Results 151 to 160 of about 5,074,594 (286)
Real-Time Cutting Temperature Monitoring and Tool Wear Prediction with Integrated Thin-Film Thermocouples and Coupled Simulation. [PDF]
Luo Y, Zuo F, Lyu B, Zhang X, Ge X.
europepmc +1 more source
Flexible Sensors for Robotics Tactile Perception: A Review
Flexible tactile sensing for robotics is reviewed through four interconnected dimensions. Physical mechanisms include piezoresistive, capacitive, piezoelectric, triboelectric, iontronic, and optical sensing. Structural design includes bioinspired, defect‐based, and MEMS‐based tactile systems.
Yu Song, Ying Chen, Yihao Chen, Xue Feng
wiley +1 more source
Research on Tool Wear Prediction Method Based on CNN-ResNet-CBAM-BiGRU. [PDF]
Sun B, Wang H, Zhang J, Zhang L, Wu X.
europepmc +1 more source
This review identifies current and future directions in abiotic nanostructured catalysts to develop reliable and sustainable glucose fuel cells to power the next generation of bioelectronic devices. ABSTRACT The global rise in incidence of chronic diseases has led to the demand for innovative solutions that help patients manage their conditions with ...
Asghar Niyazi +3 more
wiley +1 more source
CBN cutting tool's surface roughness and tool wear prediction using JOA-optimized CNN-LSTM. [PDF]
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]
Caggiano A.
europepmc +1 more source
Laser‐induced graphene (LIG) provides a scalable, laser‐direct‐written route to porous graphene architecture with tunable chemistry and defect density. Through heterojunction engineering, catalytic functionalization, and intrinsic self‐heating, LIG achieves highly sensitive and selective detection of NOX, NH3, H2, and humidity, supporting next ...
Md Abu Sayeed Biswas +6 more
wiley +1 more source
An Integrated NLP‐ML Framework for Property Prediction and Design of Steels
This study presents a data‐driven framework that uses language‐processing techniques to interpret steel processing descriptions and machine‐learning models to predict mechanical properties. By organising complex process histories into meaningful groups and enabling rapid property forecasts, the work supports faster, more informed steel design through ...
Kiran Devraju +5 more
wiley +1 more source
An Innovative Study for Tool Wear Prediction Based on Stacked Sparse Autoencoder and Ensemble Learning Strategy. [PDF]
He Z, Shi T, Chen X.
europepmc +1 more source
This review comprehensively summarizes the atomic defects in TMDs for their applications in sustainable energy storage devices, along with the latest progress in ML methodologies for high‐throughput TEM data analysis, offering insights on how ML‐empowered microscopy facilitates bridging structure–property correlation and inspires knowledge for precise ...
Zheng Luo +6 more
wiley +1 more source

