Department of Mechanical Engineering, Faculty of Engineering of Bilbao, University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain.
Ideko Centro Tecnológico, Basque Research and Technology Alliance (BRTA), 20870 Elgoibar, Spain.
Sensors (Basel). 2022 Sep 13;22(18):6911. doi: 10.3390/s22186911.
Tool wear monitoring is a critical issue in advanced manufacturing systems. In the search for sensing devices that can provide information about the grinding process, Acoustic Emission (AE) appears to be a promising technology. The present paper presents a novel deep learning-based proposal for grinding wheel wear status monitoring using an AE sensor. The most relevant finding is the possibility of efficient feature extraction form frequency plots using CNNs. Feature extraction from FFT plots requires sound domain-expert knowledge, and thus we present a new approach to automated feature extraction using a pre-trained CNN. Using the features extracted for different industrial grinding conditions, t-SNE and PCA clustering algorithms were tested for wheel wear state identification. Results are compared for different industrial grinding conditions. The initial state of the wheel, resulting from the dressing operation, is clearly identified for all the experiments carried out. This is a very important finding, since dressing strongly affects operation performance. When grinding parameters produce acute wear of the wheel, the algorithms show very good clustering performance using the features extracted by the CNN. Performance of both t-SNE and PCA was very much the same, thus confirming the excellent efficiency of the pre-trained CNN for automated feature extraction from FFT plots.
刀具磨损监测是先进制造系统中的一个关键问题。在寻找能够提供磨削过程信息的传感装置时，声发射（AE）似乎是一种很有前途的技术。本文提出了一种基于深度学习的新方法，利用 AE 传感器监测砂轮磨损状态。最相关的发现是使用 CNN 从频域图中高效提取特征的可能性。从 FFT 图中提取特征需要具备声音领域的专业知识，因此我们提出了一种使用预训练 CNN 进行自动特征提取的新方法。使用针对不同工业磨削条件提取的特征，测试了 t-SNE 和 PCA 聚类算法来识别砂轮磨损状态。对不同的工业磨削条件进行了结果比较。在进行的所有实验中，均能清楚地识别出由于修整操作而导致的砂轮初始状态。这是一个非常重要的发现，因为修整强烈影响着操作性能。当磨削参数导致砂轮急剧磨损时，使用 CNN 提取的特征，算法表现出非常好的聚类性能。t-SNE 和 PCA 的性能非常相似，这进一步证实了预训练 CNN 从 FFT 图中自动提取特征的出色效率。
