A Data-Driven Predictive Maintenance Framework for Injection Molding Process

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Journal of Manufacturing Processes


Injection molding is the most common process to produce a wide range of complex plastic parts for many different applications, and a large number of machines and devices used in the plastics industry are associated with this process. Maintenance instructions and procedures used in the majority of injection molding plants currently are based on reactive and/or preventive strategies such as replacing failed components and/or performing regularly scheduled maintenance. However, such strategies are not cost-efficient and only partially effective in preventing machine downtime or producing scraps. The emergence of Industry 4.0 related technologies, such as cyber-physical systems, Internet of Things (IoT), cloud and edge computing, new sensors, and vision-based systems, brings new opportunities for the plastics industry to enhance their production and enterprise systems. Developing data-driven, predictive maintenance systems is one such opportunity that can help injection molding companies significantly reduce their maintenance cost while increasing their product quality and production efficiency. Accordingly, in this work, we introduce a generalized framework for implementation of predictive maintenance in injection molding process by integrating a variety of different data sources available in this process and taking the advantage of both cloud and edge computing. To demonstrate this framework, a case study on monitoring of the cooling system in injection molding process is presented. The results show the effectiveness of this approach in detecting cooling issues by monitoring other process data that are not directly correlated to the mold temperature. The comparison of the predicted mold temperature with the respective sensor value demonstrates an average error of 3.29 %, which can gradually be improved by accumulating more training data in the cloud-based system.


The authors wish to acknowledge the financial support of the South Carolina Research Authority (SCRA) under the SCRA-Academic Collaboration Team Feasibility Grants (Award# 2012732) and Clemson Forward R-Initiatives Program: Clemson Research Fellows.