A novel current sensor indicator enabled WAFTR model for tool wear prediction under variable operating conditions
A novel current sensor indicator enabled WAFTR model for tool wear prediction under variable operating conditions
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DOI：
10.1016/j.jmapro.2022.08.036
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发表时间：
2022-10
影响因子：
6.2
通讯作者：
Pradeep Kundu;Xichun Luo;Yi Qin;Wenlong Chang;Ashok Kumar
中科院分区：
文献类型：
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作者：
Pradeep Kundu;Xichun Luo;Yi Qin;Wenlong Chang;Ashok Kumar
从电流传感器中提取健康指示器(HIS)来表示刀具磨损进程。由于原始电流传感器信号容易受到机床中其他部件和结构的影响，因此提取的HIS与刀具磨损的进程相关性较差。因此，本文提出了一种新的基于电流传感器的HI方法，该方法利用与电流信号包络相适应的反双曲余弦函数的均值来改善相关性。利用提取的HIS，研究人员已经开发了许多定制的机器学习(ML)模型。然而，这些模型的超参数多，难以解释，特别是在变工况下的预测精度很差。为了克服这些问题，本研究提出了一种威布尔加速失效时间回归(WAFTR)模型，该模型将工艺参数数据与HI相结合，以提高变工况下的预测精度。该模型以概率密度函数的形式映射了刀具磨损与刀具磨损的函数关系，从而识别出最优的His和加减速因子，使其具有较强的可解释性。通过控制加工参数的特定值，加速/减速系数有助于减速刀具磨损的演变。
The health indicators (HIs) were extracted from the current sensor to represent the tool wear progression. The extracted HIs were found poorly correlated with the progression of tool wear as the raw current sensor signal was susceptible to the influence of other parts and structures in the machine tool. Hence, this paper proposed a novel current sensor-based HI that utilized the mean of inverse hyperbolic cosine function fitted to an envelope of the current signal to improve the correlation. Using the extracted HIs, many bespoke machine learning (ML) models have been developed by researchers. However, these models have many hyperparameters, difficult to interpret and especially poor prediction accuracy has been observed under variable operating conditions. This study overcame these issues by proposing a Weibull Accelerated Failure Time Regression (WAFTR) model, which combines process parameters data with HI for improving the prediction accuracy under variable operating conditions. This model mapped a functional relationship with tool wear in the form of probability density function to identify best HIs and acceleration/deacceleration factors which makes it interpretable. The acceleration/deacceleration factors are useful to deaccelerate the tool wear evolution by controlling the specific values of the machining parameters.