Prediction of Machining Condition Using Time Series Imaging and Deep Learning in Slot Milling of Titanium Alloy

Abstract

Low surface quality, undesired geometrical and dimensional tolerances, and product damage due to tool wear and tool breakage lead to a dramatic increase in production cost. In this regard, monitoring tool conditions and the machining process are crucial to prevent unwanted events during the process and guarantee cost-effective and high-quality production. This study aims to predict critical machining conditions concerning surface roughness and tool breakage in slot milling of titanium alloy. Using the Siemens SINUMERIK Edge Box integrated into a CNC machine tool, signals were recorded from main spindle and different axes. Instead of extraction of features from signals, the Gramian angular field (GAF) was used to encode the whole signal into an image with no loss of information. Afterwards, the images obtained from different machining conditions were used for training a convolutional neural network (CNN) as a suitable and frequently applied deep learning method for images. The combination of GAF and trained CNN model indicates good performance in predicting critical machining conditions, particularly in the case of an imbalanced dataset. The trained classification CNN model resulted in recall, precision, and accuracy with 75%, 88%, and 94% values, respectively, for the prediction of workpiece surface quality and tool breakage.