Abstract
Unlike the past, the smart factory environment requires many changes from the 2 M (Man and Machine) side to digitalization and intelligence. In other words, as an alternative to the shrinking vacancy of workers for manufacturing competitiveness, the transition to an artificial intelligence-based real-time predictive maintenance environment or an intelligent production system is highly demanded. Therefore, research on intelligent or artificial intelligence-based real-time predictive maintenance of automated processing facilities, which occupies a large proportion in the general production system, is quite significant. In this study, for the intelligent maintenance or intelligent predictive maintenance of such processing equipment, first, the “Conv2d-LSTM” technique, which reduces the parameters of the LSTM network layer and adds a Conv2d layer, is applied to the real-time spindle load data to verify the state judgment of the machining tool. Second, the real-time diagnostic performance for machining quality (status analysis for each tool) was verified by applying the spectrogram image using “STFT” to the “Pretrained Network model” for the machining load data. As a result of the study, it was verified that the learning time was shortened, and the performance was excellent (loss value lowered) compared to the application of “Bi-LSTM” in the state judgment through the prediction of the machining tool load. It was possible to confirm the quality judgment through classification. Based on the results of this study in the future, it is expected that production facility intelligence will be possible through weight reduction and diversification of artificial intelligence models that enable edge analysis for various production facilities such as processing facilities and robots.
This work was supported by the Technology Innovation Program (20019327, Industrial IoT based Lightweight Manufacturing System Technology Development for 24hr unmanned Production System) funded by the Ministry of Trade, Industry & Energy(MOTIE), Korea and the Grand Information Technology Research Center support program (IITP-2022-2020-0-01791) supervised by the IITP (Institute for Information and communications Technology Planning and Evaluation) funded by the Ministry of Science and ICT (MIST), Korea.
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Lee, H., Kang, D.H., Jeong, S.C. (2023). A Study on Industrial Artificial Intelligence-Based Edge Analysis for Machining Facilities. In: Lee, R. (eds) Emotional Artificial Intelligence and Metaverse. Studies in Computational Intelligence, vol 1067. Springer, Cham. https://doi.org/10.1007/978-3-031-16485-9_5
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DOI: https://doi.org/10.1007/978-3-031-16485-9_5
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