Journal Article

IMPULSE-2026-00119

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png A scientific benchmark for elasto-plastic constitutive modeling - Part II: blind predictions, calibration strategies, and benchmark results

Maier, L. (Corresponding author) ; Walz, E.MLZ* ; Hofmann, M.MLZ* ; Rebelo-Kornmeier, J. ; Juan, R. ; Lian, J. ; Jeong, Y. ; Jeon, B. ; Milch, M. ; Güner, A. ; Maawad, E.MLZ* ; Volk, W. ; Hartmann, C.

2026
Springer Paris [u.a.]

Please use a persistent id in citations: doi:10.1007/s12289-026-01984-1

Abstract: This paper presents the second part of the NUMISHEET 2025 scientific benchmark on elasto-plastic constitutivemodeling. The study builds on the experimental dataset and benchmark problem definition introduced in Part I. Itevaluates 19 blind prediction results submitted by 17 international teams from academia and industry. The benchmark taskincluded calibrating constitutive models for the dual-phase steel DP800HHE, exclusively based on Part I data. The calibratedconstitutive models were used to simulate the validation experiment MUC-Test, without prior access to the experimentalvalidation results. The submitted solutions span a broad spectrum of modeling strategies, ranging from different phenomenologicalhardening models and yield locus formulations to crystal plasticity approaches. A systematic comparison withexperimental ground truth data (comprising punch force-displacement curves and local strain distributions) was conductedusing normalized error metrics. The results of this study reveal the characteristic strengths and limitations of different modelingapproaches. They also highlight the role of anisotropy and hardening law selection. Furthermore, they demonstrate thesensitivity of predictions to calibration strategies. Providing an open dataset, a transparent evaluation methodology, and acomprehensive discussion of outcomes signifies an advancement in reproducibility and objectivity in constitutive modeling.This benchmark serves as a basis for further validation, comparison, and development of constitutive models.

Keyword(s): Engineering, Industrial Materials and Processing (1st) ; Materials Science (2nd)

---

Contributing Institute(s):

1. STRESSI (STRESSI)
2. DEVA (DEVA)

Experiment(s):

1. No specific instrument

Appears in the scientific report 2026

---

Database coverage:
; Clarivate Analytics Master Journal List ; Current Contents - Engineering, Computing and Technology ; DEAL Springer ; DEAL Springer ; Essential Science Indicators ; IF < 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

---