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The stochastic modeling and calibration of an anisotropic elasto-plastic model for additive manufacturing materials are addressed in this work. We specifically focus on 316L stainless steel, produced by directed energy deposition. Tensile specimens machined from two additive manufactured (AM) box-structures were used to characterize material anisotropy and random spatial variations in elasticity and plasticity material parameters. Tensile specimens were cut parallel (horizontal) and perpendicular (vertical) to the AM deposition plane and were indexed by location. These results show substantial variability in both regimes, with fluctuation levels that differ between specimens loaded in the parallel and perpendicular build directions. Stochastic representations for the stiffness and Hill’s criterion coefficients random fields are presented next. Information-theoretic models are derived within the class of translation random fields, with the aim of promoting identifiability with limited data. The approach allows for the constitutive models to be generated on arbitrary geometries, using the so- called stochastic partial differential approach (to sampling). These representations are then partially calibrated using the aforementioned experimental results, hence enabling subsequent propagation analyses. Sampling is finally exemplified on the considered structure.
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Process–Structure–Property Simulation Approach to the Estimation of Tensile Anisotropy in 3D Printed Meta‑stable ˇ Titanium Alloy
Developing accurate process–structure–property models for metal additive manufacturing is crucial due to the numerous process parameters, extended build times, and high material costs which make it impractical to rely solely on an experimental trial and error approach when optimizing the process. In this work, a multiscale digital approach to estimate tensile anisotropy along selective laser melted titanium meta-stable alloys is presented. The approach uses a component scale thermal FEA model of the process to calculate temperature, a meso-scale phase field model to calculate microstructure evolution, and a microscale crystal plasticity model to calculate the effect of texture on the tensile properties in different directions. The model has predicted isotropic yield strength for this material, which could guide designers to choose orientations freely. However, anisotropy in hardening behavior could be expected but is caused by porosity and cracking, which are not considered in the presented models. We believe the presented approach, which relies solely on easy to use commercial simulation tools, lays a good foundation for the development of process–structure–property models to optimize process parameters. The modeling approach should be applicable to other mechanical properties and materials with appropriate considerations.
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- Award ID(s):
- 2316628
- PAR ID:
- 10523541
- Publisher / Repository:
- Springer Nature
- Date Published:
- Journal Name:
- Integrating materials and manufacturing innovation
- ISSN:
- 2193-9772
- Subject(s) / Keyword(s):
- Process–structure–property–performance · Additive manufacturing · FEA · Phase field model · Crystal plasticity
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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