金屬製程是現今所有工業的基石，然而其製造環境與產品特性對於現今工業製造環境帶來嚴峻的挑戰。具體而言，金屬製程的製造環境通常被視為骯髒、危險且辛苦的工作場域，而產品特性則傾向於小批量多樣本的生產模式。因此，如何在金屬製程中引入智慧製造技術，已成為當務之急。近年來，智慧製造技術迅速發展，包括第五代通訊技術、數位孿生、人工智慧物聯網以及邊緣運算等方法被廣泛導入於不同領域，例如節能、自動化光學品質檢測、機器人協作、自動搬運等新穎應用，極大化提升了效能與品質，並使製造流程更加靈活。然而，在智慧製造中以數據驅動為導向的技術發展上，在金屬製程必須面對的重大挑戰，即數據不平衡問題。針對此挑戰，本研究提出三個方向的解決方法與架構，以改善數據不平衡所帶來的問題。
首先，在自動化領域，本研究聚焦於建立更直觀的機器手臂前處理方法，以提升機器手臂在生產線上的導入效率。該方法主要圍繞機器人互動與語意分割議題，並通過零樣本學習（ZSL）技術應對數據極端不平衡問題。在基於VOC2012數據集的實驗中，本架構相較其他方法，平均交並比（MIoU）提升了5%，展現了顯著優勢。其次，針對鍛造工藝中螺絲頭槽的成形品質，在研究中提出了一種雙向時序自編碼模型，針對異常與正常數據比例懸殊（0.006）的情況，實現了從抽樣式品質管理到全樣品品質管理的升級。同時，該模型確保了推論效能與穩定性，進一步改善了工藝流程。最後，以金屬製程中應用領域最大的焊接工藝為例，本研究提出了一種適用於複雜環境的人工焊接品質評分系統。在系統中結合了Bi-AGAN模型，解決了長時序數據中子集異常的檢測問題，並整合箱型移動設備以提高使用於不同環境的適應性。基於準確率和召回率的評估上，Bi-AGAN模型分別達到了95%與92%，展現了其卓越性能。
綜上所述，本研究針對鍛造與焊接這兩大製程工藝，引入生成模型結合半監督學習，在處理數據不平衡所帶來的品質問題上取得了顯著效果。此外，本研究提出的引入三維模型作為語意模板的零樣本學習應用於語意分割技術，作為自動化直覺式手臂教導的前處理方法，具有廣泛應用潛力，為未來智慧製造的領域擴散有了重要參考方向。

Metal processing is the cornerstone of modern industry; however, its manufacturing environment and product characteristics pose significant challenges to today’s industrial landscape. Specifically, the manufacturing environment for metal processing is often considered dirty, hazardous, and labor-intensive, while its product characteristics lean toward small-batch, multi-variety production models. Thus, introducing smart manufacturing technologies into metal processing has become an urgent priority. In recent years, smart manufacturing technologies have rapidly developed, including the adoption of fifth-generation (5G) communication technology, digital twins, Artificial Intelligence of Things (AIoT) , and edge computing. These methods have been widely implemented across various fields, enabling innovative applications such as energy efficiency, automated optical inspection (AOI), robotic collaboration, and automated material handling. These advancements have significantly improved efficiency, quality, and flexibility in manufacturing processes. Despite these benefits, a major challenge for smart manufacturing in metal processing is the issue of data imbalance in data-driven technological development. To address this challenge, this study proposes three solution directions and architectures aimed at mitigating the issues caused by data imbalance.
First, in the field of automation, this study focuses on establishing a more intuitive preprocessing method for robotic arms to enhance their efficiency on production lines. This method primarily addresses robotic interaction and semantic segmentation issues and employs zero-shot learning (ZSL) techniques to tackle extreme data imbalance. Experiments based on the VOC2012 dataset demonstrate that this architecture improves the mean Intersection over Union (MIoU) metric by 5% compared to other approaches, showcasing significant advantages. Second, in the forging process, specifically in the quality analysis of screw head slots, this research introduces a bidirectional temporal autoencoder model. This model addresses the significant disparity between abnormal and normal data (ratio: 0.006), enabling an upgrade from sample-based quality management to full-sample quality management. Furthermore, the model ensures inference efficiency and stability, thereby improving the overall process flow. Finally, in the welding process the most widely applied area in metal processing—this study proposes a quality scoring system for manual welding in complex environments. The system integrates a Bi-AGAN model to address subset anomaly detection in long time-series data and incorporates a box-type mobile device to enhance adaptability across diverse environments. Evaluations based on accuracy and recall rates show that the Bi-AGAN model achieves 95% accuracy and 92% recall, demonstrating outstanding performance.
In summary, this study addresses quality issues arising from data imbalance in the two key metal processing techniques of forging and welding by introducing generative models combined with semi-supervised learning. Additionally, the proposed use of three-dimensional models as semantic templates in zero-shot learning for semantic segmentation serves as a robust preprocessing method for intuitive robotic arm teaching, showcasing its broad applicability. This research provides a valuable reference for future advancements and the broader application of smart manufacturing technologies across various industrial domains.

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