Tool Wear Estimation Using Few-Shot Learning in Deep Neural Network Architectures

This research leverages the specialized machine learning technique known as few-shot learning to make precise predictions of cutting tool wear during milling, even when provided with limited training data. Employing a meta-learning approach, the model demonstrates exceptional generalization capabilities from a constrained dataset, enabling seamless adaptation to new tasks with minimal examples. Utilizing a foundational deep neural network trained on a small dataset, novel models are constructed, showcasing remarkable accuracy when presented with previously unseen data. These findings underscore the distinct advantages of few-shot learning over traditional methodologies, delivering highly accurate tool wear predictions while significantly reducing computational time. Furthermore, this approach shows potential for dealing with data-intensive circumstances in real-world applications. Ultimately, the research has the potential to revolutionize machining processes by increasing tool longevity, reducing downtime, and lowering tool replacement costs.