OCSVM與LOF演算法用於智慧主軸之即時監測與顫振分析抑制

Abstract

隨著工業4.0的時代到來，對智慧工具機的需求與日俱增。智慧工具機強調機台本身具有自我監測、判斷與修復的能力，而工具機主軸切削顫振即為其中一項重要議題。顫振為主軸切削過程中所引發的自激式振動，顫振發生時會破壞工件表面精度且加速刀具磨耗，嚴重甚至會造成斷刀等現象。過去已有多位學者提出顫振偵測方法，然而大多數研究僅能分辨出成長完全的顫振訊號，無法分辨轉換狀態資料，且顫振偵測的閾值設定也多採用經驗法則，較難達到即時偵測與抑制的效果。因此，本論文提出一套智慧主軸顫振抑制模組，透過建立切削振動力學模型、小波包能量熵、異常偵測演算法及顫振抑制策略達到即時偵測及抑制顫振的效果。首先，建立切削力學模型以推導主軸顫振頻率，並繪製了三維系統穩定性圖。此模型考慮了進刀速率所造成的時間延遲影響，使系統穩定性圖增加了進刀速率的維度。其次，以小波包轉換計算相對小波包能量熵做為偵測顫振的特徵。小波包轉換對高頻有良好的靈敏度，可使顫振更早被預測。另外，以一類支持向量機與局部密度因子分辨顫振資料並比較兩種演算法的結果，實驗結果顯示局部密度因子代價函數為較低的0.0684，約略可視為有93.16%的分辨率，有較好的分類效果。最後，基於系統穩定性圖，制定顫振抑制策略，優先確保加工效率的提升，同時達到抑制顫振的效果。本論文從預處理至即時監控提出一套完整對策，成功開發出偵測顫振轉換狀態資料，並抑制顫振發生之系統。

With the advent of industry 4.0 era, the demand for intelligent machine tools is increasing. Intelligent machine tools emphasize that machine has the capability of self-monitoring, self-abnormal diagnosis and self-repairing. One of the significant issues of intelligent machine tools is chattering of spindle. Chatter is a self-excited vibration generated during cutting process. When it occurs, the accuracy of the workpiece surface is destroyed and the tool wear out rapidly. Even worse, it may cause the cutting tool broken. Different chatter detection methods have been proposed in the past. However, most researches could only identify fully developed chatter data rather than transition data. Moreover, setting threshold values of chatter often relied on experiences and experiments. Therefore, this thesis presented an intelligent-spindle chatter-suppression module to achieve real-time detections and suppressions of chatter through the establishment of spindle vibration model, relative wavelet packet energy entropy, anomaly detection algorithm and chatter suppression strategy. First, the spindle vibration model was established to derive the chatter frequency of spindle and construct the three-dimensional stability lobe diagram. This model took the variable time delay caused by feed rate into account, which increase the dimension of feed rate in stability lobe diagram. Second, the relative wavelet packet energy entropy calculated by wavelet packet transform was used as the feature of chatter detection. Wavelet packet transform had good sensitivity in high frequency which lead to early prediction of chatter. In addition, one-class support vector machine (OCSVM) and local outlier factor (LOF) were used to identify transition data. Based on the experimental results obtained from the two algorithms, local outlier factor demonstrated 0.0684 cost, which could be considered as 93.16% accuracy based on the weight of each class and was better than the one-class support vector machine. Last, based on the stability lobe diagram, a chatter suppression strategy was developed to improve the efficiency of machining and achieve effective chatter suppression. In conclusion, this thesis presented an intelligent chatter-suppressed system composed of preprocessing, real-time detection, and immediate suppressions by detecting chatter in transition state and suppressing chattering automatically.