Results 131 to 140 of about 25,726 (266)

Overcoming Printing and Interfacial Challenges in Liquid Metal Direct Writing for Integrated Stretchable Electronics

Advanced Materials Technologies, EarlyView.
Liquid metal direct writing is advanced from a technological and fundamental point. Utilizing a kinematic bed, printing on large surfaces with irregularities is enabled. Furthermore, a pressure‐driven flow during printing is discovered that affects the thickness of traces.
Maximilian Krack, Lennert Purnal, Parth Vinayakrao Sewlikar, Sam Cuyvers, Gus Geurts, Rathul Nengminza Sangma, Derese Desta, Dieter Reenaers, Bart Ruttens, Iris De Graeve, Tom Hauffman, Seppe Terryn, Bram Vanderborght, Michaël Daenen, Wim Deferme, Monika Rai   +15 more
wiley   +1 more source

Multi-Sensor Heterogeneous Signal Fusion Transformer for Tool Wear Prediction. [PDF]

Sensors (Basel)
Zhou J, Liu X, Liao Q, Wang T, Wang L, Yang P.   +5 more
europepmc   +1 more source

Smart and Sustainable Sizing: Investigating the Sensing Behavior of Glass Fabrics Coated With Waterborne Polyurethane/Graphene Dispersions

Advanced Materials Technologies, EarlyView.
Waterborne polyurethane/graphene formulations are developed as piezoresistive coatings on glass fabric to enable flexible strain sensing. The graphene‐enabled conductive network provides a stable electromechanical response under cyclic compression. The coating demonstrates reliable pressure‐dependent resistance changes, highlighting its potential for ...
Vishnu Vijayan Pillai, Premika Govindaraj, Sai Aditya Pradeep, Olivia Sequerth, Srikanth Pilla, Franz Konstantin Fuss, Peter Kingshott, Nishar Hameed, Nisa V Salim   +8 more
wiley   +1 more source

A Hybrid CBiGRUPE Model for Accurate Grinding Wheel Wear Prediction. [PDF]

Sensors (Basel)
Si S, Mu D, Tang H.
europepmc   +1 more source

Transducers Across Scales and Frequencies: A System‐Level Framework for Multiphysics Integration and Co‐Design

Advanced Materials Technologies, EarlyView.
Transducers convert physical signals into electrical and optical representations, yet each mechanism is bounded by intrinsic trade‐offs across bandwidth, sensitivity, speed, and energy. This review maps transduction mechanisms across physical scale and frequency, showing how heterogeneous integration and multiphysics co‐design transform isolated ...
Aolei Xu, Mengyao Xiao, Zhouli Sui, Yinpeng Wang, Daoye Zheng, Yuxin Liu, Luwei Wang, Huajun Liu, Chengkuo Lee   +8 more
wiley   +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

Recent Advances of Slip Sensors for Smart Robotics

Advanced Materials Technologies, EarlyView.
This review summarizes recent progress in robotic slip sensors across mechanical, electrical, thermal, optical, magnetic, and acoustic mechanisms, offering a comprehensive reference for the selection of slip sensors in robotic applications. In addition, current challenges and emerging trends are identified to advance the development of robust, adaptive,
Xingyu Zhang, Liao Wu, Shuai He, Zhao Sha, Yuwan Huang, Shuying Wu, Dewei Chu, Chun H. Wang, Shuhua Peng   +8 more
wiley   +1 more source

Tool wear prediction based on XGBoost feature selection combined with PSO-BP network. [PDF]

Sci Rep
Lin Z, Fan Y, Tan J, Li Z, Yang P, Wang H, Duan W.   +6 more
europepmc   +1 more source

Material Selection and Geometric Design for Few‐filament Shape Memory Polymer Yarns for Force Generation

Advanced Materials Technologies, EarlyView.
A custom shape memory polymer material selection process relates quantitative application criteria (e.g., compression garment yarns) to material characterization information. The selected materials are manufactured into yarn geometries, which expand the design space by creating structural stress‐strain profiles beyond the nominal material stress‐strain
Michaela Andrews, Susan Mantell, Julianna Abel   +2 more
wiley   +1 more source

Wear Prediction of Functionally Graded Composites Using Machine Learning. [PDF]

Materials (Basel)
Fathi R, Chen M, Abdallah M, Saleh B.
europepmc   +1 more source