Results 1 to 10 of about 5,320,548 (290)

A Novel Machine Learning-Based Methodology for Tool Wear Prediction Using Acoustic Emission Signals. [PDF]

Sensors (Basel), 2021
Ferrando Chacón JL, Fernández de Barrena T, García A, Sáez de Buruaga M, Badiola X, Vicente J.   +5 more
europepmc   +1 more source

A spatio-temporal feature fusion network based on deep learning for collaborative prediction of tool wear and machining precision

Discover Computing
In precision metal cutting processes, tool wear is a critical factor constraining machining accuracy and surface quality. Conventional monitoring approaches predominantly rely on offline measurements of macroscopic wear parameters, which often fail to ...
Peiwang Li, Haigen Yang, Yuyin Wang, Yan Ge, Donghuang Lin   +4 more
doaj   +1 more source

Tool Wear Prediction Based on Artificial Neural Network during Aluminum Matrix Composite Milling. [PDF]

Sensors (Basel), 2020
Wiciak-Pikuła M, Felusiak-Czyryca A, Twardowski P.   +2 more
europepmc   +1 more source

In production system for tool wear prediction using multi-sensor time series and machine learning models. [PDF]

Discov Appl Sci
Dreyer J, Carrino S, Ghorbel H, Cotofrei P.   +3 more
europepmc   +1 more source

A data-driven approach for high-accuracy tool wear prediction in machining Hastelloy C276. [PDF]

Sci Rep
Abdullah M, Rao ACU, Ramachandran T, Biswal S, Chaudhary SP, Singh R, Mishra A, Mann VS, Bhowmik A, Abate L.   +9 more
europepmc   +1 more source

Real-Time Cutting Temperature Monitoring and Tool Wear Prediction with Integrated Thin-Film Thermocouples and Coupled Simulation. [PDF]

Micromachines (Basel)
Luo Y, Zuo F, Lyu B, Zhang X, Ge X.
europepmc   +1 more source

Research on Tool Wear Prediction Method Based on CNN-ResNet-CBAM-BiGRU. [PDF]

Sensors (Basel)
Sun B, Wang H, Zhang J, Zhang L, Wu X.
europepmc   +1 more source

CBN cutting tool's surface roughness and tool wear prediction using JOA-optimized CNN-LSTM. [PDF]

Sci Rep
Khetre S, Bongale A, Kumar S.
europepmc   +1 more source

Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition. [PDF]

Sensors (Basel), 2018
Caggiano A.
europepmc   +1 more source

An Innovative Study for Tool Wear Prediction Based on Stacked Sparse Autoencoder and Ensemble Learning Strategy. [PDF]

Sensors (Basel)
He Z, Shi T, Chen X.
europepmc   +1 more source