ICME 2023: Linkages: Deformation II
Program Organizers: Charles Ward, AFRL/RXM; Heather Murdoch, U.S. Army Research Laboratory
Wednesday 1:10 PM
May 24, 2023
Room: Caribbean VI & VII
Location: Caribe Royale
1:10 PM Invited
Simulating Phenomena of Industrial Rolling via Gleeble Compression for Calibration of an Aluminum Processing Model: Jeffrey Tschirhart1; Chal Park1; Aaditya Lakshmanan1; Sazol Das1; 1Novelis
Industrial rolling of aluminum is a multi-step process involving numerous reductions under hot and cold temperatures with varying deformation speeds to achieve the final gauge and microstructure. This study looks at the effect of changing the temperature and strain rates for AA3104 aluminum through plane strain compression via the Gleeble Hydrawedge II. It makes use of FEA simulations to validate the strain distribution after deformation and evaluates how the resulting flow stress, developed texture, and microstructure compare to the expected industrial version. The flow stress and texture data acquired are used for calibration of an ICME model describing the microstructure and properties of the entire rolling process for AA3104 aluminum.
1:40 PM
The Through-process Texture Analysis of Non-grain-oriented Electrical Steel: Masoud Sistaninia1; Peter Raninger1; Petri Prevedel1; Paul Angerer1; Herbert Kreuzer2; Thomas Antretter3; 1Materials Center Leoben Forschung Gmbh; 2voestalpine Stahl GmbH; 3Montanuniversitaet Leoben
Non-grain-oriented (NO) electrical steel is widely used in electrical machines with rotating magnetic fields. The process chain consists of hot-rolling with optional batch-annealing followed by cold-rolling and final continuous annealing. Each step has a significant influence on microstructure and electromagnetic properties. A tailor-made microstructure, with specific grain-size and texture, can greatly improve magnetic properties of NO sheet material and thus the performance of rotor/stator components. For the design of such an ideal microstructure, it is necessary to quantify the influence of process-parameters on the microstructure. In the current work, texture evolutions during cold-rolling and due to recrystallization during final annealing will be evaluated based on EBSD-measurements for different conditions. Modelling approaches describing the formation of deformation and annealing texture are presented and discussed in combination with the experimental results. The through-process texture analysis provides new insights into the optimum microstructure of NO steels, leading to an improvement of electromagnetic properties.
2:00 PM
Smoothed Particle Hydrodynamics Model for Friction Stir Processing of 316 L Stainless Steel: Process Modeling and Microstructure Prediction: Lei Li1; Ayoub Soulami1; Donald Todd1; Neil Henson1; Erin Barker1; Eric Smith1; 1Pacific Northwest National Laboratory
Friction stir processing (FSP) is a solid-phase processing technique that provides localized modification and control of microstructures in the processed zones. Numerical models can help predict material deformation and temperature history during FSP that directly relate to microstructural refinement, densification, and homogeneity of the processed zone. This work presents a meshfree smoothed particle hydrodynamics (SPH) model for FSP of 316 L stainless steel using a thermo-elasto-plastic constitutive model and stick-slip tool-workpiece contact approach. The model’s predicted material flow, temperature distribution, and stress-strain state are presented and validated with experimental data. The strain rate and temperature histories obtained from the SPH model are used for predicting Zener-Hollomon parameter and average material grain size. Numerical results on the microscale are also found to agree with experimental observations in the stir zone. This high-fidelity model is feeding results as an input to lower-length scale models and informing the process conditions.
2:20 PM
Microstructural Evolution During Closed Die Forging of UDIMET720 and Prediction of Mechanical Properties: Christian Gruber1; Flora Godor1; Aleksandar Stanojevic1; Jürgen Krobath2; Peter Raninger2; Martin Stockinger3; 1voestalpine BÖHLER Aerospace GmbH & Co KG; 2Materials Center Leoben Forschung GmbH; 3Montanuniversität Leoben - Department for Product Engineering
The use of UDIMET720 (U720) in next generation aero engines requires numerical tools to predict the microstructure evolution and final mechanical properties of closed die forged engine disks. An existing simulation tool for a different superalloy, which uses integrated computational materials engineering (ICME) to predict characteristic properties such as grain size and yield strength, is used and parametrized on the microstructural mechanisms of U720. The specific setting of a homogeneous grain size distribution through metal-physical understanding of the recrystallization processes and the formation of the necessary γ'-precipitate-populations is elaborated. Inhomogeneous microstructure distributions in the pre-material and their effect on the local microstructure and property evolution along the process route are taken into account. Based on specific material characterization and testing the scientifically sound and application-oriented design tool is optimized and includes all essential mechanisms of an U720 turbine disk manufacturing.
2:40 PM
ICME and ML Framework to Predict the Microstructure During U-10Mo Fuel Fabrication: Ayoub Soulami1; Yucheng Fu1; William Frazier1; Kyoo Sil Choi1; Lei Li1; Zhijie Xu1; Curt Lavender1; Vineet Joshi1; 1Pacific Northwest National Laboratory
To reduce nuclear proliferation, low-enriched U-10Mo alloy has been identified as a promising fuel candidate for United States high-performance research reactors. During fabrication. Manufacturing the U-10Mo alloy involves a complex series of thermomechanical processing steps, including homogenization, hot rolling, annealing, cold rolling, and hot isostatic pressing. Several models/modeling methods have been developed for these individual processes. The interaction and coupling between individual processes use the concept of ICME, which aims to bridge the information passing between interacting models and investigates the impact of manufacturing processes on material microstructure evolution. Additionally, a Machine Learning (ML) model was developed and trained on both physics-based modeling and characterization data to predict the resultant microstructure after hot rolling passes and annealing. It is shown that implementing ICME leads to improved predictions, a better understanding of microstructure across multiple processes, and accelerated and more cost-effective development efforts.
3:00 PM Break