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Milling cutter fault diagnosis using unsupervised learning on small data: A robust and autonomous framework

Patange Abhishek, Soman Rohan, Pardeshi Sujit, Kuntoglu Mustafa, Ostachowicz Wieslaw

Eksploatacja i Niezawodność

2024

Vol. 26, no. 1

art. no. 178274

Tool condition affects the tolerances and the energy consumption and hence needs to be monitored. Artificial intelligence (AI) based data-driven techniques for tool condition determination are proposed. Unfortunately, the data-driven techniques are data-hungry. This paper proposes a methodology for classification based on unsupervised learning using limited unlabeled training data. The work presents a multi-class classification problem for the tool condition monitoring. The principal component analysis (PCA) is employed for dimensionality reduction and the principal components (PCs) are used as input for classification using k-means clustering. New collected data is then projected on the PC space, and classified using the clusters from the training. The methodology has been appliedforclassification of tool faults in 6 classes in a vertical milling center. The use of limited input parameters from the user makes the method ideal for monitoring a large number of machines with minimal human intervention. Furthermore, due to the small amount of data needed for the training, the method has the potential to be transferable.