "flow path" is introduced to calculate the correlation model between different locations. With this correlation model, the GP surrogate-model of the gate location can be established. The ISC mentioned above can be used in this GP model in the GPSBOM. This proposed gate location optimization method has three advantages:the objectives can be selected based on the demand, it can be applicable to the gate location design with the complexity of the product’s geometry, and can be solved by GPSBOM efficiently.The parallel computing methods for the optimization process are also studied, that a parallel GPSBOM is developed, and the simulation of filling process is also parallelized.By introducing the concept of "mutual information", a parallel ISC named EI&MI is developed. The EI&MI considers both the El value of each new sample and the total information of them all. Because that establishing the GP model with a large number of samples may lead to huge computational effort, a domain decomposition optimization strategy is proposed in which the design space is divided into several sub-spaces by primary component analysis method. Then in each sub-space the GP model is established based on the inner and neighboring samples, and the new samples can be searched based on the EI&MI. The numerical results show that the proposed parallel optimization method is able to solve the very complex problem efficiently.The parallel computing method of filling process is studied. First, based on the cluster method, a domain decomposition algorithm is developed to divide the computation domain into several sub-domains. A domain decomposition model of both "load balance" and "boundary control" is proposed to improve the parallel efficiency and reduce the communication amount. The system equations of each sub-domain can be assembled parallelized. Then by classifying the nodes, a parallel successive over relaxation (SOR) algorithm is developed to solve the system equations. Numerical results show that the method gives a high efficiency, and it is suitable for numerically simulating the injection molding filling process.The author gratefully acknowledges financial support for this work from the Major program (No.10590354) and the National Natural Science Foundation of China and the National Basic Research Program of China (No.2012CB025905).