Changyu, S.; Lixia, W.; Qian, L. Optimization of injection, moulding process parameters using combination of artificial. The program reached results by apply-, ing the weights found to the input parameter during the, test process.
The LM, algorithm trains the weights by using the Hessian, matrix. The developed (NPP)-Software was trained and tested using the Levenberg--Marquardt (LM) algorithm, the ANN. Through minimization of the injection pressure, a uniform mold fill pattern can be achieved, and, thus, the probability of overpacking is reduced. or existence of short shot or correct phenomenon. The. However, the quality of the final part may, not only be limited to warpage or shrinkage. Therefore, under the influence of nonlinear and multi-factor, injection molding goal is to effectively predict and guarantee the quality of injection molded parts. On the other hand, plastic, injection molding parameters directly affect the cost of, molding plastic products, which is the reason why the, developed (NPP)-Software is thought to be important, The primary objective of this study was to develop a, (NPP)-Software to predict the quality of the injected, parts through an ANN model. Figure 3 illustrates a sample of the cable clamp. Details about the ANN architecture, and the training and testing of these models are explained. One-thousand three-hunderd pieces of data were collected, out of which 250 were used to train the network. Again in the past, a large number of models such as response surface methodology, artificial neural network (ANN) models, and Taguchi method have been used to evaluate the performance characteristics and process parameters [10][11][12], ... Local temperature and cavity pressure is essential parameters for controlling dimensional accuracy and variations in wax injection process, As the demand of the automotive and aerospace industries for lightweight and cost effective materials increases, it is necessary to combine different materials with respect to their lightweight and functional properties. The use of a vacuum system also yielded better results. In this study, a new package program (NPP)-Software that calculates various injection molding parameters was developed to mold plastic products obtained by plastic injection molding techniques using the model of artificial neural network (ANN). Therefore, the tolerance of the part during the.
2 0 obj 385 shell plastic in the plastic injection moulding. Shi, F.; Lou, Z.L. It is imperative to state that a precise optimization of various input parameters is paramount for achieving the desired quality indices. It is measured in, be reached depends on the molding material used, the, units the molten polymer needs to flow through towards, the molding cavities, and the speed of the screw, which is, at the same time determined by the characteristics of the, hydraulic system used [30, 31]. Karatas, a newly developed formula that was based on various, injection parameters in order to determine the yield length, in plastic molding of commercial plastics; the ones most. The properties of the, HDPE used in this study have a specific gravity, melt. were recorded from the screen of the injection molding, machine. Proceedings of World Academy of Science, Engineering and, process conditions over the determination of Kt curves for. The predicted values were in good agreement with the experimental ones, which indicates that the developed particle-swarm-optimization-based fuzzy expert system can be effectively used to predict the mechanical properties of molded parts. Technological advancements have led to an increase in demand for fabrication of small and cheap miniature components or parts, especially in the medical and electronic fields. tal to the quality and accuracy of the products. in price and dimensional stability, for molding dental scaler tip which has complex geometry. It compared obtained results with the actual, results and expressed them as graphs. This is attributed to the application of robust design and process development. applications with unsupervised neural networks. The, More Case Studies Regarding the Accuracy of the, One study may not be enough to measure the accuracy, of the software; therefore, different applications were.
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