Intelligent tool-wear prediction for ball nose tungsten carbide cutters in milling of stainless steel HRC52 using a genetic algorithm-enhanced bi-directional long short-term memory network

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Publicado no:	SN Applied Sciences vol. 7, no. 10 (Oct 2025), p. 1166
Autor principal:	Tan, Kailai
Outros Autores:	Liu, Zhiqiang, Jiang, Ruisong
Publicado em:	Springer Nature B.V.
Assuntos:	Feature extraction Accuracy Stainless steel Workpieces Deep learning Wavelet transforms Algorithms Optimization techniques Correlation analysis Task complexity Tungsten Nose Cutting tools Manufacturing Long short-term memory Time series Cutters Correlation coefficients Stainless steels Correlation coefficient Tool wear Efficiency Machine learning Genetic algorithms Carbide tools Predictions Global optimization Root-mean-square errors Milling (machining) Tungsten carbide Sensors Neural networks Support vector machines Tuning Information processing Environmental
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