Enhancing Injection Molding Quality through Scientific Molding and Adaptive Process Control with External Sensors
Abstract
The injection molding process is crucial in manufacturing industries, particularly for producing complex, high-volume plastic parts. Ensuring high-quality output and reducing defects in the final product remain key challenges in injection molding. Scientific molding, coupled with adaptive process quality control using external sensors, provides an effective solution. This study explores the integration of external sensors in the injection molding process to enable real-time monitoring and adaptive control of critical parameters. The research investigates the impact of sensor-based feedback on the accuracy of process control, reduction in defects, and overall production efficiency. The findings show that the combination of scientific molding principles and external sensors results in significant improvements in product quality and process optimization, providing valuable insights into future advancements in the injection molding industry.
Keywords
Injection molding, scientific molding, adaptive process controlHow to Cite
References
Gao, R.X.; Tang, X.; Gordon, G.; Kazmer, D.O. Online product quality monitoring through in-process measurement. CIRP Ann. 2014, 63, 493–496. [Google Scholar] [CrossRef]
Su, C.W.; Su, W.J.; Cheng, F.J.; Liou, G.Y.; Hwang, S.J.; Peng, H.S.; Chu, H.Y. Optimization process parameters and adaptive quality monitoring injection molding process for materials with different viscosity. Polym. Test. 2022, 109, 107526. [Google Scholar] [CrossRef]
Tsou, H.H.; Huang, C.C.; Chen, Y.C.; Shih, S.Y. Online detection of residual stress near the gate using cavity pressure for injection molding. J. Polym. Eng. 2023, 43, 89–99. [Google Scholar] [CrossRef]
Ke, K.C.; Huang, M.S. Quality prediction for injection molding by using a multilayer perceptron neural network. Polymers 2020, 12, 1812. [Google Scholar] [CrossRef]
Su, W.J.; Peng, H.S. A real-time clamping force measurement eigenvalue for prediction, adjustment, and control of injection product quality. Polym. Eng. Sci. 2020, 61, 420–431. [Google Scholar] [CrossRef]
Chang, Y.H.; Chen, S.C.; Ting, Y.H.; Feng, C.T.; Hsu, C.C. The Investigation of Novel Dynamic Packing Technology for Injection Molded Part Quality Control and Its Production Stability by Using Real-Time PVT Control Method. Polymers 2022, 14, 2720. [Google Scholar] [CrossRef] [PubMed]
Chen, J.Y.; Wong, L.C.; Huang, M.S. Quality monitoring and control for plasticization of acrylonitrile-butadiene-styrene regrind polymer in injection molding. Polym. Eng. Sci. 2024, 64, 1057–1070. [Google Scholar] [CrossRef]
Chen, J.Y.; Yang, K.J.; Huang, M.S. Online quality monitoring of molten resin in injection molding. Int. J. Heat Mass Transf. 2018, 122, 681–693. [Google Scholar] [CrossRef]
Liou, G.Y.; Su, W.J.; Cheng, F.J.; Chang, C.H.; Tseng, R.H.; Hwang, S.J.; Peng, H.S.; Chu, H.Y. Optimize Injection-Molding Process Parameters and Build an Adaptive Process Control System Based on Nozzle Pressure Profile and Clamping Force. Polymers 2023, 15, 610. [Google Scholar] [CrossRef
License
Copyright (c) 2025 Zhao Yi
This work is licensed under a Creative Commons Attribution 4.0 International License.
