A data-driven predictive maintenance framework for injection molding process. (August 2022)

• Record Type: Journal Article
• Authors: Farahani, Saeed; Khade, Vinayak; Basu, Shouvik; Pilla, Srikanth
• Is Part Of: Journal of manufacturing processes. Volume 80(2022)
• Page Start: 887
• Page End: 897
• Publication Date: 2022-08
• Subjects: Injection molding -- Predictive maintenance -- Cloud computing -- Edge computing; Production management -- Data processing -- Periodicals; Manufacturing processes -- Periodicals; Procestechnologie; Productietechniek; Production -- Gestion -- Informatique -- Périodiques; Fabrication -- Périodiques; Manufacturing processes; Production management -- Data processing; Periodicals; 670.5
• DOI: 10.1016/j.jmapro.2022.06.013
• Languages: English
• ISSNs: 1526-6125

Abstract

Abstract: 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. Highlights: An Industry 4.0 predictive maintenance framework is introduced for injection molding. Both edge and cloud computing tools are explored for data integration and analysis. A case study is conducted to detect cooling issues using machine and in-mold data. A reasonable prediction accuracy is achieved with an average error of 3.29 %.