6th World Congress on Integrated Computational Materials Engineering (ICME 2022): Poster Session
Program Organizers: William Joost; Kester Clarke, Los Alamos National Laboratory; Danielle Cote, Worcester Polytechnic Institute; Javier Llorca, IMDEA Materials Institute & Technical University of Madrid; Heather Murdoch, U.S. Army Research Laboratory; Satyam Sahay, John Deere; Michael Sangid, Purdue University
Monday 3:00 PM
April 25, 2022
Room: Regency Ballroom CF
Location: Hyatt Regency Lake Tahoe
Computational Fluid Dynamic Analysis to Evaluate Coating Film Thickness During Dip Coating Process: Rahman Ansari1; Jayendiran Raja; Venkateswaran Perumal1; Dermot Dunne1; Cyril Tuohy1; 1Stryker Global Technology Center
Coating application is identified as one of the complex engineering problems in the manufacturing of devices in automotive, aerospace & medical. Inadequate coating on the substrate can impact the robustness of the coating and may cause adverse effects on device performance. The objective of the present study is to evaluate the influence of key coating processing parameters to better understand the relationship between coating thickness and the parameters which helps to determine the durability and effectiveness of coating on the device. In this study, numerical modeling of dip-coating process on the device is carried out using Ansys-Fluent. The dynamic contact angle has been imposed on the fluid-solid interface through a UDF to accurately capture the fluid behavior and film thickness formation. The influence of coating film thickness due to various parameters like extraction speed, viscosity, surface tension of coating material, surface roughness and dynamic contact angle are evaluated.
Computational Study of Li-Ion Conduction Mechanisms in Solid Electrolytes: Santosh Kc1; Ipshita Shahoo1; Dirar Mashaleh1; Gustavo Isarraras1; 1San Jose State University
Solid electrolytes have been attracting a lot of attention because of its superior properties over liquid electrolytes like wider electrochemical stability and better safety. However, many of them suffer from relatively low ionic conductivity. Identifying a suitable solid electrolyte is challenging and necessary. Using first-principles calculations based on density functional theory, we investigate ion conduction mechanisms in several types of solid electrolytes such as orthorhombic, perovskite, garnet and anti-perovskite-type structures. The detailed Li ion defect formation and migration mechanisms are investigated which provide the quantitative information of ionic conductivity of electrolytes. Thus, such investigation will be helpful to understand the atomic level mechanisms of Li ion conduction mechanism and to optimize ionic conductivity of electrolytes in Li-ion battery.
In-situ Testing to Acquire HR EBSD and DIC Strain Data within a Coincident Domain: Will Gilliland1; Sam Poulton1; Timothy Ruggles2; Geoffrey Bomarito3; Andrew Cannon4; Jacob Hochhalter1; 1University of Utah; 2Sandia National Laboratories; 3National Aeronautics and Space Administration; 41900 Engineering LLC; Clemson University
For this project, an inked rubber stamp was applied to a small Inconel 718 specimen. The stamp transferred a thin pattern with microscale features for digital image correlation (DIC). The pattern is easily visible at lower voltages and also thin enough to not obstruct backscattered electrons. The unique characteristics of the pattern enabled the concurrent acquisition of DIC and high-resolution electron backscatter diffraction (HR EBSD) data while the specimen was loaded in-situ. The challenges of in-situ testing and combining EBSD with DIC measurements are discussed. By combining the elastic strains (from HR EBSD) and total strains (from DIC) the result of this approach is an estimate of stress-strain behavior at points across the specimen surface. This combined dataset can then be used as higher-fidelity data in the calibration of crystal plasticity models. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
Machine Learning Based Hierarchical Multiscale Modeling of Mechanical Deformation for Metal-Matrix-Nanocomposites: Md Shahrier Hasan1; Wenwu Xu1; Gregory Berkeley1; 1San Diego State University
The mechanical properties of Metal-Matrix-Nanocomposites (MMNCs) have demonstrated significant enhancement with the presence of a small fraction of nano-inclusions. To understand this nano-inclusion-induced enhancement, multi-scale modeling is necessary, one that can pass the atomistic mechanism-dependent information to the continuum calculations. Here, a novel Machine Learning (ML) enabled hierarchical multiscale modeling was developed by coupling the atomistic Molecular Dynamics (MD) simulations with the macroscale Finite Element Method (FEM) to understand and make predictions on how the nano-inclusions affect the macroscopic properties of MMNCs. At first, MD simulations for various loading conditions and nano-inclusion structures were conducted to generate a sufficient amount of deformation and mechanical property-related data to train various ML classification and regression models which were in turn utilized in the macroscale FEM model as constitutive laws. Finally, the multiscale modeling results were qualitatively verified against the relevant modeling and experimental data for MMNCs.
Numerical Simulation and Analysis of Solid Phase Processing: A Validated Friction Extrusion Smoothed Particle Hydrodynamics Model: Lei Li1; Xiao Li1; Anthony Reynolds2; Glenn Grant1; Ayoub Soulami1; 1Battle Pacific Northwest National Laboratory; 2University of South Carolina
Shear Assisted Processing and Extrusion (ShAPE) is an emerging solid-phase processing and advanced manufacturing technique. It turns the feedstock metal materials into high-performance parts without melting them, thus saving the processing time and energy. To better understand the processing parameters on the performance of processed parts, a computational model is required. In this study, we develop a smoothed particle hydrodynamics (SPH) model to achieve this. Due to its meshfree and Lagrangian nature, SPH can handle extremely large deformation without heavy re-meshing, and track the history variables explicitly. Model predictions evaluate the effects of various parameters on the performance of the extruded parts. It is shown that the predicted results are in good agreement with the experimental data, demonstrating a promising structure-properties linkage achieved by the model. The proposed SPH model can lead to an accelerated development effort and the process-microstructure analysis as a subsequent step.
ZEISS ZEN Intellesis - a Powerful and Open Machine Learning Ecosystem for Materials Microscopy: Tobias Volkenandt1; Robin White2; William Harris; 1Carl Zeiss Microscopy GmbH; 2Carl Zeiss Microscopy LLC
In materials engineering at some point there is no way around analyzing the microstructure of real samples using microscopy. And machine learning (ML) has proven to be a valuable tool to derive tangible information from microscopic images for comparison with simulations and modelling. However, applying ML techniques in image segmentation or analysis is not straight-forward and typically comes with a steep learning curve. To overcome this hurdle, a powerful, open and easy-to-use ML ecosystem has been developed and integrated in the image acquisition and analysis suite ZEISS ZEN core. In this contribution we will discuss different features of the respective software module ZEN Intellesis and explain in detail how it can be used to implement ML. Starting from readily available pixel classification based on Random Forest segmentation, over import and execution of deep neural networks trained elsewhere, to an open cloud platform for effortless creation of deep learning models.
Process Modeling of Cure in Epoxy Based Polymer Matrix Composites Using ICME: Prathamesh Deshpande1; Sagar Shah2; Sagar Patil1; Michael Olaya2; Gregory Odegard1; Marianna Maiaru2; 1Michigan Technological University; 2University of Massachusetts Lowell
It is well-known that residual stresses are caused by resin shrinkage and differential thermal contraction between the matrix and reinforcement. Not only are these residual stresses difficult to experimentally measure, they are difficult to predict computationally. Predictive models of the laminate curing process require detailed knowledge of the resin thermo-mechanical properties as a function of conversion, which cannot be determined through traditional continuum-scale modeling methods. Therefore, an Integrated Computational Material Engineering (ICME) approach is used to predict the effect of the cure cycle on the design and manufacturing of a carbon/epoxy composite (DGEBA-polyoxypropylene diamine resin). Molecular Dynamics using the Interface Forcefield (IFF-R) is employed to predict the resin properties as a function of the crosslink density. The predicted properties are subsequently used in micro-scale models that account for random fiber distribution using the Finite Element Method (FEM). Results indicate successful implementation of the two-scale method for process modeling of thermosets.
Understanding the Keyhole Dynamics in Laser Welding with Computer Vision and Data Analytics Applied to Time-Resolved X-Ray Imaging: Jongchan Pyeon; Joseph Aroh1; Runbo Jiang1; Benjamin Gould2; Andy Ramlatchan3; Anthony Rollett1; 1Carnegie Mellon University; 2Argonne National Laboratory; 3NASA Langley Research Center
During laser welding of metals, localized metal evaporation resulting in the formation of a keyhole shaped cavity can occur if high enough energy densities are used. An unstable keyhole can have deleterious effects in certain applications (e.g. laser powder bed fusion) as it increases the likelihood of producing defects such as porosity. In this work, the dynamics of keyhole fluctuations were probed using in-situ synchrotron x-ray imaging at the Advanced Photon Source across a range of materials and laser parameters. The high temporal and spatial resolution of these experiments result in large datasets which were processed using computer vision techniques in order to extract time-resolved quantitative geometric features. These features were analyzed to understand the relationship between the variation of local keyhole geometry and the probability of processing defects to occur. Likewise, multivariate statistical tools were employed to understand the link between processing parameters, material properties, and keyhole geometry.
Structure-Property (Small Punch Creep Rupture) Relationships in Low Carbon Steels: Johan Westraadt1; 1Nelson Mandela University
Small-punch creep (SPC) testing is currently used to evaluate the creep-rupture properties of steels used in the petrochemical and power generating industries. In this project, various statistical and machine learning techniques are explored to determine the relationships between material compositions, characterised microstructures and experimentally measured creep-rupture properties for service exposed low carbon steels. A dataset consisting of 120 sample microstructures and its associated small-punch creep-rupture properties were collected from the service-exposed low carbon steels. Optical micrographs of the etched surfaces were processed using image analysis to determine the phase fractions and sizes of ferrite and pearlite. In addition, 2-point correlations combined with principle component analysis were performed on the optical micrographs and a simple microstructure-property model was constructed. A selection of 10 samples deviating from this simplified model were then further investigated using secondary electron imaging, electron-backscattered diffraction and nano-identation hardness in an effort to explain the observed deviations.
Deformation Mechanisms of Additively Manufactured FeMnCoCrAl High Entropy Alloy with Interstitial Carbon - ICME Approach: Tomi Suhonen1; Matti Lindroos1; Anssi Laukkanen1; Jarkko Metsajoki1; Ivanchenko Mykola1; Juha Lagerbom1; 1VTT Technical Research Centre of Finland
FeMnCoCrAl with 0.7at%C powders were gas atomized, consolidated by laser powder bed fusion and heat treated by hot isostatic pressing technique. Microstructures of different manufacturing steps were characterized (SEM-EDS-EBSD, TEM, XRD) and stress-strain behavior as well as deformation mechanisms (e.g.TWIP and TRIP) of consolidated specimens were studied with in-situ-SEM tensile tests. Engineering and true stress-strain curves as well as EBSD phase maps showing the deformation induced martensitic transformation as a function of strain are presented. Experiments were performed in order to get parameters and to validate whether the crystal plasticity approach is able to capture the deformation mechanisms.
The Influence of Pore Defects on the Mechanical Behavior of Product Parts Using Micromechanics: Nannan Song; shenghua wu; Flavio Souza; Jeyachandran Rajesh1; Kedar Malusare2; 1Simens Digitial Industries Software; 2Siemens PLM Software
Pore defects appear in many processes. It is essential to study and understand the influence of pores on the mechanical properties of product parts. The present research uses a micromechanical method to investigate the pore’s volume, shape, and distribution pattern on Young’s modulus, yield strength, and stress distribution of the produced parts. This research focuses on the pores generated in the bulk material. 1%-10% volume fractions of pores are researched. The influence of pore distribution patterns on mechanical behavior is investigated. Several representative volume elements (RVE) are used to compare the influence of shapes and distributions of pores on the mechanical behavior of produced parts. Spherical, rectangle, and irregular pores are investigated. The predicted results were compared with the experimental ones. It shows that this method has a very broad way of predicting the mechanical property of produced product during several different processes.
Materials Data Management to Meet Additive Manufacturing Requirements: Philippe Hebert1; 1Hexagon Manufacturing Intelligence
Industrialization of additive manufacturing leads to the generation of a significantly large amount of data, combining materials, process, build and part information. On one hand side, this poses the challenge on collecting, merging and trace the data along its lifecycle. On the other hand, once data is captured properly, it promises to enable sophisticated analysis and smart material and process choices, as the amount of collected data continues to grow in the organization. This presentation aims at describing a materials database system framework to support the additive manufacturing workflow, as well as sharing recent industry best practices.
Microstructure Classification and Quantification Method for Regular SEM Images of Complex Steel Microstructures Combining EBSD Labeling and Deep Learning: Chunguang Shen1; Chenchong Wang1; Wei Xu; 1Northeastern University
Present work develops an EBSD-trained deep learning (DL) method to integrate the advantages of traditional material characterization information and artificial intelligence strategy for classification and quantification of complex microstructures only using regular SEM images. In this method, EBSD analysis is applied to produce accurate ground truth for guiding DL model training and U-Net architecture is used to establish the correlation between SEM input image and EBSD ground truth using small sample experimental datasets. The proposed method is successfully applied to two engineering steels with complex microstructures, i.e., dual-phase steel and quenching and partitioning steel, to segment different phases and quantify phase content and grain size. Also, this method contributes to accelerate EBSD analysis because EBSD maps can be rapidly produced via present models inputting regular SEM images. The good generality of trained models is well validated using new DP and Q&P steels not belonging to training and testing set.
Fabrication of Precise Functionally Graded Materials (FGMs) via Directed Energy Deposition (DED): Romilene Cruz1; 1FormAlloy
Utilizing FormAlloy’s own award-winning Alloy Development Feeder (ADF), a patent-pending powder feeder with 16 small hoppers, and computational approaches based on the CALPHAD (CALculation of PHAse Diagrams) method, FormAlloy Technologies, Inc can make functionally graded material (FGM) material systems with high precision transition zones for both surface coatings and components with complex geometries and features, with the potential to scale the process up. The additive manufacturing (AM) process that shall utilize the ADF and CALPHAD approaches will be the versatile Directed Energy Deposition (DED) process.
Numerical Study on a Heat Transfer Model in the Solid-Carbon/Liquid-Copper-Silicon System: Khurram Iqbal1; Attra Ali1; 1Institute of Business Management (IoBM)
The thermal conductivity of solid carbon and liquid copper-silicon by using Fourier law, can be utilized as a viable technique to grow superior warm administration materials. The investigation reveals that the thermal conductivity of C/Cu composites changes because of the SiC relocation from the matrix to the interface. C/Cu composites were used to predict the value of heat flux at different thicknesses of SiC materials. Several studies on heat transfer coefficients in the literature which is mostly experimental and little fundamental theoretical work has been done. The possible reasons for this gap might arise from the difficulties of studying the high-temperature and vacuum conditions. A model for the numerical simulation of heat transfer in the solid-carbon/ liquid copper-silicon system is presented.
Prediction of Rubber Failures Properties: Effects of Aging: Reda Kadri1; Moussa Nait Abdelaziz1; Bruno Fayolle2; Mouna Ben Hassine3; Yannick Nziakou Djouguela3; Julien Sanahuja3; 1University of Lille; 2Arts et Métiers ParisTech ; 3Electiricité de France
EPDM (Ethylene Propylene Diene Monomer) is a rubber often used as an insulator in the electrical wiring of nuclear power plants. During its operational life, the material is exposed to various aging mechanisms including radio-oxidation and thermo-oxidation. Consequently, the outer surface of insulator undergoes changes in properties over time as a result of combined mechanical and chemical processes, leading to the appearance of cracks and thus to the failure of the electric cable.The insulation material end-of-life criterion is based on a standard which prescribes a minimum value of the elongation at break not less than 50% (absolute value). Our work consists in studying the influence of radiation and thermal aging on the properties of rubber. The goal is to propose a tool allowing the prediction of mechanical properties at break during aging.
Investigation of Time and Temperature Dependency of Cavitation Resistance of Al and Mg with Nonequilibrium Vacancy Concentrations: Sara Adibi1; Justin Wilkerson2; 1Mississippi State University, Center for Advanced Vehicular Systems; 2Texas A&M University
Here, we provide a systematic molecular dynamics (MD) study of the pulse duration and temperature dependence of spall strength in Al and Mg with a nonequilibrium concentration of vacancies. A superconcentration of single vacancies are randomly introduced into an otherwise perfect lattice, and the systems are allowed to evolve for up to 500 nanoseconds as a proxy for the pulse duration of a shock compression wave. Since these systems are nonequilibrium in nature, a time-dependent and temperature-dependent evolution of the microstructure ensues, which leads to a time- and temperature-dependent softening of the spall strength. Here, we report a large parametric study consisting of 196 MD calculations to quantify this softening in Al and Mg. We invoke the notion of time–temperature superposition from the field of viscoelasticity, and show that the results of our 196 MD calculations remarkably collapse onto a single master curve when normalized by an appropriate relaxation timescale. Lastly, a favorable agreement between the MD calculations and experimental measurements of thermal softening of spall strength is reported.
Fatigue Strength Prediction and High-Throughput Design by Mechanics Theory Guided Transfer Learning for Extremely Small Sample Database of Steels: Xiaolu Wei1; Chenchong Wang; Chunguang Shen1; Wei Xu1; 1Northeastern University
Fatigue strength (FS) is one of most important properties. Traditional trial-and-error method for new materials is costly and time-consuming due to complicated fatigue tests. Machine learning (ML) has been widely employed in material science but adequate data is required. In order to reduce costs of data accumulation for FS, based on transfer learning and correlation between properties of steels, a transfer framework (TR) for FS prediction was proposed. The TR aims to predict FS based on small fatigue data and low-cost big data of tensile properties. In the TR, ML models were first trained to estimate tensile properties. Then, TR models were trained to estimate FS. The resulting TR performs great predictive capability. Further, genetic algorithm was applied to search new low-alloy steels with high FS. The results exhibit high reliability and effectiveness. This research provides inspiring guidance for prediction of properties difficult to accumulate data due to high cost.
Hybrid Quantum–Classical Simulations of Metal Corrosion in Aqueous Environments: Stephen Weitzner1; Lisa Eggart2; Tuan Anh Pham1; Brandon Wood1; 1Lawrence Livermore National Laboratory; 2Michigan Technological University
Predictive molecular simulations of metal corrosion in aqueous environments are challenging due to the disparate time and length scales associated with corrosion phenomena. Because corrosion inherently involves materials degradation processes, high-fidelity quantum mechanical models are needed to accurately describe the breaking and formation of chemical bonds and the electrochemical charge transfer reactions that occur across the metal-solution interface. Nevertheless, full quantum mechanical descriptions of the electrochemical interface are challenging due to their high computational cost. In this talk, we will describe a recently developed hybrid quantum-classical approach that can be leveraged to bridge time and length scales in atomistic corrosion modeling while retaining the essential accuracy of quantum-mechanical methods. We demonstrate how this approach can be employed to study the oxidative dissolution of metal surfaces in realistic electrochemical environments.Work was performed under the auspices of the U.S. DOE by LLNL under Contract DE-AC52-07NA27344.
Importance of Silica Hydroxylation in Ampyra Adsorption: DFT Study: A Diaz Compañy1; G Roman1; E Noseda Grau1; S Simonetti1; 1IFISUR-UNS, UTN
The technological applications of silica are rely on its specific surface properties. To design adsorbent, it is crucial to understand the adsorption mechanisms. DFT calculations are performed to understand the mechanisms that control the adsorption of Ampyra drug on the different crystallographic planes of β-cristobalite: the hydroxylated (111) and (100) surfaces. The Ampyra-silica interaction is most favored on the (100) surface where the entire ring of the molecule interacts with the surface while on the (111) face, lesser exchange and fewer non-polar atoms are involved. Calculations show that the interactions mainly occur at the interface between the Ampyra and the closest silanol groups, according to the formation of the H-bonding interactions. The results indicate that the H-bonds have an important influence on the adsorption of the Ampyra. In consequence, adsorption on the (111) surface is observed to a lesser extent than on the (100) surface according the smaller hydroxyl density.
Integrated Simulation of the Heat Treatment and Calculation of the Load Bearing Capacity of Sintered Gears: Ali Rajaei1; Bengt Hallstedst1; Christoph Broeckmann1; 1IWM of RWTH Aachen University
The application of high strength sintered gears in the power transmission requires optimized surface densification and heat treatment processes to improve the load bearing capacity and service life of the part. A macroscopic modelling approach is developed to integrate the heat treatment simulation and the prediction of the load bearing capacity of case-hardened, sintered gears. The model describes quantitatively the microstructure changes and the evolution of internal stresses, depending on the process parameters. The analysis of the load bearing capacity is based on the calculated local degree of utilization, considering the full stress tensor as a result of the gear testing loads and residual stresses. The fatigue strength of the sintered steel is adopted from an available data set, depending on the density, carbon content and the highly loaded volume of the part. The model represents a digital tool to investigate the correlation between process parameters, material state and properties.
Residual Heat Effect on the Melt Pool Geometry During the Laser Powder Bed Fusion Process: Subin Shrestha1; Kevin Chou1; 1University of Louisville
In this study, raster scanning is studied to investigate the effect of the scan length and the hatch spacing on the melt pool size at different locations along the laser travel direction. The multi-track specimens with different scan lengths are fabricated using 195 W laser power, three scan speeds (375 mm/s, 750 mm/s, and 1500 mm/s), and two hatch spacing levels (80 µm and 120 µm). The melt pool boundary obtained from both experiments and particle-scale simulations reveals that the effect of residual heat is most manifested at a laser turn region. Besides, the depth of the melt pool increases with increasing track numbers, while the track height decreases. In addition, the second-layer scanning simulation shows that the inherent surface morphology from the first layer leads to the noticeable variability in the actual powder layer thickness of the second layer which in turn impacts the melt pool in second-layer scanning.
A Thermo-Mechanical Model for Prediction of Residual State during Wire Arc Additive Manufacturing (WAAM): Sami Hilal1; Djamel MISSOUM-BENZIANE1; Pierre KERFRIDEN1; Sofiane HENDILI2; Matthieu MAZIERE1; 1Centre des Matériaux-Mines Paristech; 2EDF R&D Division
Additive manufacturing processes by wire deposition such as the WAAM process allows manufacturing large components. If this process derived from welding is well known, its use at the industrial level requires to better understand the influence of the welding parameters and the deposition strategies on the microstructure, residual stresses and strains distributions. However, these quantities are very difficult to access experimentally, but can be provided by numerical simulations. This work consists in setting up, calibrating and validating a finite element model using Code_Aster to simulate the WAAM process on industrial parts. Macroscopic thermo-mechanical simulations are carried out for multiple geometries and deposition strategies. In order to calibrate and validate the set up model, instrumented experimental tests are conducted (thermocouples, thermal imaging, 3D scans). The finite element results are in good agreement with the experimental data. This work is part of the AM platform “Additive Factory Hub” involving manufacturers and academics.
Design of Stable Nanocrystalline Aluminum by Evolutionary Computation: Jake Hohl1; 1University of Nevada, Reno
Nanocrystalline aluminum offers a unique combination of mechanical properties such as high tensile strength, extended fatigue life, and wear resistance. Because of their enhanced properties, these materials have potential for applications in critical lightweight structural components. However, the small grain size of these metals leads to thermodynamic instability against grain coarsening and phase precipitation. To this end, this work focuses on identifying metallic dopants, which segregate to the grain boundaries in aluminum, that increase its stability against grain coarsening and phase precipitation. Since the design space, comprised of nanocrystalline aluminum-dopant combinations at unique compositions, is sufficiently large, we propose a multi-objective optimization of material properties and thermodynamic stability based on Non-dominated Sorting Genetic Algorithms to synthesize and evaluate Pareto optimal nanocrystalline aluminum-dopant designs.
Calculation of Initial Stage of Solidified Shell Deformation During γ to δ Transformation in Continuous Casting Mold of Steel: Kohei Furumai1; Andre Phillion2; Hatem Zurob2; 1JFE Steel; 2McMaster University
Solidification shell deformations of hypo-peritectic steel within the mold during continuous casting have been calculated in order to clarify the influence of mold flux infiltration variability on the cooling rate, the width of the low heat flux region, the height of air gap, the unevenness of solidified shell, and the resulting strain in the solidified shell. A sequentially coupled thermal-mechanical finite element model has been developed and the simulation includes heat transfer and shell deformation of solidified shell during the delta-to-gamma transformation. Further, it takes into account the effects of variability in mold flux infiltration and air gap formation on heat transfer into the mold, as well as the effect of cooling rate on the thermal expansion. The results showed that mild cooling and low variability in mold flux infiltration strongly decrease the height of the air gap, the unevenness and the strain in the solidified shell.
ICME Framework to Predict the Precipitation Kinetics and Microstructural Evolution of Hot-Rolled TRIP Steel: Pranjal Chauhan1; Vaibhav Malik1; Saurabh Kumar1; Surya Ardham2; Himanshu Nirgudkar2; Akash Bhattacharjee2; Dinesh Nath1; Gerald Tennyson2; Amarendra Singh1; 1Department of Metallurgical and Materials Engineering, Indian Institute of Technology Kanpur; 2TCS Research, Tata Consultancy Services Limited, Pune
TRIP steels obtain their excellent formability due to the transformation of retained austenite to martensite during plastic deformation. The microalloying addition through Nb enhances ductility by delaying the onset of carbide precipitation to Al/ Si base TRIP steels. In this work, an ICME framework is used to integrate caster, reheating and hot-rolling operations to capture the dynamics of microstructure and mechanical properties evolution. The focus is on the deformation and precipitation kinetics during hot rolling of Nb micro-alloyed TRIP steel. The models include a transient heat transfer and a thermomechanical model to capture temperature evolution across the slab during casting, reheating and hot rolling operation. A multi-component equilibrium precipitate model is utilized to compute the precipitation kinetics of NbC and their influence on the evolving microstructure. The models are developed using OpenFOAM and Code-Aster and the effect of various process parameters on the precipitation kinetics and evolving microstructure are presented.