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Sustainability
Volume 13
Issue 8
10.3390/su13084120
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Article

Application of Machine Learning Techniques in Injection Molding Quality Prediction: Implications on Sustainable Manufacturing Industry

by
Hail Jung
1,*,
Jinsu Jeon
2,
Dahui Choi
2 and
Jung-Ywn Park
3
1
School of Management Engineering, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Ulsan 44919, Korea
2
Graduate School of Interdisciplinary Management, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Ulsan 44919, Korea
3
Graduate School of Technology and Innovation Management, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Ulsan 44919, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Nir Kshetri
Sustainability 2021, 13(8), 4120; https://doi.org/10.3390/su13084120
Received: 9 March 2021 / Revised: 1 April 2021 / Accepted: 6 April 2021 / Published: 7 April 2021
(This article belongs to the Special Issue Fourth Revolution and Sustainability)
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Abstract

With sustainable growth highlighted as a key to success in Industry 4.0, manufacturing companies attempt to optimize production efficiency. In this study, we investigated whether machine learning has explanatory power for quality prediction problems in the injection molding industry. One concern in the injection molding industry is how to predict, and what affects, the quality of the molding products. While this is a large concern, prior studies have not yet examined such issues especially using machine learning techniques. The objective of this article, therefore, is to utilize several machine learning algorithms to test and compare their performances in quality prediction. Using several machine learning algorithms such as tree-based algorithms, regression-based algorithms, and autoencoder, we confirmed that machine learning models capture the complex relationship and that autoencoder outperforms comparing accuracy, precision, recall, and F1-score. Feature importance tests also revealed that temperature and time are influential factors that affect the quality. These findings have strong implications for enhancing sustainability in the injection molding industry. Sustainable management in Industry 4.0 requires adapting artificial intelligence techniques. In this manner, this article may be helpful for businesses that are considering the significance of machine learning algorithms in their manufacturing processes. View Full-Text
Keywords: injection molding; quality prediction; regression; decision tree; autoencoder; machine learning; feature importance; characteristics importance injection molding; quality prediction; regression; decision tree; autoencoder; machine learning; feature importance; characteristics importance
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MDPI and ACS Style

Jung, H.; Jeon, J.; Choi, D.; Park, J.-Y. Application of Machine Learning Techniques in Injection Molding Quality Prediction: Implications on Sustainable Manufacturing Industry. Sustainability 2021, 13, 4120. https://doi.org/10.3390/su13084120

AMA Style

Jung H, Jeon J, Choi D, Park J-Y. Application of Machine Learning Techniques in Injection Molding Quality Prediction: Implications on Sustainable Manufacturing Industry. Sustainability. 2021; 13(8):4120. https://doi.org/10.3390/su13084120

Chicago/Turabian Style

Jung, Hail, Jinsu Jeon, Dahui Choi, and Jung-Ywn Park. 2021. "Application of Machine Learning Techniques in Injection Molding Quality Prediction: Implications on Sustainable Manufacturing Industry" Sustainability 13, no. 8: 4120. https://doi.org/10.3390/su13084120

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