Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China.
Union Big Data, Chengdu 610000, China.
Sensors (Basel). 2020 Aug 29;20(17):4896. doi: 10.3390/s20174896.
In recent years, industrial production has become more and more automated. Machine cutting tool as an important part of industrial production have a large impact on the production efficiency and costs of products. In a real manufacturing process, tool breakage often occurs in an instant without warning, which results a extremely unbalanced ratio of the tool breakage samples to the normal ones. In this case, the traditional supervised learning model can not fit the sample of tool breakage well, which results to inaccurate prediction of tool breakage. In this paper, we use the high precision Hall sensor to collect spindle current data of computer numerical control (CNC). Combining the anomaly detection and deep learning methods, we propose a simple and novel method called CNN-AD to solve the class-imbalance problem in tool breakage prediction. Compared with other prediction algorithms, the proposed method can converge faster and has better accuracy.
近年来，工业生产越来越自动化。作为工业生产的重要组成部分，机械切削工具对产品的生产效率和成本有很大的影响。在实际的制造过程中，刀具的破损往往在瞬间发生，没有任何预警，这导致刀具破损的样本与正常样本极不平衡。在这种情况下，传统的监督学习模型不能很好地适应刀具破损的样本，导致刀具破损的预测不准确。在本文中，我们使用高精度霍尔传感器采集数控机床（CNC）的主轴电流数据。结合异常检测和深度学习方法，我们提出了一种简单而新颖的方法，称为 CNN-AD，以解决刀具破损预测中的类别不平衡问题。与其他预测算法相比，所提出的方法可以更快地收敛，并且具有更好的准确性。
