人工智能（AI）技術的引入是為了豐富人們多年的生活，AI影響了大多數行業，並有效地改善了生產過程。本研究的目標第1年建立H型鋼構件塗層的自動識別模型，第2年建立塗層厚度檢測和最佳機器人塗覆路徑以及第3年鋼構件的最佳物流路線運輸。關於AI應用，模式識別和鋼構件生產的廣泛文獻綜述提供了構建研究方法的概要，包括卷積神經網絡（CNN）、模糊超矩形複合神經網絡（FHRCNN）和自組織功能地圖優化（SOMO）。本研究標的是透過桃園市觀音工業園區於2020年1月新建的鋼構件廠房，以作為實驗和實踐環境，產出研究成果為建置塗層識別、厚度檢測及全面支持自動鋼構件生產檢測以及鋼部件運輸的最佳物流路線。本研究將在新建的鋼構件廠房進行，以檢查它們是否滿足工業需求。預期的發現將透過AI應用及降低生產成本，改進的生產品質控制以及增進職業安全與健康，並協助企業成立智慧化鋼構生產部門/實驗室，深信期許成為國內「鋼構生產智慧製造試營運場域」 (示範生產線) ，使學術研究和工業從業人員受益。 ;Artificial Intelligence (AI) technologies have been brought in order to enrich people’s lives for years. They have also affected most industries and made significant improvement for production. The objectives in the study are to establish (Year 1) the automatic recognition model for coating H-shape steel components, (Year 2) the coating thickness detection and optimal robotic coating path, and (Year 3) the optimal logistical routes for steel component shipping. A wide-ranging literature review in AI applications, pattern recognition, and steel component production provides the outline that constructs the methodology for the study including Convolution Neural Network (CNN), Fuzzy Hyper-Rectangular Composite Neural Networks (FHRCNN), and Self-Organizing feature Map Optimization (SOMO). Through the new facilities that has been completely set up in January 2020 at the Guan-yin (桃園市觀音區) industrial zoom, Taoyuan City, we can have an experimental and practical environment to fully support automatic steel component production by coating recognition, thickness detection, and optimal logistical routes for steel component shipping. The experiments will be conducted in the newly established facilities in order to check if they meet the industrial needs. The anticipated findings will benefit both academic and industrial practitioners by AI applications, cost efficiency, improved quality control, and occupational safety and health.
