Integrating stochastic effects and uncertainties into inverse analysis of hot bulk forging processes through automated API-driven finite element simulations and machine learning
Artem Alimov, Yuyao Jiang, Markus Gardill, Sebastian Härtel
Abstract. At present, finite element simulation and inverse analysis are widely used for the design and analysis of metal forming processes, allowing the identification of possible defects at the pre-production stage and realizing the “First Part – Good Part” strategy. However, the forging process is generally considered in FEM simulations as a deterministic process without taking into account variations in material properties and process parameters, which leads to reduced forgings quality and process reliability. Considering uncertainties and stochastic effects is one of the major challenges in FEM simulations. Inverse analysis can be used to identify, characterize and integrate the uncertainties and stochastic effects into forging process analysis. Several works have attempted to include stochastic effects into sheet metal forming simulation as well as simulation of hot forging of turbine blades. However, issues of automating and utilizing inverse analysis in this field remain open and require further development. This paper develops an approach for analyzing uncertainties and stochastic effects based on automated API-driven finite element simulations. A control code that automatically varies the dimensions, position and orientation of the workpiece as well as process parameters, starts the simulation and performs automatic evaluation of the simulation results has been developed. Based on the obtained dataset, a parameter sensitivity analysis was performed and the relative importance of each parameter on the formation of defects such as folding or underfilling was determined. The developed approach is further verified through experimental trials on an industrial screw press equipped with a data acquisition system.
Keywords
Stochastic Effects, Hot Bulk Forging, Inverse Analysis, FE-Simulation, Machine Learning
Published online 5/7/2025, 10 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Artem Alimov, Yuyao Jiang, Markus Gardill, Sebastian Härtel, Integrating stochastic effects and uncertainties into inverse analysis of hot bulk forging processes through automated API-driven finite element simulations and machine learning, Materials Research Proceedings, Vol. 54, pp 1528-1537, 2025
DOI: https://doi.org/10.21741/9781644903599-165
The article was published as article 165 of the book Material Forming
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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