Calculating Molding Parameters in Plastic Injection Molds with ANN and Developing Software

Review of Factors that Affect Shrinkage of Molded Part in Injection Molding

“…Concerning different kind of polymers as semicrystalline (HDPE [54,87], PS [72,88], PP [49,52,60,71]), polymer wax [61], amorphous (ABS [60], PVC [56]), or ceramic-based feedstock (W [62], Si 3 N 4 [22]), experimental results demonstrated that shrinkage was critically influenced by melt temperature.…”

“…Often works that identified mold temperature as critical parameter, coupled this result with melt temperature: in these cases the authors talk generally of ''temperatures.'' This parameter was considered critical for amorphous (PS and PC [67], PMMA [63]) for semicrystalline polymers (PP [67], Nylon 46 [55], HDPE [87]), or for ceramic feedstock (Zr [75]), in combined effect with holding pressure.…”

Process parameter optimization of plastic injection molding: a review

“…A huge number of works applying ANN to injection molding has been reported in the specialized literature, which include the prediction of injection pressure and injection time by training and testing ANN using process dataset generated by the C-Mold ® software [120], injection pressure and injection time of metal molding process by a feed forward ANN integrated with the Gauss training method [119], nonlinear tensile modulus of injection molded polycarbonate samples as a function of process parameters [50], changes required in process parameters in order to achieve the desired final response in terms of dimensional accuracy [60], optimum control of the injection molding ram velocity using an ANN-based predictive learning controller [40], quality of molding process and machine parameters using an ANN-based flash monitoring system integrated with vibration monitoring and threshold prediction based on process parameter settings [16], process parameter based shrinkage using a forward mapping ANN model and shrinkage based optimal set of process parameters using a reverse mapping ANN model [73], optimum molding parameters for minimum defects in molded parts [89], better influence of process parameters on shrinkage in comparison to Moldflow ® [115], etc. Due to their fast response and higher accuracy, the back propagation neural networks (BPNN) is also widely preferred in injection molding [11,72], e.g., computation of mold complexity index based on difficulty in manufacturing injection molds [95], reduction in time requirement for planning and optimizing injection molding process parameters by supporting BPNN with experimental data [97], BPNN trained and tested with data generated by the Taguchi method for more accurate and effective prediction of warpage and shrinkage behaviour of injection molded thin walled parts than those of C-Mold ® software and Taguchi method [67], BPNN trained and tested with data generated by Moldflow ® for evaluating the optimal set of process parameters and warpage of injection molded automobile glove compartment by deriving relationships among them [123], etc.…”

Evaluating the Suitability of Alumide Tooling for Injection Moulding of Different Polymers

“…Almost every product used daily consists of some, if not all, plastic components. More than 70 per cent of components in consumer products are manufactured using plastic materials [4]. Most of these plastic products are produced through the plastic injection moulding (IM) process [5].…”