Predicting critical machining conditions using time-series imaging and deep learning in slot milling of titanium alloy

Abstract

Tool wear and tool breakage cause product damage in terms of low surface quality and undesired geometrical and dimensional tolerances, followed by a dramatic increase in the production cost. In this study, an Artificial Intelligence (AI) model has been developed to predict the critical machining conditions concerning surface roughness and tool breakage in the slot milling of titanium alloy. The signals recorded from the main spindle and different axes through the Siemens SINUMERIK EDGE Box integrated into a CNC machine tool were converted into images using Gramian Angular Field (GAF). Further, the converted images were used for training Convolutional Neural Network (CNN). The combination of GAF and trained CNN model indicates good performance in predicting critical machining conditions, particularly in the case of an imbalanced dataset.