TY - JOUR
T1 - Determination of dynamic flow stress equation based on discrete experimental data
T2 - Part 1 Methodology and the dependence of dynamic flow stress on strain-rate
AU - Huang, Xianglin
AU - Li, Q. M.
N1 - Publisher Copyright:
© 2025 The Author(s)
PY - 2025/12
Y1 - 2025/12
N2 - In this study, a framework to determine the dynamic flow stress equation of materials based on the discrete data of varied (or instantaneous) strain-rate from split Hopkinson pressure bar (SHPB) experiments is proposed. The conventional constant strain-rate requirement in SHPB test is purposely relaxed to generate rich dynamic flow stress data (FSD) which are widely and diversely distributed in the plastic strain and strain-rate space. Data qualification criteria were proposed to screen the raw FSD, with which qualified FSD (a coarsely filled matrix) were obtained. The qualified FSD were used to train the Artificial Neural Network (ANN) to obtain finely filled FSD, which were decomposed using Singular Value Decomposition (SVD) method. The flow stress equation can be obtained from the SVD results with high accuracy. In addition, the flow stress equation based on the conventional method was established and evaluated. Five uncertainties inherent in the conventional method in the determination of the flow stress equation were identified. The comparison between the proposed and the conventional flow stress equations demonstrates the effectiveness and reliability of the flow stress equation obtained from the proposed method.
AB - In this study, a framework to determine the dynamic flow stress equation of materials based on the discrete data of varied (or instantaneous) strain-rate from split Hopkinson pressure bar (SHPB) experiments is proposed. The conventional constant strain-rate requirement in SHPB test is purposely relaxed to generate rich dynamic flow stress data (FSD) which are widely and diversely distributed in the plastic strain and strain-rate space. Data qualification criteria were proposed to screen the raw FSD, with which qualified FSD (a coarsely filled matrix) were obtained. The qualified FSD were used to train the Artificial Neural Network (ANN) to obtain finely filled FSD, which were decomposed using Singular Value Decomposition (SVD) method. The flow stress equation can be obtained from the SVD results with high accuracy. In addition, the flow stress equation based on the conventional method was established and evaluated. Five uncertainties inherent in the conventional method in the determination of the flow stress equation were identified. The comparison between the proposed and the conventional flow stress equations demonstrates the effectiveness and reliability of the flow stress equation obtained from the proposed method.
KW - Artificial neural network (ANN)
KW - Dynamic flow stress
KW - Singular value decomposition (SVD)
KW - Split Hopkinson pressure bar (SHPB)
UR - http://www.scopus.com/inward/record.url?scp=105007138731&partnerID=8YFLogxK
U2 - 10.1016/j.ijimpeng.2025.105403
DO - 10.1016/j.ijimpeng.2025.105403
M3 - Article
AN - SCOPUS:105007138731
SN - 0734-743X
VL - 206
JO - International Journal of Impact Engineering
JF - International Journal of Impact Engineering
M1 - 105403
ER -