- Open Access
- Article
Cavity Sensing for Defect Prevention in Injection Molding
by Oumayma Haberchad 1,*
and Yassine Salih-Alj 2
1 Control and Instrumentation Engineering Department, College of Engineering and Physics, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Ara
2 School of Science and Engineering, Al Akhawayn University, Ifrane, 53000, Morocco
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 4, Issue 5, Page # 11-20, 2024; DOI: 10.55708/js0405002
Keywords: Injection Molding, Sensors, Data Acquisition, Process Monitoring, Quality Control
Received: 04 April 2025, Revised: 07 May 2025, Accepted: 15 May 2025, Published Online: 25 May 2025
(This article belongs to the Special Issue Special Issue on Multidisciplinary Sciences and Advanced Technology 2024 & Section Biochemical Research Methods (BRM))
APA Style
Haberchad, O., & Salih-Alj, Y. (2025). Cavity sensing for defect prevention in injection molding. Journal of Engineering Research and Sciences, 4(5), 11–20. https://doi.org/10.55708/js0405002
Chicago/Turabian Style
Haberchad, Oumayma, and Yassine Salih-Alj. 2025. “Cavity Sensing for Defect Prevention in Injection Molding.” Journal of Engineering Research and Sciences 4 (5): 11–20. https://doi.org/10.55708/js0405002.
IEEE Style
O. Haberchad and Y. Salih-Alj, “Cavity sensing for defect prevention in injection molding,” J. Eng. Res. Sci., vol. 4, no. 5, pp. 11–20, 2025, doi: 10.55708/js0405002.
Real-time monitoring of injection molding parameters plays a pivotal role in enhancing product quality, reducing defects and improving production. This study presents a cavity data acquisition system for real time monitoring of process parameters inside the mold. The system consists of non-destructive in-mold sensors that monitor the status of the melt within the cavities. Furthermore, the geometry of the injected part is taken into consideration when selecting the position of the sensors. This enables early discovery of defects by studying abnormal variations of the monitored parameters in areas where these defects are suspected. A case scenario is shown in which we simulate the molding profile of a plastic part using SolidWorks Plastics. The suggested sensors’ placements are then derived. Results indicate that the piezoelectric sensor measures with a root mean square error (RMSE) that is less than 0.0004 V and a peak error of 0.0012 V. The proposed method promises more control over injection conditions inside the mold, as well as enhanced overall production quality.
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