Extraction of the plastic properties of metallic materials from scratch tests using deep learning

Extraction of the plastic properties of metallic materials from scratch tests using deep learning

Powered by machine learning and computer technology, neural networks have opened new paths for solving engineering problems. In this paper, the plastic parameters, i.e., the yield stress and strain hardening index, of metallic materials are extracted from scratch tests using deep learning methods. U...

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Bibliographic Details
Main Authors: Zhang, J., Qin, J., Li, Y., Lu, Chunsheng, Liu, H., Zhao, M.
Format: Journal Article
Language:English
Published: ELSEVIER 2022
Subjects:
Science & Technology
Technology
Materials Science, Multidisciplinary
Mechanics
Materials Science
Plastic properties
Scratch test
Deep learning
Finite element simulation
Multi -target regression
Neural networks
SPHERICAL INDENTATION
MECHANICAL-PROPERTIES
INSTRUMENTED INDENTATION
ELASTOPLASTIC MATERIALS
MATERIAL PARAMETERS
FRACTURE-TOUGHNESS
ELASTIC-MODULUS
HARDNESS
MODEL
WORK
Online Access:http://hdl.handle.net/20.500.11937/90385

Internet

http://hdl.handle.net/20.500.11937/90385

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