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Novel Deep Learning Approach for Practical Applications of Indentation by Kim, Yongju (author);Gu, Gang Hee (author);Asghari-Rad, Peyman (author);Noh, Jaebum (author);Rho, Junsuk (author);Seo, Min Hong (author);Kim, Hyoung Seop (author) is a scholarly article available to read on EtoBox.

What is Novel Deep Learning Approach for Practical Applications of Indentation about?

## a b s t r a c t The instrumented indentation technique has been investigated to efficiently evaluate the mechanical responses of materials with few limitations on the shape and size of the specimen. There have been attempts to discover a direct correlation between the stress-strain curve and the indenting loaddisplacement curve by introducing the concept of representative strain and stress. However, it is still difficult to find relible parameters and to distinguish similar load-displacement curves that correspond to different stress-strain curves with a limited number of experimental datasets. The present study introduces a finite element method (FEM)-based simulation that can output various load-displacement datasets corresponding to intrinsic properties of materials, including strain rate; these datasets are validated using experimental indentation results for diverse metallic materials at different indenting speeds (0.6, 0.9, 1.2 mm/min). In addition, an autoencoder (AE)-shaped artificial neural network (ANN) model is designed to efficiently characterize those datasets. Then, the indenting load-displacement datasets are extracted into effective physically meaningful datasets

Author

Kim, Yongju (author);Gu, Gang Hee (author);Asghari-Rad, Peyman (author);Noh, Jaebum (author);Rho, Junsuk (author);Seo, Min Hong (author);Kim, Hyoung Seop (author)

Publisher

Elsevier BV

Published

2022

Language

EN