| 摘要: | 隨著工業4.0 的推進，多階段製造程序(Multistage Manufacturing Processes, MMPs) 的穩定性與良率優化已成為製造業的核心挑戰。MMPs具有高度耦合與參數交互作用的特性，任一階段的微小偏差皆可能引發連鎖性瑕疵。以紡織製程為例，整經(warping)、上漿(sizing)、併軸 (beaming) 及織布 (weaving) 等連續步驟間的參數波動，常導致斷紗(brokenwarp) 或間歇性瑕疵(intermittent warp)，造成嚴重的原料浪費與生產成本增加。
現有研究廣泛採用樹狀模型(Tree-basedModels)進行缺陷預測，但在實務應用上仍面臨兩大核心限制：首先，傳統模型生成的參數建議區間往往過於寬泛，缺乏精確的操作指導性；其次，單一決策路徑難以捕捉多參數間的交互作用(InteractionEffect)，導致優化成效受限。
為此，本研究提出一套整合預測模型、交互作用分析與受約束優化之參數區間生成架構。首先，針對關鍵瑕疵建立多目標預測模型fi，並導入ShapleyResidual等工具尋找交互作用的特徵間的交互關係。接著，推論特徵及其交互作用模式，建立參數優化規則。最後，本研究設計一組受約束的優化目標函數，在極大化效益指標(Estimated Benefit, EB) 的同時，加入規則長度(NumberofRules)作為可信度的條件，確保生成的規則R具備統計意義並有效避免過擬合。
實驗結果顯示，本研究所提方法能有效收斂參數區間，在提升效益基準(EB)的同時，顯著降低了製程中的瑕疵率。此系統不僅提供了具備可解釋性的製程建議，更為製造現場提供了具備操作彈性的區間控制策略，達成了從缺陷預測轉向「缺陷預防」的零缺陷製造目標。;With the advancement of Industry 4.0, optimizing stability and yield in Multistage Manufacturing Processes (MMPs) has become a core challenge for the manufacturing industry. Characterized by high coupling and complex parameter interactions, even minor deviations in any stage of an MMP can trigger cascading defects. In the context of textile manufacturing, fluctuations in process parameters across sequential steps—such as warping, sizing, beaming,
and weaving—frequently lead to defects like broken warp or intermittent warp, resulting in significant material waste and increased production costs.
Although existing research extensively employs Tree-based models for defect prediction,
their practical application remains hindered by two primary limitations. First, the parameter recommendations generated by traditional models are often overly broad, lacking the precision required for operational guidance. Second, single-path decision rules fail to capture the interaction effects among multiple parameters, thereby limiting the effectiveness of optimization.
To address these issues, this study proposes an integrated framework that combines predictive modeling, Feature Interaction analysis, and constrained optimization for generating precise parameter interval recommendations. First, multi-objective predictive models fi are developed for four key defect types, utilizing tools such as SHAPLEY RESIDUAL to extract core parameters influencing defects. Next, the patterns of individual features and their interactions
are inferred to establish optimization rules. Finally, a constrained objective function is designed
to maximize the Estimated Benefit (EB) while incorporating a Number of Parameters as a credibility constraint. This ensures that the generated rules R possess statistical significance and effectively mitigate the risk of overfitting.
Experimental results demonstrate that the proposed method effectively streamlines the parameter selection process, balancing rule simplicity with high effectiveness. This significantly reduces defect rates while enhancing EB. By offering flexible interval-based control strategies, the system bridges the gap between defect prediction and prevention, advancing the goal of zero-defect manufacturing. |
