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Emerging approaches to process control in injection molding: a comprehensive review.

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Title: Emerging approaches to process control in injection molding: a comprehensive review.
Authors: Cao, Peng1 (AUTHOR) 2023210439@buct.edu.cn, Xie, Jinzhao1 (AUTHOR) xiejz@mail.buct.edu.cn, Ma, Yitao1 (AUTHOR) yitaoma@outlook.com, Zhao, Bing1 (AUTHOR) 2023210436@buct.edu.cn, Yang, Weimin1,2 (AUTHOR) yangwm@mail.buct.edu.cn, Xie, Pengcheng1,2 (AUTHOR) xiepc@mail.buct.edu.cn
Source: International Journal of Advanced Manufacturing Technology. Oct2025, Vol. 140 Issue 9/10, p4483-4502. 20p.
Subjects: Injection molding, Computer-aided engineering, Artificial neural networks, Optimization algorithms, Machine learning, Genetic algorithms, Process control systems
Abstract: Injection molding process control, a vital technology in modern manufacturing, has seen substantial advancements in recent years through the integration of Computer-Aided Engineering (CAE) technology and intelligent optimization algorithms. CAE technology plays a crucial role in optimizing process parameters and mold design by simulating key behaviors in the injection molding process, including flow, cooling, shrinkage, and warpage. However, complex process conditions and nonlinear material behaviors pose limitations on the control precision and real-time performance of traditional CAE methods. To address these challenges, intelligent optimization algorithms—such as machine learning, Genetic Algorithms (GAs), and neural networks—have been introduced into the field of injection molding process control, enhancing the accuracy and efficiency of parameter prediction, defect detection, and adaptive control. This paper reviews recent advancements in the application of CAE technology within injection molding, alongside developments in intelligent algorithms for process optimization, parameter control, and predictive model construction. We further discuss the impact and future trends of combining CAE technology and intelligent algorithms on injection molding process control. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Advanced Manufacturing Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Emerging approaches to process control in injection molding: a comprehensive review.
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  Data: <searchLink fieldCode="AR" term="%22Cao%2C+Peng%22">Cao, Peng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 2023210439@buct.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Xie%2C+Jinzhao%22">Xie, Jinzhao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> xiejz@mail.buct.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Ma%2C+Yitao%22">Ma, Yitao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> yitaoma@outlook.com</i><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Bing%22">Zhao, Bing</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 2023210436@buct.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Yang%2C+Weimin%22">Yang, Weimin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> yangwm@mail.buct.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Xie%2C+Pengcheng%22">Xie, Pengcheng</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> xiepc@mail.buct.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Advanced+Manufacturing+Technology%22">International Journal of Advanced Manufacturing Technology</searchLink>. Oct2025, Vol. 140 Issue 9/10, p4483-4502. 20p.
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  Data: <searchLink fieldCode="DE" term="%22Injection+molding%22">Injection molding</searchLink><br /><searchLink fieldCode="DE" term="%22Computer-aided+engineering%22">Computer-aided engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Process+control+systems%22">Process control systems</searchLink>
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  Label: Abstract
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  Data: Injection molding process control, a vital technology in modern manufacturing, has seen substantial advancements in recent years through the integration of Computer-Aided Engineering (CAE) technology and intelligent optimization algorithms. CAE technology plays a crucial role in optimizing process parameters and mold design by simulating key behaviors in the injection molding process, including flow, cooling, shrinkage, and warpage. However, complex process conditions and nonlinear material behaviors pose limitations on the control precision and real-time performance of traditional CAE methods. To address these challenges, intelligent optimization algorithms—such as machine learning, Genetic Algorithms (GAs), and neural networks—have been introduced into the field of injection molding process control, enhancing the accuracy and efficiency of parameter prediction, defect detection, and adaptive control. This paper reviews recent advancements in the application of CAE technology within injection molding, alongside developments in intelligent algorithms for process optimization, parameter control, and predictive model construction. We further discuss the impact and future trends of combining CAE technology and intelligent algorithms on injection molding process control. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of International Journal of Advanced Manufacturing Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00170-025-16577-6
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        Text: English
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      – SubjectFull: Injection molding
        Type: general
      – SubjectFull: Computer-aided engineering
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      – SubjectFull: Artificial neural networks
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      – SubjectFull: Optimization algorithms
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      – SubjectFull: Machine learning
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      – SubjectFull: Genetic algorithms
        Type: general
      – SubjectFull: Process control systems
        Type: general
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      – TitleFull: Emerging approaches to process control in injection molding: a comprehensive review.
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            NameFull: Cao, Peng
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            NameFull: Yang, Weimin
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              Text: Oct2025
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