隨著製造工廠對於設備製造效率的要求不斷提升，如何有效降低加工機的刀具維護成本以及相關效率提升，已成為最近幾年熱門的研究議題，特別是有關刀具預測與監測智能化之研究。如針對透過機台安裝感測器之訊號，如何判斷刀具切削時之狀態。故本論文以刀具刀腹磨耗階段做為刀具狀態監測之依據，並使用壓縮和激勵殘差神經網路(Squeeze-and-excitation residual networks, SE-ResNet)監督式深度學習模型，進行模型訓練與預測，並將已訓練完成之數據模型導入具開放平台通訊統一架構(Open platform communications unified architecture, OPC UA)網路協定之監測系統，進行同場區多機台監測與預測。
本論文使用IEEE NUAA_Ideahouse刀具磨耗資料集的感測器數據作為訓練資料，並使用資料集的端銑刀刀腹最大磨耗寬度做為刀腹磨耗階段之分類依據，將其磨耗階段分為初期磨耗、均勻磨耗以及嚴重磨耗。為了符合模型之資料輸入型態，須透過Z分數標準化(Z-score standardization)、滑動視窗(Sliding window)演算方法進行資料數據處理，並以資料融合方式將一維數據合併為二維數據型態。本預測模型以ResNet-18模型為基礎進行預測模型層數與參數調整，透過增加壓縮和激勵網路(Squeeze-and-excitation networks, SENet)與減少模型層數，可以減少模型大小與增加模型層與層間之特徵提取。透過驗證數據測試辨識模型之F1分數，以及針對二種相似模型與以比較，可得到本模型驗證之F1分數準確率為99.04%，最終模型測試之F1分數準確率為98.62%。本論文最後建立端銑刀刀腹磨耗階段監測平台，其具有機台感測數據監測、刀具刀腹磨耗階段監測與機台狀態異常警報監測功能，作為刀具是否需要更換與機台保養之預警。

In recent years, as manufacturing factories demand higher equipment efficiency, intelligent prediction and monitoring of tool wear have gained attention. This study uses sensors to predict tool wear stages which are based on the maximum tool flank wear width. The IEEE NUAA_Ideahouse tool wear dataset's sensor data is utilized for data preprocessing, model training, and model testing. The study uses Z-score standardization and sliding window for data processing and uses the SE-ResNet model for training. The experimental results show that validation accuracy is 99.04% and test accuracy is 98.62%. Lastly, the research integrates the Open Platform Communications Unified Architecture (OPC UA) for multi-machine monitoring, enabling the system to have machine sensor data monitoring, tool wear stage monitoring, and abnormal machine state alerts. This system facilitates timely tool replacement and maintenance warnings.

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