Injection moulding is commonly used in production processes for the production of plastic parts with a broad lot size, so continual improvements in the performance of product quality are important for maintaining a competitive advantage in the injection moulding industry. The presence of any difference impacts productivity for the whole lot. Conventionally, the consistency of the moulded product depends on the machine operator's expertise and understanding, since we can not monitor the process in between. The operator tries various settings, as suggested by the manufacturer, to achieve the optimum efficiency. For the optimization of the injection moulding process, various offline optimization techniques such as ANN, GA, Iterative system, and simulations are in use. But still, consistency errors occur due to variation during the moulding cycles. The data for operating process parameters can be reciprocated by modern injection moulding equipment, and external control methodologies can also be applied to the system. The work discussed focuses on the implementation of smart technology for real-time monitoring and control of the process of injection moulding. To make the process of moulding smart. To track the process inside the mould, cavity sensors, including pressure and temperature sensors, are used. Data generation, acquisition, collection, analysis, and failure detection are part of smart monitoring. In order to compensate for the variance of the moulding condition in the mould cavity so that the quality consistency is always satisfied, an AI-based autonomous control strategy is later determined. To derive the control strategy, process parameters and their interrelationship with quality defects have been studied and used. After its implementation, the whole process is expected to be smart and autonomous. To validate the established system for an automotive product, real industry scenarios and processing data were used. An automotive product, i.e. card door trim, is the research object chosen for this research. A comparative analysis shows that the number of scrap parts can be decreased effectively by 20 percent. This work is done with the mindset of constantly bringing the goods to the highest level, and in many respects it will definitely benefit the moulding and automotive industry.
Author(s) Details
School of Mechanical & Automotive Engineering, University of Ulsan, Ulsan 680-749, Korea.
Hong Seok Park
School of Mechanical & Automotive Engineering, University of Ulsan, Ulsan 680-749, Korea.
Prof. Dr. Chang Myung Lee
School of Mechanical & Automotive Engineering, University of Ulsan, Ulsan 680-749, Korea.
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