This paper focuses on the study and development of learning algorithms oriented to wear classification and predictive maintenance (PdM) of the cutting tool (CT) of a clamping machine for producing structural steel bars. While several works dedicated to CTs for turning and milling operations, or in general for metal removal operations, also known as subtractive manufacturing processes, can be found in the literature, the phenomena related to cutting with a cutting knife have not been widely treated in the literature. This article intends to focus on the analysis of the latter problem. The objective is to estimate the wear of the CT, a critical component of the steel bar cutting machine. The SVM classifiers were therefore used to classify the wear. For the predictive maintenance purpose, two algorithms were implemented for the prediction of the remaining service life, based on the Degradation Model and the Similarity Model respectively; in the first method, a prediction and state update function were used, while in the second method, a Long Short-Term Memory (LSTM) Neural Network (NN) was used.
Machine learning for monitoring and predictive maintenance of cutting tool wear for clean-cut machining machines / Bonci, A.; Di Biase, A.; Dragoni, Aldo Franco; Longhi, S.; Sernani, P.; Zega, A.. - ELETTRONICO. - Volume 2022:(2022), pp. 1-8. (Intervento presentato al convegno 27th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2022 tenutosi a Stuttgart - Germany nel 2022) [10.1109/ETFA52439.2022.9921571].