8th World Congress on Integrated Computational Materials Engineering (ICME 2025): ICME Application to Advanced Manufacturing III
Program Organizers: Victoria Miller, University of Florida; Stephen DeWitt, Oak Ridge National Laboratory
Wednesday 9:00 AM
June 18, 2025
Room: Platinum Ballroom 3
Location: Anaheim Marriott
Session Chair: William Joost
9:00 AM
Tailoring Microstructure in Functionally Graded NiTi Alloys Using In-Situ Alloying Directed Energy Deposition: Chinnapat Panwisawas1; 1School of Engineering and Materials Science, Queen Mary University of London
Inhomogeneous non-equilibrium microstructures of NiTi alloys can be induced by additive manufacturing (AM), resulting in compromised performance and impeded functional applications. NiTi alloys with varying gradient compositions have been in-situ alloyed using directed energy deposition (DED): mixed powder NiTi, graded Ti/Ni, and graded Ti/NiTi, to achieve tailored microstructure gradients. The printability of these graded materials was evaluated to control intermetallic phase distribution. Systematic investigations of microstructural characterisation, phase transformation, and mechanical properties have been performed to obtain tailored microstructure. Computational modelling unveils the mechanisms governing dynamic temperature fluctuations, as well as thermal and mass transport within melt pools. Effective control over the formation and distribution of intermetallic compounds has been achieved by a broadening of the martensitic transformation interval through microstructure gradient design. This study provides valuable insights into the fabrication of AM graded NiTi alloys, thus enabling intricate structural designs tailored to meet specific functional requirements.
9:30 AM
Enhancing the Printability of Laser Powder Bed Fusion-Processed Aluminum 7xxx Series Alloys Using Grain Refinement and Eutectic Solidification Strategies: Chukwudalu Uba1; 1University of Louisiana Lafayette
Additive manufacturing, particularly LPBF, is vital to Industry 4.0, enabling high-precision part production across aerospace, biomedical, and manufacturing sectors. This layer-by-layer technology improves material properties over conventional methods, especially in high-performance alloys like titanium and steel. Al 7xxx alloys offer low density and high-specific strength yet face LPBF challenges such as hot cracking and porosity due to rapid solidification and thermal gradients. This study presents a computational and experimental framework to enhance Al 7xxx LPBF processibility via compositional modification. Using CALPHAD, printable Al 7xxx compositions were designed with V, Ti, and Mg additions to enable grain refinement and eutectic solidification. Subsequent LPBF/SLM experiments and characterization tests, such as metallography (SEM), SEM, EDS, XRD, and micro-CT scan, confirmed the production of refined microstructures with reduced porosity. This work supports the Materials Genome Initiative’s goal to accelerate materials development, promoting crack-free, high-quality Al 7xxx components through an integrated computational materials engineering approach.
9:50 AM
Physics-Based Modeling for Fatigue Crack Initiation Predictions in Additively Manufactured AlSi10Mg Alloys: Deepali Patil1; Anthony Spangenberger1; Diana Lados1; 1Worcester Polytechnic Institute
Additive manufacturing (AM) is rapidly becoming an essential manufacturing process across various industries due to its design flexibility and potential for substantial weight, cost, and energy savings. However, the use of AM for safety-critical components is limited by process-induced defects, which reduce fatigue lifetimes and increase variability. In this research, Stochastic Volume Elements (SVEs) of AM AlSi10Mg are developed for a given set of process parameters using a physics-based approach and fatigue loading is simulated via crystal plasticity simulations with FFT solvers. The stress-strain (S-S) response from these simulations is post-processed to obtain a Fatigue Indicator Parameter (FIP), aiding in predicting the Fatigue Crack Initiation (FCI) life of these alloys. This model is calibrated and validated with experimental fatigue data, allowing it to predict fatigue crack initiation life and S-N curves across the AM process parameter space. The feasibility of this approach and future modeling initiatives will be discussed.
10:10 AM
Numerical Simulations of the Wire-Arc Additive Manufacturing (WAAM) Process: Fernando Valiente Dies1; 1ANSTO & The University of Sydney
Wire Arc Additive Manufacturing (WAAM) is a direct energy deposition additive manufacturing process that uses well-established welding technology. It consists of a sequential deposition of weld passes and layers to form engineering components. The WAAM process is characterised by high heat input, high deposition rate, high surface roughness and the anisotropy of material properties.In this project, the WAAM process has been employed to manufacture multipass, multilayer walls made using 316L Stainless Steel. An array of thermocouples on the base plate has been used to monitor the transient temperature field during the WAAM manufacturing of test specimens. The thermocouple readings are used to calibrate the thermal model, which we then used in a phase-field model capturing the solidification process and formation of weld-like microstructure. Furthermore, the same thermal model was used in the mechanical model capturing material response to the heat source, thus predicting the macroscopic distortion and residual stresses.
10:30 AM Break
10:50 AM
ICME Modeling for Process-Structure-Property Toolchain Development for Laser Powder Bed Fusion: Ranadip Acharya1; Nitin Chandola1; Subham Mridha1; Boliang Zhang1; Vijay Jagdale1; 1Collins Aerospace
This abstract presents an Integrated Computational Materials Engineering (ICME) approach for developing a comprehensive toolchain aimed at optimizing the laser powder bed fusion (LPBF) process. The toolchain integrates models that link processing parameters to resultant microstructure and material properties, enabling predictive capabilities for LPBF applications. By systematically analyzing the thermal, mechanical, and metallurgical behaviors during the LPBF process, this framework allows for the identification of optimal processing conditions tailored to specific material systems through fast-acting analytical models and a high-fidelity computational toolchain. The study employs computational fluid dynamics, cellular automata, phase field modeling and crystal-plasticity based finite element analysis to simulate the dynamic interactions between process variables and microstructural evolution resulting in property variation. Ultimately, this ICME framework provides a robust foundation for the future development of 'first-time-right' technologies in additive manufacturing.
11:10 AM
Effect of Random Porosities and Surface Roughness on Fatigue Life of Additively Manufactured Maraging Steel: Aditya Pandey1; Vidit Gaur1; 1Indian Institute of Technology Roorkee
A combined approach of machine learning, computational fluid dynamics and analytical model was established to explore the interaction between porosity distribution and surface roughness on the fatigue life of additively manufactured components. A new fatigue life estimation model was proposed by introducing parameter gamma (γ) by modifying the Murakami’s model based on defect size and effective stress. A total of 100 fatigue test were conducted at different stress ratios followed by the post fracture defect analysis and surface roughness measurements. Different machine learning algorithms such as artificial neural network (ANN) and random forest (RF) etc. were employed to estimate the fatigue life. The results revealed that the randomly distributed subsurface porosities and surface defects increases the scatter in fatigue lives thereby leading to uncertainty in fatigue life prediction. Furthermore, the machine learning algorithms exhibit promising prediction performance, especially when considering the parameter gamma and combining surface roughness with porosity.
11:30 AM
Modeling the Effect of Powder Reuse on Meltpool Dynamics and Defect Formation in Additively Manufactured Components: Pranjal Chauhan1; Amarendra Kumar Singh1; 1Indian Institute of Technology Kanpur
The reuse of metal powder in additive manufacturing provides economic and environmental advantages through reduced raw material consumption and minimized waste. However, repeated powder reuse introduces variability in material properties, impacting melt pool stability and potentially compromising the structural integrity of manufactured parts. A 2D numerical model is developed to address these challenges by simulating the Electron Beam Melting (EBM) process with powder bed composed of a mixture of virgin and reused alloy powders. Overall, an ICME-based approach is implemented to optimize the blend ratios of virgin and reused powders, aiming to reduce defect formation and improve cost efficiency. This framework provides insights into powder management strategies to limit defect formation by optimizing the mixing ratio of virgin and reused powders for consistent part performance in additively manufactured EBM components.
11:50 AM
Computational Investigation on the Combined Effect of Surface Roughness and Pore Attributes on Strain Concentrators in Metal Additively Manufactured Materials: Erick Ramirez1; George Weber2; Saikumar Yeratapally3; Kenji Shimada1; 1Carnegie Mellon University; 2NASA Langley Research Center; 3Science and Technology Corporation
Metal additive manufacturing (AM) provides a pathway for creating highly optimized components. However, porosity and surface roughness (SR) continue to be prevalent issues for fatigue performance despite best efforts in optimizing process parameters and postprocessing techniques. This work uses finite element analysis to conduct a parametric study to link various pore attributes (i.e., size, aspect ratio, orientation, and location) to strain concentration factors (SCFs) in the presence of SR under elastic/plastic deformation during uniaxial loading. Keyhole and lack-of-fusion pores are idealized by prolate and oblate ellipsoids, respectively, while SR is reconstructed from high-resolution X-ray computed tomography images of an AM tensile specimen. Each simulation of the parametric study assumes a single pore in a Ti-6Al-4V material, modeled with J2-plasticity. This investigation reveals how isolated porosity increases SCF at the rough surface and how variability in SR geometry results in variability in SCF at the pore.