Materials Design and Processing Optimization for Advanced Manufacturing: From Fundamentals to Application: On-Demand Oral Presentations
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 8:00 AM
March 14, 2022
Room: Materials Design
Location: On-Demand Room
High-fidelity Modeling of Multi-material Additive Manufacturing: Wentao Yan1; Yanming Zhang1; Zeshi Yang1; Lu Wang1; 1National University of Singapore
Additive manufacturing possesses promising capabilities of fabricating new materials by mixing multi-material powders and manipulating chemical compositions. In this talk, we will present our work on high-fidelity modeling of the complex additive manufacturing process: from powder spreading (powder bed fusion)/feeding (directed energy deposition) to laser/electron melting. To evaluate the mixture uniformity of the powder layer/stream, we employ the computational fluid dynamics (CFD) and discrete element method (DEM) coupling method to simulate the powder spreading/blowing processes of mixed multi-material powders. To thoroughly understand the material composition distribution in the melting procedure, we develop a multi-physics thermal-fluid flow model to simulate the different melting/flow/solidification behaviours of different powders as well as the interactions between solid powders and molten pool. All these models are validated against experiments, and show appealing potentials to provide guidance for additive manufacturing of mixed powders, e.g., for in-situ alloying for new materials and particle-reinforced composites.
Improving Powder Feedstock for Metal Additive Manufacturing: Danielle Cote1; Kyle Tsaknopoulos1; Jack Grubbs1; Christopher Massar1; Matthew Gleason1; Bryer Sousa1; Matthew Siopis2; 1Worcester Polytechnic Institute; 2CCDC Army Research Laboratory
With recent advances in additive manufacturing (AM) typically concentrated on the techniques and instruments, this research topic focuses on improving the powder feedstock for metal AM. Besides the chemical composition, there are several other factors and alteration methods which affect the materials and mechanical properties of the printed material, and recyclability, handling, spreadability, efficiency, and additional powder properties. This research highlights these non-chemical alterations to the feedstock powder to improve overall printing efficiency and properties. Characterization techniques include nanoindentation, powder rheology, micro-plastometry, microcompression analysis, moisture content analysis, electron microscopy, and more. Examples will be shared using several metallic feedstock powders printed with various metal AM techniques.
Additive Manufacturing of Aluminium: Alloy Design and Machine Learning Assisted Process Optimization: Xiaopeng Li1; Qian Liu1; Jay Kruzic1; 1University of New South Wales
Laser powder bed fusion technique (LPBF) has been widely used to fabricate various aluminium alloys in the past decade. However, due to intrinsic materials characteristics, e.g., solidification cracking, not many aluminium alloys have satisfactory LPBF processability. Therefore, it is in urgent need to design and develop more suitable aluminium alloys and their composites for LPBF. Meanwhile, once new aluminium alloys are designed, it is also of great importance to optimise the LPBF process to achieve high quality components without any apparent processing defects such as cracks or porosity. In this presentation, a novel in-situ alloy design process was first introduced to develop nanoparticle decorated aluminium alloys for LPBF and the resultant microstructure along with mechanical properties were investigated. Following this, a machine-learning assisted LPBF process optimisation process for aluminium alloys is described in detail to provide new insights into the microstructure control and properties manipulation of LPBF fabricated aluminium alloys.
Experimental Determination of Forming Limit Diagram for AISI 304: Krishnam Raju1; Abhishek Kumar1; Sushil Kumar Mishra1; Narasimhan K1; 1IIT, Bombay
Metal forming can be defined as deforming the metal into desired shape without any undesired features. In sheet metal forming, forming limit diagram (FLD) used as limiting strains that sheet metal can sustain without failure. Hence forming limit diagram is a vital tool, that helps in designing the sheet metal component particularly to aerospace and automobile applications. Austenitic stainless steel is promising material for automobile and aerospace application because of its drawablity, weldability in addition to strength and corrosion resistance. The present work involves experimental determination of forming limit for AISI 304 annealed sheets of 1.2 mm. Different samples were tested for various strain conditions and validated by numerial analysis in Pamp stamp 2G simulation. With the help of FLD, formability of AISI 304 Stainless Steel sheet was predicted and found experimental data is in good agreement with simulated data.
ExaCA Workflow and Microstructure Modeling as Part of the ExaAM Framework: Matt Rolchigo1; Jim Belak2; Samuel Reeve1; John Coleman1; Gerry Knapp1; Robert Carson2; Alex Plotkowski1; Lyle Levine3; Eddie Schwalbach4; 1Oak Ridge National Laboratory; 2Lawrence Livermore National Laboratory; 3National Institute of Standards and Technology; 4Air Force Research Laboratory
ExaCA, an exascale-capable cellular automata application, is a microstructure modeling tool and one part of the ExaAM workflow within the Exascale Computing Project (ECP), where the goal is self-consistent modeling of AM processing, microstructure, and properties on upcoming exascale computers. The grain-scale microstructure predicted by CA is crucial within this workflow, driven by the time-temperature history predicted by process models OpenFOAM and Truchas-PBF, is highly sensitive to uncertain solidification input parameters, and in turn strongly influences the resultant properties predicted by ExaConstit. This talk will focus on the data format and workflow considerations for passing time-temperature history to the CA model and passing as-solidified microstructure data from the CA model, as well as the propagation of uncertainties within the workflow. Finally, microstructures predicted by ExaCA for various process conditions will be validated against experimentally obtained grain structures from AM parts and the sources of uncertainty analyzed. Supported by ECP (17-SC-20-SC).
Effect of the Casting Process on the Microstructure and Mechanical Properties of a Cast Ni-based Alloy: Govindarajan Muralidharan1; Shivakant Shukla1; Jiten Shah2; Jim Myers3; 1Oak Ridge National Laboratory; 2PDA LLC; 3Metaltek International
Generation 3 Concentrated Solar Power Systems are being targeted for operating temperatures greater than 700°C. Haynes®282® has the potential to satisfy the high temperature mechanical property requirements but cost and availability are major factors to be considered in its use. Components such as piping, pump and valve bodies can be manufactured at a lower cost using casting processes and hence can be produced on demand thus improving flexibility. This talk will present the effect of casting processes and subsequent heat-treatment on the microstructure, high temperature tensile and short-term creep properties of Haynes®282® and compare these with that obtained from traditional wrought processing. This work was supported by the US Department of Energy - Solar Energy Technologies Office Concentrating Solar Thermal Power Program. This research was conducted by Oak Ridge National Laboratory, which is managed by UT Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE).
A Machine Learning Based Methodology to Predict the Build Quality of Metallic Alloys Additively Manufactured by Laser Powder Bed Fusion: Jeongmin Woo1; Kevin Graydon1; Yongho Sohn1; 1University of Central Florida
Metallic alloys additively manufactured by laser powder bed fusion (LPBF) can suffer from flaw formation either due to lack-of-fusion or keyhole porosity, corresponding to insufficient or excessive transfer, respectively, of energy generated by scanning laser. Relationships among volumetric energy density of LPBF, density of samples produced and thermophysical properties of alloys were examined using the experimental results from Mg (WE43), Al (Al10SiMg), Ti (Ti6Al4V), Fe (316SS, 15-5PH), Ni (IN718, IN625) alloys. A constitutive function based on relevant thermally-activated processes were employed to model the variation in sample density as a function of LPBF volumetric energy density. Coefficients of the functions were determined using a genetic algorithm, and their relations to thermophysical properties of metallic alloys were examined using machine learning. Findings are presented with respect to progress towards prediction methodologies for identifying optimal LPBF parameters for metallic alloys, both commercially available and to be designed in the future.
Design of a High Temperature Precipitates for a Complex Concentrated Alloy Using Model Ternary Alloys: Jaimie Tiley1; Soumya Nag1; Ercan Cakmak1; Fan Zhang2; Y Wang3; Pania Newell3; 1Oak Ridge National Laboratory; 2Computherm LLC; 3University of Utah
To develop/optimize the compositions for high temperature RCCAs, the CALPHAD method was used to estimate the stability of B2 phase. Specifically, Hf, Ta, Nb, and Zr additions to the Al-Ti system were modeled to investigate their impact on B2 stability at high temperatures. Phase evolution was characterization of homogenized samples were conducted using X-ray diffraction at temperatures up to 1200C (in controlled atmosphere). The resulting analysis was coupled with electron microscopy to validate CALPHAD predictions and provide insight into chemical segregation and phase evolution. Results indicate that the addition of Hf greatly reduced the formation of B2 structures, while the addition of Ta increased B2 stability to higher temperatures. Addition of Zr and Nb decreased the Bcc/B2 transition temperature, yet the resultant B2 was still stable at temperatures high enough for practical applications. The resulting microstructures were strength tested using a micro-indentation technique to determine properties up to 800C.
Texture Gradients and Asymmetries in ShAPE Processed ZK60 Magnesium: Benjamin Schuessler1; Dalong Zhang1; Nicole Overman1; Jens Darsell1; Vineet Joshi1; 1Pacific Northwest National Laboratory
Shear assisted processing and extrusion (ShAPE) is an emerging high-strain, solid-phase processing (SPP) technique; it can be used to manufacture metallic parts without melting the constituent materials. ShAPE can refine microstructures and develop preferred textures in a single step precluding the need for energy-intensive processing steps typically employed in conventional alloy development and part manufacturing. Parts manufactured using ShAPE process demonstrate unique property combinations that are challenging to achieve using traditional manufacturing routes. In this work, solid ZK60 magnesium rods were created using varying extrusion rates, RPM and cooling. The effect of these processing parameters on the crystallographic texture and grain refinement development was studied using electron backscatter diffraction. The microstructural statistics gathered in this study assist in developing models for microstructural predictions in Mg alloys processed through ShAPE.
Designing Ultra-conductors Using Solid Phase Processing: Keerti Kappagantula1; Aditya Nittala1; William Frazier1; Kashi Subedi2; Xiao Li1; Woongjo Choi1; Bharat Gwalani1; Joshua Silverstein1; Julian Atehortua1; Hrishikesh Das1; Frank Kraft2; David Drabold2; Glenn Grant1; 1Pacific Northwest National Laboratory; 2Ohio University
Ultra-conductors are metal composites with nanoadditives that demonstrate enhanced current density, electrical conductivity and lowered temperature coefficient of resistance over commercial conductors (aluminum, copper alloys) used in applications such as electric grid, motors, and automobiles. Despite two decades of research, manufacturing ultra-conductors has been a challenge. While conventional physics informs that adding a second phase to a pure metal decreases electrical performance, recent research demonstrates that the use of advanced manufacturing processes such as shear processing can enable ultra-conductive behavior in metal composites with graphene additives. This talk will highlight the microstructural features and process pathways identified in ultra-conductors manufactured via melt-based, hot extrusion and shear assisted processing and extrusion technologies that lead to improved conductor behavior. A combination, experimental process conditions, composite microstructural characterization studies, finite element and molecular dynamic simulation results will be presented to introduce the ultra-conductor design process using advanced manufacturing techniques in this presentation.