{"title":"Bayesian protocols for high-throughput identification of kinematic hardening model forms","authors":"","doi":"10.1016/j.ijmecsci.2024.109791","DOIUrl":null,"url":null,"abstract":"<div><div>Constitutive models are essential for assessing the mechanical response of complex materials, yet uncertainties in model forms and parameters persist due to the influence of micromechanisms and microstructural features. We develop Bayesian protocols to iteratively refine both model forms and the associated material properties for complex constitutive models. Our aim is to provide rigorous, probabilistically informed evaluations of improvements achieved with increasing model complexity. Leveraging high-throughput experimental microindentation data, the protocols involve three steps: (i) emulating FE simulations using multi-output Gaussian process surrogate models, (ii) calibrating an initial simple constitutive model against experimental data, and (iii) progressively enhancing model complexity by iteratively improving agreement between simulations and experiments. The various model forms are compared using model form probabilities and aggregate discrepancies. Sobol indices are used to quantify the identifiability of material properties, aiming to prevent parameter proliferation. We apply this protocol to identify the optimal form of cyclic plasticity models for duplex Ti-6Al-4V. Although tailored for cyclic plasticity models, these protocols hold promise for calibrating and refining nonlinear constitutive models across diverse material classes.</div></div>","PeriodicalId":56287,"journal":{"name":"International Journal of Mechanical Sciences","volume":null,"pages":null},"PeriodicalIF":7.1000,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Mechanical Sciences","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0020740324008324","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Constitutive models are essential for assessing the mechanical response of complex materials, yet uncertainties in model forms and parameters persist due to the influence of micromechanisms and microstructural features. We develop Bayesian protocols to iteratively refine both model forms and the associated material properties for complex constitutive models. Our aim is to provide rigorous, probabilistically informed evaluations of improvements achieved with increasing model complexity. Leveraging high-throughput experimental microindentation data, the protocols involve three steps: (i) emulating FE simulations using multi-output Gaussian process surrogate models, (ii) calibrating an initial simple constitutive model against experimental data, and (iii) progressively enhancing model complexity by iteratively improving agreement between simulations and experiments. The various model forms are compared using model form probabilities and aggregate discrepancies. Sobol indices are used to quantify the identifiability of material properties, aiming to prevent parameter proliferation. We apply this protocol to identify the optimal form of cyclic plasticity models for duplex Ti-6Al-4V. Although tailored for cyclic plasticity models, these protocols hold promise for calibrating and refining nonlinear constitutive models across diverse material classes.
期刊介绍:
The International Journal of Mechanical Sciences (IJMS) serves as a global platform for the publication and dissemination of original research that contributes to a deeper scientific understanding of the fundamental disciplines within mechanical, civil, and material engineering.
The primary focus of IJMS is to showcase innovative and ground-breaking work that utilizes analytical and computational modeling techniques, such as Finite Element Method (FEM), Boundary Element Method (BEM), and mesh-free methods, among others. These modeling methods are applied to diverse fields including rigid-body mechanics (e.g., dynamics, vibration, stability), structural mechanics, metal forming, advanced materials (e.g., metals, composites, cellular, smart) behavior and applications, impact mechanics, strain localization, and other nonlinear effects (e.g., large deflections, plasticity, fracture).
Additionally, IJMS covers the realms of fluid mechanics (both external and internal flows), tribology, thermodynamics, and materials processing. These subjects collectively form the core of the journal's content.
In summary, IJMS provides a prestigious platform for researchers to present their original contributions, shedding light on analytical and computational modeling methods in various areas of mechanical engineering, as well as exploring the behavior and application of advanced materials, fluid mechanics, thermodynamics, and materials processing.