Predictive tools used in simulating wrought alloy manufacturing processes and final product performance predominantly employ phenomenological constitutive theories. Such models are proving inadequate for various applications. On the other hand, physics based crystal plasticity models have enjoyed remarkable success in predicting anisotropic mechanical response and the concurrent evolution of the underlying crystallographic texture in finite plastic deformation of several polycrystalline metals. However, these tools are extremely computationally expensive. It is proposed to develop spectral crystal plasticity based finite element tools for simulating deformation processing operations and mechanical performance of Advanced High Strength Steels (AHSS) with computational times that are comparable to the tools currently used by the industry.
AHSS represent a broad class of steels with dramatic improvements in properties over traditional steels. The proposed collaboration with two major industries will ensure that the tools developed will be rapidly adopted into the commercial environment. The proposed interdisciplinary research cuts across materials science, mechanical engineering and applied mathematics and will contribute significantly to the development of skilled human resources in critical science and technology fields. Furthermore, funding for this proposal will positively impact the initiatives already underway at Drexel University to ensure inclusion of traditionally under-represented minorities and women in engineering.
Manufacturing process simulation tools are critical for accelerating the design, development, and deployment of new/improved materials in emerging technologies. For advanced structural materials, the considerations often involve plastic deformation and require the use of multiscale materials simulations using finite element tools. Currently, these tools predominantly employ phenomenological constitutive theories for the macroscale material behavior. Although physics based crystal plasticity models have enjoyed remarkable success in predicting anisotropic mechanical response of several polycrystalline metals and in predicting the concurrent evolution of the underlying microstructure (mainly crystallographic texture) in finite plastic deformation, their high computational cost has impeded their broad adoption by the metal working/shaping industry. This work has pioneered a new approach for incorporating core materials knowledge into manufacturing simulation tools. More specifically, it was demonstrated that it is possible to capture the materials knowledge at the lower length scales into highly efficient spectral databases that can then be seamlessly integrated with manufacturing simulation tools. This project has successfully demonstrated that the incorporation of the spectral databases for the fast computation of crystal plasticity calculations improves the computational speed by about 100 times. This translates to an improvement of the computational speed of crystal plasticity based simulations in the commercial finite element code ABAQUS by about 40 times. These remarkable reductions come with very minimal loss of accuracy (about an avergae error of 1.5%). This project produced two PhD students who are now in junior academic positions (one in the US and the other in a foreign university). The overall approach developed in this project provides a completely new direction of future research for computationally efficient scale-bridging in multiscale materials simulations. The approach developed here is the very first of its kind demonstrating the benefits of integrating data-science approaches with the conventional computational mechanics and materials approaches. The overall approach and philosophy utilized and executed in this project is broadly applicable to a wide variety of multiscale materials phenomena
