Machine Learning Algorithms Applied to the Identification of CPB06 Yield Criterion Parameters

Abstract:

In this work, a new methodology for the identification of the CPB06 yield criterion parameters is presented. This methodology is based on the application of Machine Learning models and the Levenberg-Marquardt optimization algorithm. The proposed methodology relies on data obtained from a biaxial tensile test with a cruciform specimen and aims to overcome some of the challenges usually faced during material characterization with the CPB06 yield criterion. The predictive performances achieved were positive overall, when comparing the yield surfaces obtained for testing cases, highlighting the potential of the proposed methodology.

• Periodical: Key Engineering Materials (Volume 1049)
• Pages: 89-98
• DOI: https://doi.org/10.4028/p-Jot8jG
• Online since: April 2026
• Authors: Armando Marques*, Tomás Parreira, Bernardete Ribeiro, Pedro Prates, André Pereira
• Keywords: Constitutive Modelling, Machine Learning, Parameter Identification

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