Data science for finite strain mechanical science of ductile materials

Modesar Shakoor, Orion L. Kafka, Cheng Yu, Wing Kam Liu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


A mechanical science of materials, based on data science, is formulated to predict process–structure–property–performance relationships. Sampling techniques are used to build a training database, which is then compressed using unsupervised learning methods, and finally used to generate predictions by means of supervised learning methods or mechanistic equations. The method presented in this paper relies on an a priori deterministic sampling of the solution space, a K-means clustering method, and a mechanistic Lippmann–Schwinger equation solved using a self-consistent scheme. This method is formulated in a finite strain setting in order to model the large plastic strains that develop during metal forming processes. An efficient implementation of an inclusion fragmentation model is introduced in order to model this micromechanism in a clustered discretization. With the addition of a fatigue strength prediction method also based on data science, process–structure–property–performance relationships can be predicted in the case of cold-drawn NiTi tubes.

Original languageEnglish (US)
Pages (from-to)33-45
Number of pages13
JournalComputational Mechanics
Issue number1
StatePublished - Jul 15 2019


  • Data science
  • Large deformation
  • Micromechanics
  • Reduced order modeling

ASJC Scopus subject areas

  • Computational Mechanics
  • Ocean Engineering
  • Mechanical Engineering
  • Computational Theory and Mathematics
  • Computational Mathematics
  • Applied Mathematics


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