Tool Wear Prediction Based on Residual Connection and Temporal Networks
Ziteng Li,
Xinnan Lei,
Zhichao You
et al.
Abstract:Since tool wear accumulates in the cutting process, the condition of the cutting tool shows a degradation trend, which ultimately affects the surface quality. Tool wear monitoring and prediction are of significant importance in intelligent manufacturing. The cutting signal shows short-term randomness due to non-uniform materials in the workpiece, making it difficult to accurately monitor tool condition by relying on instantaneous signals. To reduce the impact of transient fluctuations, this paper proposes a no… Show more
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