Optimization of Process Parameters of Injection Molding with Neural Network Application in a Process Simulation Environment

Application of neural network and computer simulation to improve surface profile of injection molding optic lens

“…Moreover, this method has also experimental or measuring errors and takes long time in experiments and needs many devices for experiments, mold, injection machine and an oven maintaining the moisture of polymer, etc. On the other hand, the methods of gathering data from computer simulation using CAE program provides the advantage of overcoming the weak points of experimental methods despite the occurrence of modeling and numerical errors [9,10]. Eventually the numerical method was selected because learning data can be gathered from computation simulation on each injection molding condition.…”

A genetic algorithm-based artificial neural network model for the optimization of machining processes

“…To generate an optimum set of process parameters at the design state of injection molding, Choi et al [4] used an ANN model with inputs of filling time, melt temperature, holding time, coolant temperature and packing pressure and with outputs of melt temperature difference, mold temperature difference, over packed element, sink index and average and variance of linear shrinkage. The compensation of thermal distortion was the goal of Hatamura et al [5].…”

Review of research in the determination of process parameters for plastic injection molding

“…Figure 5 shows the structure of a neural network for the quality prediction of molded parts. To obtain the optimal parameter setting for injection molding, Choi et al quantified the quality of 60 molded parts in terms of a performance index which is essentially a function of two geometrical characteristics of a molded part, namely variance of linear shrinkage and sink index. A set of optimum process parameters can be obtained by minimizing the performance index.…”