摘要
刀具狀態會直接影響工件的加工品質，透過刀具狀態監控技術，監控刀具狀態並控制工件品質以及避免停機。因此許多工具機的製造業者提出刀具監控機能給客戶，並提升自身產品競爭力。刀具磨耗會增加切削熱、切削力、激發振動，本文建立馬達切削負載監控機能，透過導入區段化的監控技術，比對相同加工區段的負載變化，並整合監控閥值與警報系統。為了提升馬達負載的刀具狀態辨識準確度，使用總體經驗模態分解法（Ensemble Empirical Mode Decomposition, EEMD）進行濾波，透過切削實驗驗證導入後的差異。經實驗驗證，EEMD方法於相同切削條件下，可有效提升刀具磨耗時馬達負載變化量。
並列摘要
Tool condition directly affects workpiece quality. It is necessary to use tool condition monitoring (TCM) technology to monitor tool conditions. It can enhance the quality of products and reduce equipment downtime. Many machine tool manufacturers provide tool monitoring functions to increase the competitiveness of their products. Tool wear increases cutting force, generates heat, and excites vibration. This article uses motor load to develop a tool condition monitoring function and uses segment technology to compare the load variation in every machining cycle. This function also includes threshold levels and an alarm system. Ensemble Empirical Mode Decomposition (EEMD) method is applied to filter motor load, which can improve cutting load monitoring function accuracy. The experiment has conducted to verify the method's effectiveness. The result shows that EEMD can increase tool wear accuracy under the same processing condition.
參考文獻
延伸閱讀
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