Development and application of intelligent monitoring system for rapid tooling applied in low-pressure injection molding
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ORIGINAL ARTICLE
Development and application of intelligent monitoring system for rapid tooling applied in low-pressure injection molding Chil-Chyuan Kuo 1,2
&
Wei-Jie Chen 1
Received: 12 June 2020 / Accepted: 2 November 2020 # Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract Investment casting is widely used to manufacture a variety of metal components through wax patterns fabricated by low-pressure injection molding of wax. With the high labor cost in the precision casting industry, ensuring the quality of wax patterns during the production process is crucial to manufacturers. Accordingly, an intelligent monitoring system was developed for silicone rubber mold applied in injection molding of wax patterns. The cooling time of the injection molded part obtained by the intelligent monitoring system is very close to that obtained by the on-site data acquisition system. The relationship between the temperature of the injection molded part and the cooling time after the injection molding of wax can be monitored remotely. The developed intelligent monitoring system has the functions of identifying six process parameters, including injection temperature, coolant inlet temperature, coolant inlet coolant flow rate, injection pressure, injection time, and mold temperature. The developed intelligent monitoring system is very practical and provides good application prospects in the batch production process of wax patterns. Keywords Investment casting . Injection molding of wax . Intelligent monitoring system . Cooling time
1 Introduction The plastic and composite industries have begun to undergo dramatic changes in the areas of digitalization and systems integration [1]. Shahin et al. [2] demonstrated a comprehensive review of links between lean tools and Industry 4.0 technologies. In addition, a cloud-based Kanban decision support system was presented as a real-world demonstrator for integration of an Industry 4.0 technology and a major lean tool. Cohen et al. [3] explored three impact levels: strategic, tactical, and operational and elaborated on likely changes in assembly design aspects because of the capabilities and flexibility that these new technologies will bring. Adu-Amankwa et al. [4] proposed a predictive maintenance cost model for computer numerical control machine shops and provides a
* Chil-Chyuan Kuo [email protected] 1
Department of Mechanical Engineering, Ming Chi University of Technology, No. 84, Gungjuan Road, New Taipei City 243, Taiwan
2
Research Center for Intelligent Medical Devices, Ming Chi University of Technology, No. 84, Gungjuan Road, New Taipei City 243, Taiwan
significant cost savings in the small-medium enterprises. Cohen et al. [5] addressed the state-of-the-art readiness for Industry 4.0 concerning manufacturing and assembly systems through a literature review of the relevant papers published. In addition, this paper demonstrated valuable contributions to both theory and application in Industry 4.0. Rossit et al. [6] showed a framework based on tolerance p
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