An Intelligent Manufacturing System for Injection Molding

  • Wen-Chin Chen Department of Industrial Management Chung Hua University
  • Manh-Hung Nguyen Program of Technology Management, Chung Hua University
  • Pei-Hao Tai Department of Industrial Management Chung Hua University
Keywords: PIM, industry 4.0, IoT, big data, cloud computing, IMS, BPNN, modified PSO-GA


In recent years, the great trends of industry 4.0, internet of things (IoT), big data analytics, and cloud computing, the design and development of plastic injection molding (PIM) products has been more requested to achieve the requirements of light, thin, short, small, multi-function, high-precision, energy-saving, and obliged to fulfill a large number of customized production. To tackle this arduous challenge, effectively developing a novel PIM intelligent manufacturing system will play a crucial role. The aim of the proposed study is to carry on building an intelligent manufacturing system (IMS) for PIM industry, which is composed of three subsystems: a multiple response optimization systems of PIM, a database management system of process parameters, and a PIM real-time monitoring and control system. Firstly, the multiple response optimization systems present an intelligent optimization system to find optimal process parameters of multiple quality characteristics in the PIM process. Secondly, the database management system allows for saving the experimental data, PIM process parameter settings and quality goals. The third is a PIM real-time monitoring and control system, which establishes a graphic monitoring and control interface to real-time monitor the parameters of PIM machine and the optimal process parameter settings. The proposed PIM intelligent manufacturing systems enable the functions of real-time monitoring, process parameter optimization and database management, which can assure better PIM product quality and yield rate, effectively reduce the manufacturing cost, and promote the competition of the PIM industry in the future.


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How to Cite
W.-C. Chen, M.-H. Nguyen, and P.-H. Tai, “An Intelligent Manufacturing System for Injection Molding”, Proc. eng. technol. innov., vol. 8, pp. 09-14, Apr. 2018.