Parameterization of a Non-local Crystal Plasticity Model for Tempered Lath Martensite Using Nanoindentation and Inverse Method

Engels, Jenni K.; Vajragupta, Napat; Hartmaier, Alexander

doi:10.3389/fmats.2019.00247

Cited by 15 publications

(3 citation statements)

References 68 publications

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“…The general feasibility of obtaining true material parameters from the inverse analysis of indentation results has been demonstrated by Schmaling and Hartmaier [60] for conventional hardness tests. In more recent work, the reliability of this inverse method has been shown for nanoindentation [61] and also for cyclic indentations [62]. Thus, nanoindentation tests were performed on the Al and Si-rich phases of the AB and HT states using a Berkovich indenter.…”

Section: Identification Of Materials Parametersmentioning

confidence: 99%

Micromechanical Modeling of AlSi10Mg Processed by Laser-Based Additive Manufacturing: From as-Built to Heat-Treated Microstructures

Rajan¹,

Krochmal

Wegener

et al. 2022

Materials

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The unique microstructure of the alloy AlSi10Mg produced by the laser-based powder bed fusion of metals (PBF-LB/M) provides high-strength and high-strain-hardening capabilities of the material. The microstructure and mechanical properties of 3D-printed, i.e., additively manufactured, AlSi10Mg are significantly altered by post-building heat-treatment processes applied in order to tailor the final properties of the parts. Using an accurate computational model to predict and improve the mechanical performance of 3D-printed samples considering their microstructural features can accelerate their employment in envisaged applications. The present study aims to investigate the correlation between microstructural features and the mechanical behavior of as-built, direct-aged, and T6 heat-treated samples of PBF-LB/M AlSi10Mg under tensile loading using experiment and microstructure-sensitive modeling approaches. Nanoindentation tests are used to calibrate the parameters of the constitutive models for the Al and Si-rich phases. The experimental investigations revealed that heat treatment significantly changes the sub-grain morphology of the Si-rich phase, and this can have a considerable effect on the mechanical behavior of the components. The effect of the modeling of the Si-rich phase in the representative volume elements on the prediction of mechanical behavior is investigated using the J2 plasticity model. The combination of the crystal plasticity model for Al and the J2 plasticity model for the Si-rich phase is used to predict the tensile properties of the as-built and heat-treated states. The predicted results are in good agreement with the experimental results. This approach can be used to understand the microstructure–property relationship of PBF-LB/M AlSi10Mg and eventually tailor heat treatment for PBF-LB/M AlSi10Mg based on the requirement of the application.

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Section: Identification Of Materials Parametersmentioning

confidence: 99%

Micromechanical Modeling of AlSi10Mg Processed by Laser-Based Additive Manufacturing: From as-Built to Heat-Treated Microstructures

Rajan¹,

Krochmal

Wegener

et al. 2022

Materials

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“…Dedicated approaches include micro-tensile experiments (Gianola and Eberl 2009), ultrasonic testing (Huntington 1947) or high-energy x-ray diffraction (Dawson et al 2018). Also, nano-indentation (Liu et al 2020;Chakraborty and Eisenlohr 2017;Engels et al 2019) or milled micropillars (Cruzado et al 2015) may be used for calibrating the material parameters for the single crystal model, also taking into account the strain pole-figures (Wielewski et al (2017).…”

Section: Introductionmentioning

confidence: 99%

“…For identifying the CP parameters from single-crystal measurements, a number of rather classical strategies, like the simplex method (Chakraborty and Eisenlohr 2017) or the Nelder-Mead algorithm (Engels et al 2019), were reported to be effective. Another prevalent method to calibrate crystal plasticity parameters is the use of genetic algorithms (Prithivirajan and Sangid 2018;Kapoor et al 2021;Bandyopadhyay et al 2020;Guery et al 2016;Pagan et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Identifying material parameters in crystal plasticity by Bayesian optimization

et al. 2021

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In this work, we advocate using Bayesian techniques for inversely identifying material parameters for multiscale crystal plasticity models. Multiscale approaches for modeling polycrystalline materials may significantly reduce the effort necessary for characterizing such material models experimentally, in particular when a large number of cycles is considered, as typical for fatigue applications. Even when appropriate microstructures and microscopic material models are identified, calibrating the individual parameters of the model to some experimental data is necessary for industrial use, and the task is formidable as even a single simulation run is time consuming (although less expensive than a corresponding experiment). For solving this problem, we investigate Gaussian process based Bayesian optimization, which iteratively builds up and improves a surrogate model of the objective function, at the same time accounting for uncertainties encountered during the optimization process. We describe the approach in detail, calibrating the material parameters of a high-strength steel as an application. We demonstrate that the proposed method improves upon comparable approaches based on an evolutionary algorithm and performing derivative-free methods.

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Crystal Plasticity

Diehl

2024

Comprehensive Mechanics of Materials

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Parameterization of a Non-local Crystal Plasticity Model for Tempered Lath Martensite Using Nanoindentation and Inverse Method

Cited by 15 publications

References 68 publications

Micromechanical Modeling of AlSi10Mg Processed by Laser-Based Additive Manufacturing: From as-Built to Heat-Treated Microstructures

Micromechanical Modeling of AlSi10Mg Processed by Laser-Based Additive Manufacturing: From as-Built to Heat-Treated Microstructures

Identifying material parameters in crystal plasticity by Bayesian optimization

Crystal Plasticity

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