去毛邊是金屬加工的最後一道工序，也是決定產品加工品質的關鍵製程。傳統上，這項工藝依賴經驗豐富的老師傅手工完成，不僅效率低下，且加工品質不穩定。面對少子化帶來的勞力短缺，工廠迫切需要替代方案。機械手臂作為解決方案之一，能夠取代勞力密集且危險的工作，但其剛性不足與累積精度誤差限制了在金屬加工領域的廣泛應用。為了克服這些挑戰，本研究開發了一種新的去毛邊策略，結合力量控制與變化點演算法。透過力量控制可以有效減少加工過程中的振動，而變化點演算法則解決了毛邊尺寸的隨機變化。此外，本研究針對航空材料，建立了一套切削力學模型，並對切削過程中的被動參數進行了深入探討，提出了刀具選用的建議。在探討機械手臂剛性，使用條件數分析比較了不同切削姿態對加工品質的影響，並確定了最佳的加工姿態。實驗結果顯示，使用本研究方法可以顯著提高加工品質，表面粗糙度為1.0 μm，已達到業界要求加工標準。

Deburring represents the final and a critical phase in metalworking, significantly influencing the quality of the finished product. Traditionally, this task has depended on the manual expertise of skilled craftsmen, which often leads to low efficiency and inconsistent quality. Faced with labor shortages due to declining birth rates, the industry urgently requires alternative solutions. Robotic arms offer a potential solution by replacing labor-intensive and hazardous tasks; however, their application in metalworking has been limited due to insufficient rigidity and cumulative precision errors. To overcome these challenges, this study introduces a novel deburring strategy that integrates force control with a change point detection algorithm. Force control effectively mitigates vibrations during the machining process, while the change point algorithm accommodates the random variability in burr dimensions. Moreover, this research develops a cutting mechanics model tailored for aerospace materials and provides an in-depth analysis of passive parameters during the cutting process, offering tool selection recommendations. The study further investigates the rigidity of robotic arms through condition number analysis, comparing the effects of different cutting postures on machining quality and identifying the optimal posture. Experimental results demonstrate that the methodologies developed in this research substantially enhance machining quality, achieving a surface roughness of 1.0 µm, which meets industry processing standards.

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