Open AccessOn the reliability of yield functions in deep drawing simulations
Author(s) -
H Ghiabakloo,
Niko Manopulo,
J Mora,
B.D. Carleer,
Albert Van Bael
Publication year - 2022
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1238/1/012073
Subject(s) - yield (engineering) , blank , deep drawing , calibration , function (biology) , process (computing) , experimental data , reliability (semiconductor) , sensitivity (control systems) , sheet metal , aluminium , materials science , plane (geometry) , mechanics , computer science , mathematics , geometry , engineering , metallurgy , composite material , thermodynamics , physics , statistics , evolutionary biology , electronic engineering , biology , operating system , power (physics)