Process modeling in laser powder bed fusion towards defect detection and quality control via machine learning: The state-of-the-art and research challenges

P Wang, Y Yang, NS Moghaddam - Journal of Manufacturing Processes, 2022 - Elsevier
In recent years, machine learning (ML) techniques have been extensively investigated to
strengthen the understanding of the complex process dynamics underlying metal additive …

Nickel-based superalloy single crystals fabricated via electron beam melting

P Fernandez-Zelaia, MM Kirka, AM Rossy, Y Lee… - Acta Materialia, 2021 - Elsevier
Additive manufacturing technologies have emerged as potentially disruptive processes
whose possible impacts range across supply chain logistics, prototy**, and novel …

Feature engineering of material structure for AI-based materials knowledge systems

SR Kalidindi - Journal of Applied Physics, 2020 - pubs.aip.org
This tutorial introduces systematically the foundational concepts undergirding the recently
formulated AI (artificial intelligence)-based materials knowledge system (AI-MKS) …

Application of Gaussian process regression models for capturing the evolution of microstructure statistics in aging of nickel-based superalloys

YC Yabansu, A Iskakov, A Kapustina, S Rajagopalan… - Acta Materialia, 2019 - Elsevier
Nickel-based superalloys, used extensively in advanced gas turbine engines, exhibit
complex microstructures that evolve during exposure to high temperatures (ie, aging …

Influence of geometry on columnar to equiaxed transition during electron beam powder bed fusion of IN718

N Raghavan, BC Stump, P Fernandez-Zelaia… - Additive …, 2021 - Elsevier
Correlation between spot-melt scan parameters (linear spot-density aka areal energy
density), build geometry, and solidification microstructure evolution (columnar vs equiaxed) …

Gaussian process autoregression models for the evolution of polycrystalline microstructures subjected to arbitrary stretching tensors

S Hashemi, SR Kalidindi - International Journal of Plasticity, 2023 - Elsevier
Crystal plasticity finite element models (CPFEM) have shown tremendous potential for
simulating the microstructure evolution paths in polycrystalline aggregates subjected to …

A machine learning framework for the temporal evolution of microstructure during static recrystallization of polycrystalline materials simulated by cellular automaton

S Hashemi, SR Kalidindi - Computational Materials Science, 2021 - Elsevier
Reduced-order models of process-structure evolution linkages play a central role in the
discovery and development of new/improved materials and their deployment in advanced …

Estimation of local strain fields in two-phase elastic composite materials using UNet-based deep learning

M Raj, S Thakre, RK Annabattula… - Integrating Materials and …, 2021 - Springer
The knowledge of the distribution of local micromechanical fields is crucial in the design of
composite materials. Traditionally full-field methods (such as finite element methods) and …

The effects of material anisotropy on secondary processing of additively manufactured CoCrMo

P Fernandez-Zelaia, V Nguyen, H Zhang, A Kumar… - Additive …, 2019 - Elsevier
Components produced by near net shape additive manufacturing processes often require
subsequent subtractive finishing operations to satisfy requisite surface finish and geometric …

Uncertainty quantification and propagation in the microstructure-sensitive prediction of the stress-strain response of woven ceramic matrix composites

AP Generale, SR Kalidindi - Computers & Structures, 2023 - Elsevier
Hierarchical multiscale modeling of heterogeneous materials has traditionally relied upon a
deterministic estimation of constitutive properties when making microstructure-sensitive …