{"id":859,"date":"2026-03-08T01:07:32","date_gmt":"2026-03-07T17:07:32","guid":{"rendered":"https:\/\/iweb20wp-b205b.url.tku.edu.tw\/jase\/?post_type=tkuisotope&p=859"},"modified":"2026-03-22T15:08:08","modified_gmt":"2026-03-22T07:08:08","slug":"intelligent-manufacturing-using-intelligent-technologies-to-transform-industry","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=intelligent-manufacturing-using-intelligent-technologies-to-transform-industry","title":{"rendered":"Intelligent Manufacturing: Using Intelligent Technologies To Transform Industry"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 30<\/a><\/p>\n<\/div>\n\n\n\n

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Mingyu Li<\/i><\/a><\/p>\n\n\n\n

School of Henan Institute of International Business and Economics; Zhengzhou Henan, 450002, China<\/p>\n<\/div>\n\n\n\n

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Received:\u00a0June 15, 2025
Accepted:\u00a0September 21, 2025
Publication Date:\u00a0March 8, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Feature selection for the input variables.<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 RIS<\/a> | BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202607_30.029<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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This paper presents a feature-based paradigm for intelligent manufacturing, highlighting the operational influence of critical variables temperature, operating mode, power consumption, network latency, production speed, and error rate on system efficiency. Four hybrid models were created by integrating Stacking Classification (SC) and Gaussian Process Classification (GPC) with Artificial Rabbit Optimization (ARO) and Coronavirus Herd Immunity Optimizer (CHIO) to elucidate intricate feature connections and improve prediction accuracy. The models\u2014STCO, STAO, GPCO, and GPAO were evaluated using metrics such as Accuracy, F1-score, and Matthews Correlation Coefficient. STAO attained the greatest test accuracy (0.981) and MCC (0.972), thereby validating its exceptional performance. Feature importance analysis indicated that production speed and error rate are the most significant variables. SHAP and FAST studies provided additional insights, indicating that interaction effects among characteristics predominantly influence model behavior. The findings indicate that hybrid intelligent models utilizing feature-level input priority provide enhanced predicted accuracy and increased explainability, rendering them appropriate for real-time industrial application.<\/p>\n\n\n\n

Keywords:\u00a0Intelligent Manufacturing; Feature Importance Analysis; Production Efficiency Prediction; Feature-Based Modeling; Bio-inspired Optimization<\/em><\/p>\n\n\n\n

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