Deep learning-based CNC milling tool wear stage estimation with multi-signal analysis

Abstract

In this work, the convolutional neural network (CNN), which is a deep learning method in which the features are extracted by an inner process, was performed to detect the wear stages of the milling tool. These stages that define the total lifespan of the tool are known as initial wear (IW), steady-state wear (SSW), and accelerated wear (AW). Short Time Fourier Transform (STFT) was applied to signals, and signal spectrograms were used to train CNN models with different complex architectures. Vibration signals, acoustic emission signals, and motor current signals from The Nasa Ames Milling Dataset were used to obtain the spectrograms. Pre-trained CNNs (GoogleNet, AlexNet, ResNet-50, and EfficientNet-B0) detected the tool wear stage with varying accuracies. It has been seen that the time duration of model training increases as the size of the dataset grows and the network architecture becomes more complex. The recommended method has also been tested on the 2010 PHM Data Challenge Dataset. CNN shows promise for condition monitoring of milling operations and detecting tool wear stage.

Testing and modeling the possibility of using a lamellar grinding wheel with an adaptive background structure

“…The tool wear process can be characterized by the change of control parameters [26,28]. Tool condition monitoring reduces the production costs and time required to service cutting tools as well as increases the efficiency of the manufacturing process [4,5,11]. Various strategies and techniques are used in the interpretation of measured data [20,29,30].…”

Modernization of the stamping process using eddy current and load sensors in the manufacturing of automotive parts

“…Monitoring this distance makes it possible to determine that a wider coil has entered the tool. Better results can indeed be achieved by combining eddy current sensors with force or acoustic emission (AE) sensors [24,25]. These two groups of sensors are also widely used for punch and die wear monitoring and fracture detection.…”