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大綱
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摘要
製造業近年來對於加工機故障或刀具磨耗之預測模型開發日趨重視。過往數據分析的刀具磨耗監測系統研究中，大部分是根據已經制定好的切削條件(轉速、進給)去建立刀具磨耗監測模型的，使用此模型對監測模型對應的切削條件之量測數據有良好的辨識性。但對於此切削條件外的數據的辨識性能力之研究不多，因此此方法建立的模型之泛化性(generalization)仍需要驗證。本研究嘗試將特定切削條件下的數據排除於訓練數據集中，並作為測試樣本，測試模型之辨識率，藉此檢驗刀具磨耗監測模型之泛化性。
研究中利用置於虎鉗上的加速規擷取之振動訊號，另外以電流勾錶量測主軸電流訊號，並將這些訊號作為刀具磨耗狀態的對應特徵。加速規安裝空間需求小、安裝容易、成本低，且有較高的信噪比(Signal-to-Noise ratio)，相較於安裝動力計更適合用於量測小型五軸加工機之加工訊號，而主軸電流訊號與切削力有高度正相關。本研究利用上述特徵以隨機森林(Random forest)演算法建立機器學習模型來辨識刀具磨耗之狀態，為測試模型之泛化性，採用排除特定實驗條件於訓練集外而作為測試集的方式測試模型，對各種實驗條進行測試後準確率大部分超越80%，最低則是65%。對實驗數據事先進行標準化後，去除頻率域訊號能量的因素，使模型選取到結構振動特徵，可將最低的準確率提升至75%，另外也解決了將嚴重磨耗判斷為輕度磨耗的問題，可以正確地判斷換刀時機。
關鍵字：振動訊號、加速規、刀具磨耗、隨機森林、銑削
Abstract
In recent years, the manufacturing
industry has paid more and more attention to the development of models for
predicting machine failure or tool wear. In the past, there are researches
studying tool wear monitoring system based on data analysis. Most of them
built tool wear monitoring model using cutting conditions, for example
speed and feed, according to ones they had already set. By using these
models, we can correctly identify the testing data which correspond to the
condition they set. In contrast, few researches that studying identifying
the data which is not correspond to the condition we set. Therefore,
generalization of models using these methods still needs to be discussed.
In this research, data corresponding to specific cutting condition is
excluded from training data set. Then, these data will be included to
testing data set to test the accuracy of the model. The generalization of
tool wear monitoring model could be test by this method.
The accelerometer is attached on vise to
capture vibration signal, and the current clamp meter is used to capture
spindle current signal. These signal will be the features of their tool
wear condition. Accelerometer is small, easy to install, relatively low
cost and is has high signal-to-noise ratio. Using vibration signal can
monitor the change of tool status more instantaneously than the cutting
force. The spindle current has highly positive correlation with cutting
force. In this study, vibration and current signal will be used to build a
random forest model to identify the wear status. In order to test the
generalization of the model, some data capture in specific cutting
condition is exclude from training set but include in testing set. The
result is that the accuracies are higher than 80% in almost all conditions,
and the lowest on is about 65%. After standardization, the power factor
will be removed so that features of structure vibration when feature
selecting, and the lowest accuracy can increase to 75%, in addition, the
problem that model fail identified serious wear into little wear is solved.
Therefore, the model can identify tool wear status more correctly..
Keywords: vibration
signal, accelerometers, tool wear, machine learning, milling
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