以控制器伺服馬達電流監測切削力變化及刀具磨耗技術研究

隨著工業 4.0 發展，製造業導入人工智慧與大數據技術，以提升生產效率與競爭力。傳統刀具磨耗監測需拆卸刀具，導致機器停機並增加成本。為克服此限制，本研究探討透過控制器伺服馬達電流監測切削力與刀具磨耗。實驗使用亞崴五軸工具機 (FV560)，變換切削參數以收集西門子控制器 (840D) 的主軸馬達電流訊號並建立切削力模型。透過快速傅立葉轉換 (Fast Fourier Transform, FFT) 與希爾伯特-黃轉換 (Hilbert-Huang Transform, HHT) 提取刀具磨耗相關特徵。結果顯示，切削力與主軸電流呈線性關係，且電流訊號會隨刀具磨耗變化而改變，FFT 與 HHT 分析結果高度吻合，驗證了電流監測方法的可行性。

With the advancement of Industry 4.0, the manufacturing industry has integrated artificial intelligence and big data technologies to enhance production efficiency and competitiveness. Traditional tool wear monitoring requires tool disassembly leading to machine downtime and increased costs. To overcome this limitation, this study explores monitoring cutting force and tool wear through servo motor current from the controller. Experiments were conducted using Awea five-axis machine (FV560), where spindle motor current signals were collected from Siemens 840D controller under varying cutting parameters to establish a cutting force model. Fast Fourier Transform (FFT) and Hilbert-Huang Transform (HHT) were employed to extract tool wear-related features. The results indicate a linear relationship between cutting force and spindle current, with current signals varying according to tool wear. The consistency between FFT and HHT analyses validates the feasibility of the current-based monitoring method.