Injection molding has been widely used in the mass production of high-precision products. The finished products obtained through injection molding must have a high quality. Machine parameters do not accurately reflect the molding conditions of the polymer melt; thus, the use of machine parameters leads to erroneous quality judgments. Moreover, the cost of mass inspections of finished products has led to strict restrictions on comprehensive quality testing. Therefore, an automatic quality inspection that provides effective and accurate quality judgment for each injection-molded part is required. This study proposes a multilayer perceptron (MLP) neural network model combined with quality indices for performing fast and automatic prediction of the geometry of finished products. The pressure curves detected by the in-mold pressure sensor, which reflect the flow state of the melt, changes in various indicators and molding quality, were considered in this study. Furthermore, the quality indices extracted from pressure curves with a strong correlation with the part quality were input into the MLP model for learning and prediction. The results indicate that the training and testing of the first-stage holding pressure index, pressure integral index, residual pressure drop index and peak pressure index with respect to the geometric widths were accurate (accuracy rate exceeded 92%), which demonstrates the feasibility of the proposed method.

https://www.mdpi.com/2073-4360/12/8/1812/pdf

Quality Prediction for Injection Molding by Using a Multilayer Perceptron Neural Network.

The quality control of plastic products is an essential aspect of the plastic injection molding (PIM) process. However, the warpage and shrinkage deformations continue to exist because the PIM process is easily interfered with by several related or independent process parameters. Thus, great efforts have been devoted to optimizing process parameters to minimize the warpage and shrinkage deformations of products during the last decades. In this review, we begin by introducing the manufacturing process in PIM and the cause of warpage and shrinkage deformations, followed by the mechanism about how process parameters, like mold temperature, melt temperature, injection rate, injection pressure, holding pressure, holding and cooling duration, affect those defects. Then, we summarize the recent progress of the design of experiments and four advanced methods (artificial neural networks, genetic algorithm, response surface methodology, and Kriging model) on optimizing process parameters to minimize the warpage and shrinkage deformations. In the end, future perspectives of quality control in injection molding machines are discussed. 

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Recent progress in minimizing the warpage and shrinkage deformations by the optimization of process parameters in plastic injection molding: a review

Cavity pressure‐based holding pressure adjustment for enhancing the consistency of injection molding quality

Determination of process parameters based on cavity pressure characteristics to enhance quality uniformity in injection molding

Filling-to-packing switchover (also called V/P switchover) is critical for assuring injection molding quality. An improper V/P switchover setting may result in various defects of injection-molded parts, such as excessive residual stress, flash, short shot, and warpage, etc. To enhance a consistent molding quality, recent V/P switchover approaches adopt cavity pressure profiles requiring sensors embedded in mold cavities, which is invasive to mold cavities and more expensive. Instead of using cavity pressure sensors, by working with the most popular screw position switchover control, this study hereby proposes a novel approach of tuning V/P switchover timing using a tie-bar elongation profile. In this investigation, a dumbbell testing specimen mold is applied to verify the feasibility of the method proposed. The results show that the mold filling and packing stages can be observed along the tie-bar elongation profile, detected by mounting strain gauges on the tie bars. Also, the characteristics of the cavity pressure are similar to those of the tie-bar elongation profile under a proper clamping force condition. Moreover, the varying process parameter settings which include injection speed, V/P switchover point, and holding pressure, can be reflected in these profiles. By extracting their characteristics, the application of the V/P switchover is proved to be realistic. This research conducted an experiment to verify the proposed V/P switchover decision method based on the tie-bar elongation profile. The result showed that the fluctuation of the part’s weight corresponding to a slight change of the barrel’s temperature from 210 °C to 215 °C can be successfully controlled with this method. Besides, the maximum clamping force increment extracted from the tie-bar elongation profile was found to be a good indicator for online monitoring of the reground material variation.

https://www.mdpi.com/2073-4360/11/7/1168/pdf

Tie-Bar Elongation Based Filling-To-Packing Switchover Control and Prediction of Injection Molding Quality

Quality control is a crucial issue in the injection molding process with target of obtaining a high yield rate and reducing production cost. Consequently, effective methods for monitoring the injection conditions (e.g., pressure, velocity, and temperature) in real-time and adjusting these conditions adaptively as required to ensure a consistent part quality are essential. This study proposes a quality index based on the clamping force increment during the injection cycle, as determined by four strain gauges attached to the tie bars of the injection molding machine. Also, various quality indexes for online quality monitoring and prediction purposes based on the pressure, viscosity, and energy features extracted from the pressure profiles obtained at the load cell, nozzle, and molding cavity, respectively, are compared. The feasibility of the proposed quality indexes is investigated experimentally for various settings of the barrel temperature, back pressure, and rotational speed of the plasticizing screw. It is shown that all of the quality indexes are correlated with the injection-molded quality and hence provide a feasible basis for the realization of an on-line quality monitoring and control system. Particularly, the tie-bar elongation quality index requires no modification or invasion of the injection molding system or cavity and hence provides a particularly attractive solution for monitoring and controlling the part quality.

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Chien-Chou Tseng

Papers

Quality Indexes Design for Online Monitoring Polymer Injection Molding