Remaining Useful Life Prediction of Cutting Tools Based on a Depthwise Separable TCN-BiLSTM Model with Temporal Attention

The prediction of remaining tool life is critical in practical manufacturing, as it enables early identification of tool wear conditions, preventing downtime and quality issues caused by tool failure. This, in turn, helps reduce costs and improve production efficiency. To address the limitations of...

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Egile Nagusiak:	Shaoyan Wang, Haili Jia, Aimin Wang, Long Wu, Qianxiong Li
Formatua:	Artigo
Hizkuntza:	Inglês
Argitaratua:	MDPI AG 2025-11-01
Saila:	Lubricants
Gaiak:	remaining tool life prediction temporal convolutional network long short-term memory network temporal attention mechanism residual connection
Sarrera elektronikoa:	https://www.mdpi.com/2075-4442/13/11/507
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