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.
Cavity pressure is one of the best indicators of injection molding conditions and thus has been used for quality prediction in the injection molding process. Also, the repeatability of the cavity pressure profile at each shot indicates the consistency of the part quality, which is easily affected by environmental changes, such as barrel temperature. To maintain quality consistency (such as part weight and geometrical dimensions) during mass production, this study proposed a novel method of the holding pressure adjustment to control the deviation in the cavity pressure distribution during each shot. Injection molding of a thin‐walled dumbbell‐shaped sample was performed to verify the proposed process, which proved the feasibility of this method for suppressing the influence of the barrel temperature changes on part quality.
Conventional methods for assessing the quality of components mass produced using injection molding are expensive and time-consuming or involve imprecise statistical process control parameters. A suitable alternative would be to employ machine learning to classify the quality of parts by using quality indices and quality grading. In this study, we used a multilayer perceptron (MLP) neural network along with a few quality indices to accurately predict the quality of “qualified” and “unqualified” geometric shapes of a finished product. These quality indices, which exhibited a strong correlation with part quality, were extracted from pressure curves and input into the MLP model for learning and prediction. By filtering outliers from the input data and converting the measured quality into quality grades used as output data, we increased the prediction accuracy of the MLP model and classified the quality of finished parts into various quality levels. The MLP model may misjudge datapoints in the “to-be-confirmed” area, which is located between the “qualified” and “unqualified” areas. We classified the “to-be-confirmed” area, and only the quality of products in this area were evaluated further, which reduced the cost of quality control considerably. An integrated circuit tray was manufactured to experimentally demonstrate the feasibility of the proposed method.
This paper reports a novel hot embossing technique using rapid heating and uniform pressure for replication of microstructures on polymeric substrates.
Many components need microstructures on both upper and lower surfaces for integrating and enhancing functions. For the replication of microstructures on the polymeric substrate, hot embossing is an inexpensive and flexible method. However, the cycle time is too long and the embossing pressure is not uniform. This study is devoted to developing an innovative hot embossing system, which integrates induction heating and gas-assisted pressuring for the imprinting of double-sided microstructures. In this study, a wrapped coil for induction heating was designed, implemented, and tested. Then, an apparatus with wrapped coils for induction heating and gas pressuring for hot embossing was designed and constructed in a chamber. Experiments showed that the cycle time can be reduced to 4 min. V-cut patterns and microlens array had been successfully replicated on both surfaces of the polycarbonate substrates. The replication rates were above 95%. This study proves the potential of induction heating gas-assisted embossing for rapid replication of double-sided microstructures for industrial applications.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.