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What is Tool Wear Prediction in TC18 Milling about?

This study investigates the tool wear mechanisms and prediction during the milling of TC18 titanium alloy using deep learning techniques. It identifies three stages of tool wear and establishes predictive models using convolutional bi-directional long short-term memory networks and gated recurrent units, achieving prediction errors within 8%. The findings suggest that deep learning offers a promising approach for real-time monitoring of tool wear in manufacturing processes.

Author

muallim1043

Language

EN