Tool Wear State Monitoring in Titanium Alloy Milling Based on Wavelet Packet and TTAO-CNN-BiLSTM-AM

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Detalles Bibliográficos
Publicado en:	Processes vol. 13, no. 1 (2025), p. 13
Autor principal:	Yang, Zongshuo
Otros Autores:	Li, Li, Zhang, Yunfeng, Jiang, Zhengquan, Liu, Xuegang
Publicado:	MDPI AG
Materias:	Accuracy Deep learning Titanium Wavelet transforms Artificial neural networks Back propagation networks Topology Machine tools Frequencies Cutting tools Manufacturing Long short-term memory Wear rate Tool wear Efficiency Titanium base alloys Research methodology Fault diagnosis Titanium alloys Milling (machining) Neural networks Triangulation Algorithms Information processing Heat resistance
Acceso en línea:	Citation/Abstract Full Text + Graphics Full Text - PDF
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