CNN-based in situ tool wear detection: A study on model training and data augmentation in turning inserts

Abstract

We present an automated turning insert wear detection system developed for aeronautical Low Pressure Turbines (LPT) casing machining process based on a binary classifier using Convolutional Neural Networks (CNN). This method involves acquiring the image on the machine itself. During this process, removing the insert from the tool holder is not necessary, and the wear assessment is performed before the next workpiece is mechanized. Since datasets in tool wear prediction are often imbalanced, a multi perspective camera technology as well as data augmentation and class weighting are utilized to address both the number of worn parts considered and the cost of image acquisition. In this study four different insert types and two CNN architectures (specific and universal models) are considered and evaluated. The effects of data augmentation and training set size are discussed. While the models trained perform well on round inserts, they fail on rhombic insert types. An accuracy up to 97.8% (Matthew’s correlation coefficient of 0.955) is achieved by the machine learning model. Additionally, it can detect defects on a variety of insert types.