Material Model Calibration Using Machine Learning: A Comparative Study


  • Mariana Seabra University of Porto Faculty of Engineering, Porto, Portugal
  • Ana Costa University of Porto Faculty of Engineering, Porto, Portugal



neural network, material properties, Duplex Stainless Steel, multiscale modeling


A methodology based on Machine Learning, namely Fully Connected Neural Networks, is proposed to replace traditional parameter calibration strategies. In particular, the relation between hardness, yield strength and tensile strength is explored. The proposed methodology is used to predict the yield strength and the tensile strength of a Super Duplex Stainless Steel that was not included in the neural network training data base. Moreover, it is also used to determine such material parameters for individual microstructural phases, which feed a multiscale Finite Element simulation. The methodology is experimentally validated.


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Author Biographies

Mariana Seabra, University of Porto Faculty of Engineering, Porto, Portugal

Mariana Seabra is an Invited Auxiliary Professor at the Faculty of Engineering, University of Porto. Her research career has been devoted to numerical methods, in particular the Finite Element Method applied to ductile damage, fracture and fatigue problems. More recently it has been focused on machine learning methods and its application to structural mechanics and material science. She is also a member of the LAETA research group.

Ana Costa, University of Porto Faculty of Engineering, Porto, Portugal

Ana Costa is a PhD student at the Faculty of Engineering, University of Porto, working on material science, machine learning methods and its application to structural mechanics. Her research career started as a volunteer work at a physics laboratory and, later, in a mechanical construction materials laboratory. During her degree period, she also worked as an intern at FCA Fiat Chrysler Automóveis. Finally, she enrolled the project of Multi-Scale Methodologies with Order Reduction Models for Advanced Materials and Processes as a research fellow at INEGI during her master’s degree.


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