Prediction of Flank Wear during Turning of EN8 Steel with Cutting Force Signals Using a Deep Learning Approach

Currently, manufacturing industries focus on intelligent manufacturing. Prediction and monitoring of tool wear are essential in any material removal process, implementation a condition system (TCMS) is necessary. This work presents the flank prediction during hard turning EN8 steel using deep learning (DL) algorithm. The operation conducted with three levels selected parameters. CNMG 120408 grade, TiN-coated cemented carbide used for turning. Cutting force assessed under dry-cutting conditions. DL algorithms such as adaptive neuro-fuzzy inference (ANFIS) convolutional auto encoder (CAE) to predict single-point cutting tool. model developed parameters wear. different ANFIS CAE models employed develop model. Grid-based structure Gauss membership function performed better than models. model’s average testing error 0.0074011 mm accuracy 99.81% achieved.