Recently, research on applying artificial intelligence (AI) to various industries, especially manufacturing, is being actively conducted. In the field of smart factory, the purpose is to improve productivity based on data generated in the process of producing or processing products. The tool breakage during metal product processing causes fatal difficulties of predicting tool life. Moreover, if tool life is not predicted, defects may occur product reliability deteriorate, which may adversely affect product performance or economic aspects. In this paper, data related to machining is collected from CNC equipment in real time, and through machine learning and deep learning, which factors affect the wear of cutting tools are identified and the lifespan of cutting tools is predicted. An AI-based solution was applied to the system, productivity improved due to an increase in tool life.
Keywords
CNC Machining, Tool Life, Machine Learning, Deep Learning, Life Prediction.
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This work was supported by Changshin University Research Fund of 2023-045.
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Jung Kyu Park
Jung Kyu Park
Department of Computer Engineering, Changshin University, Korea.
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Cite this article
Jae-Hak Lee, Young-Han Jeong and Jung Kyu Park, “Predicting Factory Equipment Lifespan Through Manufacturing Data Analysis using AI”, Journal of Machine and Computing, pp. 693-701, July 2024. doi: 10.53759/7669/jmc202404066.
