A transfer learning approach based tool wear detection in the turning process using vibration signals

Continuous monitoring of the cutting tool insert’s condition is essential to enhance product quality and efficient machining process, by reducing the machine downtime. But the available tool condition monitoring approaches are often limited by coolant induced visibility loss in the cutting zone that reduces the feature reliability. This study proposes a transfer learning based deep learning method where the machining vibration signals are converted into visual representations and classified using ResNet 18, MobileNet V2, SqueezeNet, ShuffleNet, DenseNet 201, and EfficientNet B0 pretrained convolutional neural networks. This combination enables the model to learn deep wear profiles from vibration data without the manual feature extraction. Also, this method enhances signal strength, making it highly suitable for smart, scalable, and real world manufacturing environments. The effects of the proposed pretrained network hyperparameters, such as mini batch size, solver type, learning rate, and filter size, were studied and EfficientNet B0 was identified as the best performing network with a classification accuracy of 89.23% for tool condition monitoring tasks.