Engineering topics
Publications and source records attributed to Park, Taejoon.
MAD 3 (Material Data Driven Design) is a novel and unique software solution that provides initial plastic anisotropy of polycrystalline metals using crystallographic texture information, developed at Sandia National Laboratories. In this document, we describe the structure and functionality of the current MAD 3 software (v1.01).
Conventional sheet metal forming tooling in the automotive industry is made up of hardened steel and used for mass-production. Prototype tooling made of metal is durable, but it is only used for a small number of parts despite its high cost, contributing heavily to the vehicle development cost. Additive Manufacturing (AM) offers a low-cost alternative for fabricating tooling suitable for low-volume production. Here, this study investigates the feasibility of using AM polymer composite tooling for the stamping of 1.5mm thickness HSS 590 steel sheets through a two-pronged approach – experimental and numerical analysis. Sheet metal stamping experiments were conducted with glass fiber reinforced polycarbonate (GF-PC) AM polymer tooling and their performance was evaluated based on various metrics such as tool deformation and part accuracy. Finite element simulations of the stamping process were performed, which accurately captured the tool deformations seen in experiments with the use of anisotropic material models. The effective use of simulations in optimizing process parameters to achieve the desired final part geometry is also demonstrated. The GF-PC AM polymer composite tooling was found to be well suited for low volume production applications with potential cost savings.
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A hierarchically coupled cellular automata (CA) model, crystal plasticity finite element method (CPFEM), and thermal finite element (FE) model is developed to predict the softening kinetics of the bulged steel tube during non-isothermal annealing. Through the developed model, the kinetics of softening mechanisms including static recovery (SRV) and static recrystallization (SRX), as well as the recrystallization texture are predicted. Later, the Johnson-Mehl-Avrami-Kohnogorov (JMAK) model based on the predicted SRX data is developed to interpret the recrystallization behavior of the material. To perform this study, diverse experimental tests including tube hydroforming (THF), annealing, uniaxial tensile test, hardness test, as well as microstructure observations through optical microscopy and Electron Backscatter Diffraction (EBSD) tests on steel tube are performed. The obtained experimental data are utilized to calibrate and verify the implemented CPFEM model for simulation of THF process, thermal FE model for prediction of the local temperature over annealing time, and CA algorithm for modeling of the softening kinetics and texture evolution throughout the annealing process. The study shows that the predicted deformation characteristics, softening kinetics, recrystallization texture and temperature profile during non-isothermal annealing are in good agreement with experimental data. During the annealing process, a total of four stages for the kinetics of softening mechanisms is observed: No softening; SRV only; SRV dominant; and SRX dominant. During the progress of SRX, the behavior of recrystallization is abruptly changed, confirming that two different mechanisms are controlling the kinetics of transformation.
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Shear Assisted Processing and Extrusion (ShAPE) experimental setup and tooling were adopted for extruding thin-walled AA7075 aluminum tube from as-cast non-homogenized billet material in a single run. The mechanical and microstructural characterizations were performed on the extrudate in the as-extruded condition without subsequent heat treating. It was shown that the ShAPE process developed a significantly refined microstructure with uniform and almost equiaxed grain structure on both hoop and axial cross-sections of the extrudate as well as through the thickness of the material. The pole figures and inverse pole figures of the EBSD data showed an strong shear texture development, and it was found out that axial shear is the dominant deformation mechanism in the regions near the inner surface of the tube, while combined axial and torsional shears are the two dominant modes of deformation near the outer surface of the extrudate. As for the mechanical properties, there was an increase of 150% and 73% in the yield and ultimate strengths of the tube produced using ShAPE process, respectively, and a 18% decrease in maximum uniform plastic elongation compared to the conventionally extruded AA7075-O tube.
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A constitutive law was developed based on the evolutionary yield function to account for the evolution of anisotropy induced by the plastic deformation. For the effective description of anisotropy, the yield stress function and plastic potential were separately defined based on the non-associated flow rule. In particular, for the description of the equivalent status, the accumulated plastic work was employed as an alternative to the accumulated plastic strain. Numerical formulations based on the plastic work were also derived in case the hardening rule, as well as the evolution of the plastic potential and yield stress function, were defined in terms of the plastic work. The developed constitutive law was characterized using the mechanical properties of the multi-phase BAO QP980 steel and niobium sheets at room temperature. From the uniaxial tension tests and the balanced biaxial tension test, separate sets of anisotropic coefficients for each of the plastic potential and yield stress functions were obtained as a function of the plastic work. By comparing with non-evolving yield functions, the importance of the developed constitutive law to properly describe the evolution of the plastic potential and yield function were validated.
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Ever-tightening regulations on fuel economy, and the likely future regulation of carbon emissions, demand persistent innovation in vehicle design to reduce vehicle mass. Classical methods for computational mass reduction include sizing, shape and topology optimization. One of the few remaining options for weight reduction can be found in materials engineering and material design optimization. Apart from considering different types of materials, by adding material diversity and composite materials, an appealing option in automotive design is to engineer steel alloys for the purpose of reducing plate thickness while retaining sufficient strength and ductility required for durability and safety. A project to develop computational material models for advanced high strength steel is currently being executed under the auspices of the United States Automotive Materials Partnership (USAMP) funded by the US Department of Energy. Under this program, new Third Generation Advanced High Strength Steel (i.e., 3GAHSS) are being designed, tested and integrated with the remaining design variables of a benchmark vehicle Finite Element model. The objectives of the project are to integrate atomistic, microstructural, forming and performance models to create an integrated computational materials engineering (ICME) toolkit for 3GAHSS. The mechanical properties of Advanced High Strength Steels (AHSS) are controlled by many factors, including phase composition and distribution in the overall microstructure, volume fraction, size and morphology of phase constituents as well as stability of the metastable retained austenite phase. The complex phase transformation and deformation mechanisms in these steels make the well-established traditional techniques obsolete, and a multi-scale microstructure-based modeling approach following the ICME [0]strategy was therefore chosen in this project. Multi-scale modeling as a major area of research and development is an outgrowth of the Comprehensive Test Ban Treaty of 1996 which banned surface testing of nuclear devices [1]. This had the effect that experimental work was reduced from large scale tests to multiscale experiments to provide material models with validation at different length scales. In the subsequent years industry realized that multi-scale modeling and simulation-based design were transferable to the design optimization of any structural system. Horstemeyer [1] lists a number of advantages of the use of multiscale modeling. Among these are: the reduction of product development time by alleviating costly trial-and-error iterations as well as the reduction of product costs through innovations in material, product and process designs. Multi-scale modeling can reduce the number of costly large scale experiments and can increase product quality by providing more accurate predictions. Research tends to be focussed on each particular length scale, which enhances accuracy in the long term. This paper serves as an introduction to the LS-OPT and LS-DYNA methodology for multi-scale modeling. It mainly focuses on an approach to integrate material identification using material models of different length scales. As an example, a multi-scale material identification strategy, consisting of a Crystal Plasticity (CP) material model and a homogenized State Variable (SV) model, is discussed and the parameter identification of the individual material models of different length scales is demonstrated. The paper concludes with thoughts on integrating the multi-scale methodology into the overall vehicle design.