An improvement in data interpretation to estimate residual stresses and mechanical properties using instrumented indentation: A comparison between machine learning and Kriging model-Reference-Cited by-同舟云学术

An improvement in data interpretation to estimate residual stresses and mechanical properties using instrumented indentation: A comparison between machine learning and Kriging model

Published:2022-09 Issue: Volume:114 Page:105186
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Salmani Ghanbari S.,Mahmoudi A.H.^ORCID

Funder

Bu Ali Sina University

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference28 articles.

1. User’s manual. abaqus. dassault systèmes;ABAQUS,2014

2. ASTM E2546-15,, 2015. Standard practice for instrumented indentation testing.

3. A robust inverse analysis method to estimate the local tensile properties of heterogeneous materials from nano-indentation data;Bono;Int. J. Mech. Sci.,2017

4. On the determination of residual stress and strain fields by sharp indentation testing: Part I: theoretical and numerical analysis;Carlsson;Acta Metall.,2001

5. A statistical approach to some mine valuation and allied problems on the witwatersrand;D,1951

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