Cutting Tool Condition Monitoring using a Deep Learning-based Artificial Neural Network

doi:10.23940/ijpe.22.01.p5.3746

Abstract

Abstract: A cutting tool is a significant constituent in the manufacturing process and a framework assisting its self-monitoring is one of the requirements of Industry 4.0. The Deep Learning (DL) approach is suitable for modeling such a framework and the application of multi-layer fully connected neural nets makes the model robust. This article presents the design of an Artificial Neural Network (ANN) classifier based on statistical learning of machining vibrations. Six distinct tool faults have been analyzed considering turning operations incorporating feature computation, choice, and classification. The output of the trained ANN is utilized for the classification of the fault and fault-free condition in the cutting tool and exhibited an accuracy of 93.33%. Later, the performance of this model has been compared with Machine Learning (ML) classifiers. Considering the comparative study, it is understood that the Deep Learning-based ANN model shows higher accuracy and can therefore be suggested for condition monitoring of a single-point cutting tool.

Key words: artificial neural network, condition monitoring, cutting tool, deep learning, statistical learning

Sonali S. Patil, Sujit S. Pardeshi, Nikhil Pradhan, and Abhishek D. Patange. Cutting Tool Condition Monitoring using a Deep Learning-based Artificial Neural Network [J]. Int J Performability Eng, 2022, 18(1): 37-46.

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