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Open AccessArticle

Quality Classification of Injection-Molded Components by Using Quality Indices, Grading, and Machine Learning

Department of Mechatronics Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City 824, Taiwan
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Author to whom correspondence should be addressed.
Academic Editors: Hiroshi Ito, Kentaro Taki and Shih-Jung Liu
Polymers 2021, 13(3), 353; https://doi.org/10.3390/polym13030353
Received: 9 December 2020 / Revised: 9 January 2021 / Accepted: 19 January 2021 / Published: 22 January 2021
(This article belongs to the Special Issue Precise Polymer Processing Technology)
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. View Full-Text
Keywords: injection molding; cavity pressure curve; machine learning; multilayer perceptron neural network; quality indices; quality control injection molding; cavity pressure curve; machine learning; multilayer perceptron neural network; quality indices; quality control
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MDPI and ACS Style

Ke, K.-C.; Huang, M.-S. Quality Classification of Injection-Molded Components by Using Quality Indices, Grading, and Machine Learning. Polymers 2021, 13, 353. https://doi.org/10.3390/polym13030353

AMA Style

Ke K-C, Huang M-S. Quality Classification of Injection-Molded Components by Using Quality Indices, Grading, and Machine Learning. Polymers. 2021; 13(3):353. https://doi.org/10.3390/polym13030353

Chicago/Turabian Style

Ke, Kun-Cheng; Huang, Ming-Shyan. 2021. "Quality Classification of Injection-Molded Components by Using Quality Indices, Grading, and Machine Learning" Polymers 13, no. 3: 353. https://doi.org/10.3390/polym13030353

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