Open AccessOn the use of fixed point translations as input variable for digital twins in deep drawing compared to current methods
Author(s) -
Matthias Ryser,
Pavel Hora,
Markus Bambach
Publication year - 2021
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/1157/1/012084
Subject(s) - blank , computer science , process (computing) , deep drawing , variable (mathematics) , current (fluid) , state variable , process variable , point (geometry) , artificial intelligence , algorithm , mathematics , mechanical engineering , engineering , geometry , mathematical analysis , physics , electrical engineering , thermodynamics , operating system
Recent work aims at the inverse parameter estimation in deep drawing using pretrained surrogate models for the detection of the current process, material or tool parameters. The use of the methodology requires the definition of state variables to describe the current process state. Whereas our recent work makes use of draw-ins and local blankholder forces, other approaches from the literature also use skid-lines measured after the deep drawing process. For the future, the solution with even higher information content would be to detect the global strain distribution on the final part and use it as a state variable for process detection, which has not been documented in the literature to the best knowledge of the authors. In this work, we present a first step into this direction by comparing the surrogate model based parameter estimation by using draw-ins and by using the movement of material fixed points on the blank over the deep drawing process. The result shows that the mathematical methods used for parameter prediction based on draw-ins can directly be used for the prediction with fixed point translations as reference. For the investigations, a cup drawing process is used.