SNMPL [SNMPL]Prof.Hyoung-Seop Kim:Novel deep learning approach for practical applications of indentation
페이지 정보
작성자 최고관리자
댓글 0건 조회 365회 작성일 2022-05-04 11:41
본문
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 by introducing a data postprocessing
procedure. The proposed indentation FEM-AE-shaped ANN model demonstrates that a
long-range true stress-strain curve can be attained even from a noisy experimental load-displacement
dataset.
관련링크
- 이전글[CML]Prof.Dong-Woo Suh: Microstructure and tensile properties of chemically heterogeneous steel consisting of martensite and austenite 22.06.14
- 다음글[SNMPL]Prof.Hyoung-Seop Kim:High-entropy alloys with heterogeneous microstructure: Processing and mechanical properties 22.05.03
댓글목록
등록된 댓글이 없습니다.