Please join us for a seminar with Thomas Voisin, deputy group leader for Metallurgy and Advanced Microscopy and staff scientist in the Materials Science Division at Lawrence Livermore National Laboratory (LLNL). The seminar is titled “Microstructures, deformation mechanisms, and corrosion properties of additively manufactured 316L stainless steels.”

The seminar will begin at 10:30 AM on Friday, May 31 in Malone Hall G33/35.

Abstract: At LLNL, laser powder-bed-fusion (L-PBF) has become an additive manufacturing (AM) technique of choice to manufacture complex components, but also to obtain materials with enhanced properties. Rapid thermomechanical cycles during L-PBF retain non-equilibrium microstructures in as-fabricated metallic alloys, most often resulting in improved properties. For L-PBF 316L stainless steels (316LSS), this means breaking the strength/ductility tradeoff and improving resistance to pitting corrosion in seawater.
For mechanical properties, rapid-solidification cellular structures provide an explanation for the significant increase in strength, but much is left to understand concerning the high ductility. We will show that specific dislocation configurations at cell walls trigger mechanical twinning at low stress, which enhances plasticity.
For corrosion, prior studies have shown that the high resistance to pitting in seawater is due to the absence of Mn sulfides. However, pitting mechanisms on as-built surfaces remain largely unknown. We will show that slags that form during LPBF act as pit nucleation sites and the surface oxide on as-built parts contains a thin layer of Mn-rich silicon oxide that formed at high temperature during LPBF, offering exceptional corrosion protection.

This work was performed under the auspices of the U.S. Department of Energy by LLNL under Contract DE-AC52-07NA27344.

Bio: Thomas Voisin is deputy group leader for Metallurgy and Advanced Microscopy and staff scientist in the Materials Science Division at LLNL, leading several projects investigating the relationship between processing, microstructures, and mechanical or corrosion properties of additively manufactured metallic alloys. He is involved in various activities, including fundamental study of plastic deformation of metals and development of new high entropy alloys (refractory and eutectic). More generally, Thomas actively focuses on developing a coherent research group where different in-situ characterization techniques, from atomic to macroscopic scales, work together to accelerate our understanding of metals properties.

Please join us for a seminar with Songbai Ji, a professor of biomedical engineering at Worcester Polytechnic Institute (WPI). The seminar is titled “Sex-specific, multiscale and large-scale modeling of traumatic brain injury.”

The seminar will begin at 11:00 AM on Wednesday, April 24 in Malone Hall G33.

Abstract: Computational models of the brain play a pivotal role in studying the mechanisms of traumatic brain injury and designing mitigation strategies. In this talk, I will share thoughts about how to extend state-of-the-art by combining a global brain injury model with axonal injury models at the microscale through a multiscale modeling approach. The axonal injury models will incorporate significant sex differences in morphology, which are related to the observed sex differences in concussion based on initial findings. Finally, advanced deep learning techniques will be used to dramatically reduce the simulation cost form hours to under a second. This will enable large-scale modeling of head impacts to conduct population-based injury studies, as opposed to being limited to a single head impact for a specific individual. With wearable head impact sensors more widely adopted, these tools could pave the way for improved onsite injury detection during practices and games for contact sports players and other practical applications, such as better design of protective headgears.

Bio: Dr. Songbai Ji is a Professor of Biomedical Engineering at the Worcester Polytechnic Institute (WPI), Massachusetts, a Sigma Xi Outstanding Senior Researcher. He has more than 20 years’ experience in impact biomechanics of the brain with more than 100 referred journal papers and numerous conference proceeding papers published. His work in this area is best known for the Worcester Head Injury Model (WHIM), which has been continuously funded by the NIH, NSF, and industry for its development. He serves as an associate editor for a number of journals, including Journal of Biomechanical Engineering, Frontier, etc.. Recently, he co-led an international committee for a consensus white paper on “best practices” of brain impact modeling for sports-related concussion (Ji et al., ABME 2022).

Please join us for a seminar with Dr. Tian Xie, a senior researcher and project lead at Microsoft Research AI4Science. The seminar is titled “MatterGen: a generative model for inorganic materials design.”

The seminar will begin at 2:30 PM on Friday, Feb. 23 in Gillman Hall 50.

This seminar will also be accessible virtually. Connection information will be distributed the morning of the seminar via email. Those interested in attending who are not on HEMI’s email list can reach out to Sarah Preis at [email protected] for connection information.

Bio: Tian Xie is a senior researcher and project lead at Microsoft Research AI4Science. He leads a team of researchers, engineers, and program manager to develop the next generation machine learning models for materials discovery. Before joining Microsoft, he was a postdoc in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT from 2020 to 2022, co-advised by Tommi Jaakkola and Regina Barzilay. He got his PhD in Materials Science and Engineering at MIT in 2020, advised by Jeffrey C. Grossman. Tian is most known for his research in graph representation learning and generative models for materials, including widely used models like CGCNN and CDVAE.

Please join us for a seminar with Prof. Moataz Attallah of the University of Birmingham. The seminar is titled “Accelerating Materials and Process Development in Additive Manufacturing.”

The seminar will begin at 3:00 PM on Monday, Dec. 4 in Malone Hall 137.

This seminar will also be accessible via Zoom. Connection information will be distributed the morning of the seminar via email. Those interested in attending who are not on HEMI’s email list can reach out to Sarah Preis at [email protected] for connection information.

Bio: Professor Moataz Attallah holds a chair in advanced materials processing at the School of Metallurgy and Materials University of Birmingham, where he leads the Advanced Materials & Processing Lab (AMPLab). His research focuses on metallic materials processing, with an emphasis on laser-based additive manufacturing, AM post-processing strategies, and novel applications of metal AM in the aerospace, nuclear, defence, motor racing, space, and telecommunications sectors. He sits on the advisory board and provides consultancy to companies and universities in Europe, North America, the Middle East and Asia. He co-authored over 200 scientific reports and 3 book chapters, as well as being a co-inventor on 5 granted patents.

Please join us for a seminar with Prof. Richard Regueiro, University of Colorado Boulder, titled “Overview of Center for micromorphic multiphysics porous and particulate materials simulations within exascale computing workflows.”

The seminar will begin at 11:00 AM on Friday, Nov. 10 in Malone Hall 137.

Bio: Professor Richard Regueiro received his PhD in Civil and Environmental Engineering at Stanford University in 1998. He then became a member of the technical staff at Sandia National Laboratories, California, from 1998 to 2005, at which time he began his academic career in the Department of Civil, Environmental, and Architectural Engineering at the University of Colorado Boulder. His research focuses on computational multiscale multiphysics materials modeling for simulating inelastic deformation and failure in heterogeneous porous media, including saturated and partially saturated soils and rock, unbonded particulate materials (e.g. sand, gravel, metallic powders), bonded particulate materials (e.g., sandstone, asphalt, concrete, explosive materials), soft biological tissues (e.g., ocular lens tissue, lung parenchyma, vertebral disk), and thin deformable porous materials and membranes, for instance. Scales of interest range from the microstructural and ultrastructural to the continuum. He is currently Principal Investigator (PI) for an NNSA Advanced Simulation and Computing (ASC) Predictive Science Academic Alliance Program (PSAAP) project, “Center for Micromorphic Multiphysics Porous and Particulate Materials Simulations within Exascale Computing Workflows.”

Acoustic signature and reconstruction of defect avalanches in metals

Acoustic emission (AE) is a physical phenomenon where transient elastic stress waves propagate from a source(s) in a solid material due to an external stimulus (e.g. load). The sources are local regions of irreversible change within the material volume and can be associated with dislocation motion, phase transformations, twinning, crack initiation, and/or crack growth, among others. When these stress waves reach the surface, they can be characterized by a surface displacement, and are commonly detected as a change in the electrical signal output. AE detection is thus a useful tool for non-destructive evaluation (NDE) of materials in structural applications. However, it is challenging to quantify the interplay between different plasticity mechanisms with AE due to the inability to decode the measured acoustic waves into separate signatures of overlapping active mechanisms. In this talk, we will focus on AE signals induced by dislocation activities. The first part will involve the numerical simulations of AE signals by incorporating elasto-dynamic displacements calculations into our in-house Discrete Dislocation Dynamics (DDD) simulations. In the second part, we will discuss our attempts to parse the mechanisms, position and time information out of the AE signals from simulations using machine learning techniques (e.g. Physic Informed Neural Networks).

Junjie Yang is a 2nd year PhD student working with Prof. Jaafar El-Awady and Prof. Tamer Zaki in the Department of Mechanical Engineering at the Johns Hopkins University. His research focuses on dislocation plasticity of metals and alloys using computational methods.