Materials Design and Processing Optimization for Advanced Manufacturing: From Fundamentals to Application: Materials Design and Processing Optimization: Young Investigator Session II
Sponsored by: TMS Structural Materials Division, TMS: Alloy Phases Committee
Program Organizers: Wei Xiong, University of Pittsburgh; Dana Frankel, Apple Inc; Gregory Olson, Massachusetts Institute of Technology
Monday 2:00 PM
February 28, 2022
Room: 253B
Location: Anaheim Convention Center
Session Chair: Wei Xiong, University of Pittsburgh; Dana Frankel, QuesTek Innovations LLC
2:00 PM Invited
Composition and Thermo-mechanical Processing Optimization towards Superior Steels: Two Case Studies: C. Tasan1; Gianluca Roscioli1; Menglei Jiang1; Hyun Oh1; 1Massachusetts Institute of Technology
Different strategies can be employed to design steels with superior fracture resistance. In this talk we will focus on two case studies. First, focusing on steels with reverted metastable austenite, we will discuss how composition optimization may improve austenite stability for optimal transformation toughening behaviour. Second, focusing on steels for cutting applications, we will demonstrate how custom design of thermo-mechanical processing can reduce chipping tendency in hard steels. In both of these studies we employ various microstructure characterization techniques (e.g. electron backscatter diffraction, electron channelling contrast imaging) as well as in-situ mechanical testing approaches (e.g. in-situ SEM tensile testing, in-situ SEM nanoindentation) to demonstrate the consequences of the introduced changes in processing.
2:25 PM Invited
Understanding Welding Metallurgy and Process-structure-property Relationships for Advanced Manufacturing: High-throughput Experimental Techniques Guided by Thermodynamic Modeling: Carolin Fink1; 1Ohio State University
The ability to be joined and formed into complex shaped components is an important constraint for the use of structural alloys. Fusion-based welding and associated advanced manufacturing processes, such as metal additive manufacturing are critical fabrication techniques that are challenging due to their non-isothermal nature and complex chemical and physical reactions. Microstructures and properties of the welds and/or deposits directly affect service performance. Hence, a detailed understanding of the composition-process-structure-property relationships is increasingly important for new material design and processing optimization. This talk will highlight two areas of current research that utilize a combination of thermodynamic modeling and high-throughput experiments: (1) Design of weldable multi-principle element alloys for structural use; and (2) Process-structure-property relationship for cryogenic cooling assisted wire arc additive manufacturing of nickel-aluminum bronze.
2:50 PM Invited
Development and Manufacturing of Solid-solution or Precipitation-strengthened Multi-principal Element Alloys with Superior Properties: Haiming Wen1; Matthew Luebbe1; Hans Pommerenke1; 1Missouri University of Science and Technology
Conventional alloy design based on single-principal element systems limits the space and flexibility to explore alloy chemistries and optimize microstructures. The concept of multi-principal element alloys (MPEAs, or high-entropy alloys) has revolutionized traditional alloy design strategies, creating tremendous opportunities to explore new alloys in the vast compositional space. However, the specific properties depend on the specific composition, processing and microstructure, and the composition-process-structure-property relationships need to be well understood. In this study, solid-solution or precipitation-strengthened MPEAs with different composition and microstructure were processed by conventional manufacturing (casting followed by rolling and heat treatment) or advanced manufacturing techniques (including powder metallurgy route involving rapid sintering, additive manufacturing, and severe plastic deformation). The microstructure of the fabricated MPEAs were carefully studied utilizing advanced microstructural characterization techniques. Mechanical properties and irradiation behavior were investigated. Results provide some insights to optimize the design and processing of MPEAs to achieve superior mechanical and physical properties.
3:15 PM Invited
Deformation and Failure of Cold Sprayed Metal Matrix Composites: A Synchrotron X-ray Approach: Lewei He1; Mostafa Hassani1; 1Cornell University
While additive manufacturing of monolithic materials, e.g., metals or ceramics, has seen significant recent progress, the development of the additive manufacturing of composite materials for structural application is still at its early stage. Developing a fundamental understanding of the process-microstructure-property relationships for advanced/additive processes is a critical step toward a further proliferation of this class of materials in critical applications. Here we use a solid-state additive process, namely, cold spray, to fabricate free-standing Ni-CrC composites. We use advanced microstructural and mechanical characterizations, including electron microscopy, in-situ high-energy X-ray diffraction, and computed tomography, to study this material's microstructure and mechanical response. In particular, we focus on the phase-specific response and discuss the load partitioning and load transfer between the two phases of Ni-CrC composites during loading. We find different mechanistic responses under tension and compression. We explore post-spray modifications to optimize the mechanical behavior of the Ni-CrC composites.
3:40 PM Break
3:55 PM Invited
Short-range Order and Its Impacts on the BCC NbMoTaW Multi-principal Element Alloy by Machine-learning Potentials: XiaoXiang Yu1; Qiang Zhu2; YunJiang Wang3; Lin Li4; 1Northwestern University; 2University of Nevada, Las Vegas; 3Institute of Mechanics, Chinese Academy of Sciences; 4The University of Alabama
Body-centered cubic (BCC) refractory multi-principal element alloys (MPEAs) show exceptional mechanical properties compared to conventional BCC alloys. The mechanistic origin of superior strength is yet under-explored, and the question remains open to the influence of short-range order (SRO) on the strengthening mechanisms. Here we employ a machine-learning force field trained by a neural network (NN) with bispectrum coefficients as descriptors. The NN interatomic potential provides a transferable force field with density functional theory accuracy. We apply this novel NN potential in the following hybrid molecular dynamics/Monte Carlo simulations to elucidate the complicated interplay between SRO, phase stability, dislocation core structures, plasticity, and strength in the NbMoTaW MPEA. This approach enables rapid high-throughput screening through a vast compositional space, paves the way for computation-guided materials design of new MPEAs with better performance, and opens avenues for tuning the mechanical properties by processing optimization.
4:20 PM Invited
Local Electronic Descriptors for Defect Properties of bcc Refractory Alloys: Yong-Jie Hu1; Liang Qi2; 1Drexel University; 2University of Michigan
The interactions between solute atoms and crystalline defects such as vacancies, dislocations, and grain boundaries are essential in determining alloy properties. First-principles calculation is an ideal tool for investigating solute-defect interactions, but supercell size limitation usually makes it difficult to study the defects involving structural and chemical complexities. Here we present that the solute-defect interaction energies of transition-metal solutes in bcc refractory metals can be effectively predicted using a set of electronic descriptors through a simple linear relationship. The electronic descriptors are obtained by quantifying the variations in the local electronic structures of defects in pure metals but applied for the predictions in alloy systems. The derived prediction model is generally valid for different types of defects and locations of substitutional sites. Additionally, we show that the prediction model established from simple defect structures can efficiently predict the interaction energies and solute-segregation profiles of the defects with complex atomic structures.
4:45 PM Invited
How Deep Learning Can Help with Materials Design: Sara Kadkhodaei1; Ali Davariashtiyani1; 1University of Illinois at Chicago
In recent years, deep learning has emerged as a successful method for a broad range of artificial intelligence tasks in materials design, especially for learning and prediction of complex structure-property relationships at a variety of scales. While the main utility of deep learning is to predict the structure-property relationships on never-seen-before data, the complex and highly non-linear design of neural networks is prone to overfitting to the training data. Here, I will discuss a number of techniques which we have utilized to make our neural network models generalize better to unseen data. I will present two different models: First is a neural network model that is trained to predict the synthesizability of novel crystalline materials. Second is a neural network model which predicts the formation energy of inorganic crystal compounds. I will discuss the utility of these models in accelerating materials discovery and optimization.
5:10 PM Invited
Materials Design of High-melting-point Materials from First Principles, Database, and Machine Learning: Qijun Hong1; 1Arizona State University
We build an accurate and cost-effective method and an automated tool for melting temperature calculation from first-principles . We employ the method and tool to discover the material that has the world’s highest melting temperature, as well as dozens of refractory materials of various types. We build a melting temperature database that contains thousands of high-melting-point materials, from both experiment and computation. Using the database, we then train a machine learning model that aims to predict melting temperature. Novelly predicted materials are computed via first-principles calculations, with their melting temperatures later included in the database to both complement the database and improve the machine learning predictive model. This iterative process facilitates materials design and discovery of high-melting-point materials.