Health Monitoring of Milling Tools under Distinct Operating Conditions by a Deep Convolutional Neural Network model

One of the most popular manufacturing techniques is milling. It can be used to make a variety of geometric components, such as flat grooves, surfaces, etc. The condition of the milling tool has a major impact on the quality of milling processes. Hence the importance of follow-up. When working on monitoring solutions, it is crucial to take into account different operating variables, such as rotational speed, especially in real world experiences. This work addresses the topic of predictive maintenance by exploiting the fusion of sensor data and the artificial intelligence-based analysis of signals measured by sensors. With a set of data such as vibration and sound reflection from the sensors, we focus on finding solutions for the task of detecting the health condition of machines. A Deep Convolutional Neural Network (DCNN) model is provided with fusion at the sensor data level to detect five consecutive health states of a milling tool; From a healthier state to a state of degradation. In addition, a demonstrator is built with Simulink to simulate and visualize the detection process. To examine the capacity of our model, the signal data was processed individually and subsequently merged. Experiments were carried out on three sets of data recorded during a real milling process. Results using the proposed DCNN architecture with raw data have reached an accuracy of more than 94% for all data sets.