Results 231 to 240 of about 5,320,548 (290)

Physics-informed neural network framework for predicting drilling-induced delamination in GFRP composites. [PDF]

PLoS One
Arunadevi M, M S S, B R N M, M C G.
europepmc   +1 more source

Tribology of GA and GI Steel Sheets in Forming: Coating-Failure Mechanisms, Evaluation Methods, and Governing Factors. [PDF]

Materials (Basel)
Liu T, Mustafa ME, Cui J, Chen R, Wei X, Ma B, Zhang X.   +6 more
europepmc   +1 more source

Comparative machine learning analysis of wear characteristics in industrial waste-filled epoxy-jute composites. [PDF]

Sci Rep
Pati PR, Kumar SS, Purohit A, Kumar A, Singh J, Ashok N.   +5 more
europepmc   +1 more source

An AI-Assisted Tool to Predict Continuous Glucose Monitor Adherence in Children With Type 1 Diabetes in Oman: Protocol for a Multiphase Mixed Methods Translational Study. [PDF]

JMIR Res Protoc
Al Ghafri T, Al Harthi S, Bait Ishaq A, Al Harthi H, Al Muaini M, Al-Ghadani R, Aljufaili A, Al Harb M, Al Saadi A, Abdessalem A, Shaffi N, Ahmed M, Jaboob S, Al Maqbali N, Al-Shehhi M, Al Kalbani T, Al Shukaili A, Al Harthi J.   +17 more
europepmc   +1 more source

Nonlinear Modeling of Surface Roughness in Turning of Nitinol: Influence of Cutting Forces, Temperature, and Negative Rake Angle Tools. [PDF]

Materials (Basel)
Zębala W, Matras A, Seltenreich M.
europepmc   +1 more source

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Tool wear prediction in turning

Journal of Materials Processing Technology, 2004
Abstract The aim of the present work is to develop a reliable method to predict flank wear precisely in a turning process by developing a mathematical model and comparing it with the experimental results. Some important factors like the index of diffusion, wear coefficient, rate of increase of normal load with respect to flank wear and the hardness ...
S.K. Choudhury, P. Srinivas
openaire   +1 more source

Physics-informed Gaussian process for tool wear prediction

ISA Transactions, 2023
The tool wear monitoring (TWM) system plays an increasingly important role to ensure high quality finishing and system safety in advanced CNC machining process. The pure data-based TWM approaches generally needs to develop complex machine learning models and require massive sensory data to learn the models to reach high monitoring accuracy, while the ...
Kunpeng Zhu, Chengyi Huang, Si Li, Xin Lin   +3 more
openaire   +2 more sources