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1. Rapid Development of Metal Additive Manufacturing Using Artificial Intelligence/Machine Learning and High-Throughput Material Testing

   https://doi.org/10.1146/annurev-matsci-080423-121436  

   Adapa, Venkata_Surya Karthik; Kalidindi, Surya R; Saldana, Christopher J (July 2025, Annual Review of Materials Research)

   Metal additive manufacturing (AM) holds immense potential for developing advanced structural alloys. However, the complex, heterogeneous nature of AM-produced materials presents significant challenges to traditional material characterization and optimization methods. This review explores the integration of artificial intelligence (AI) and machine learning (ML) with high-throughput material characterization protocols to rapidly establish the process–structure–property (PSP) relationships critically needed to dramatically accelerate the development of metal AM processes. Combinatorial high-throughput evaluations, including rapid material synthesis and nonstandard high-throughput testing protocols, such as spherical indentation and small punch tests, are discussed for their capability to rapidly assess mechanical properties and establish PSP linkages. Furthermore, the review examines the role of AI and ML in optimizing AM processes, particularly through Bayesian optimization, which offers new avenues for efficient exploration of high-dimensional design spaces. The review envisions a future where AI- and ML-driven, autonomous AM development cycles significantly enhance material and process optimization. 

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2. Bayesian Calibration of Multiple Coupled Simulation Models for Metal Additive Manufacturing: A Bayesian Network Approach

   https://doi.org/10.1115/1.4052270  

   Ye, Jiahui; Mahmoudi, Mohamad; Karayagiz, Kubra; Johnson, Luke; Seede, Raiyan; Karaman, Ibrahim; Arroyave, Raymundo; Elwany, Alaa (March 2022, ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg)

   Abstract Modeling and simulation for additive manufacturing (AM) are critical enablers for understanding process physics, conducting process planning and optimization, and streamlining qualification and certification. It is often the case that a suite of hierarchically linked (or coupled) simulation models is needed to achieve the above tasks, as the entirety of the complex physical phenomena relevant to the understanding of process-structure-property-performance relationships in the context of AM precludes the use of a single simulation framework. In this study using a Bayesian network approach, we address the important problem of conducting uncertainty quantification (UQ) analysis for multiple hierarchical models to establish process-microstructure relationships in laser powder bed fusion (LPBF) AM. More significantly, we present the framework to calibrate and analyze simulation models that have experimentally unmeasurable variables, which are quantities of interest predicted by an upstream model and deemed necessary for the downstream model in the chain. We validate the framework using a case study on predicting the microstructure of a binary nickel-niobium alloy processed using LPBF as a function of processing parameters. Our framework is shown to be able to predict segregation of niobium with up to 94.3% prediction accuracy on test data. 

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3. Fish scale inspired structures - a review of materials, manufacturing and models

   https://doi.org/10.1088/1748-3190/ac7fd0  

   Hossain, Md Shahjahan; Ebrahimi, Hossein; Ghosh, Ranajay (October 2022, Bioinspiration & Biomimetics)

   Abstract Fish scales inspired materials platform can provide advanced mechanical properties and functionalities. These materials, inspired from fish scales take the form of either composite materials or multi-material discrete exoskeleton type structures. Over the last decade, they have been under intense scrutiny for generating tailorable and tunable stiffness, penetration and fracture resistance, buckling prevention, nonlinear damping, hydrodynamic and camouflaging functions. Such programmable behavior emerges from leveraging their unique morphology and structure-property relationships. Several advanced tools of characterization, manufacturing, modeling and computation have been employed to understand and discover their behavior. With the rapid proliferation of additive manufacturing (AM) techniques, and advancing envelope of modeling and computational methods, this field is seeing renewed efforts to realize even more ambitious designs. We present a review and recapitulation of the state-of-the art in fish scale inspired materials in this paper. 

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4. Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design

   https://doi.org/10.1007/s40192-022-00285-0  

   Mao, Yuwei; Yang, Zijiang; Jha, Dipendra; Paul, Arindam; Liao, Wei-keng; Choudhary, Alok; Agrawal, Ankit (November 2022, Integrating Materials and Manufacturing Innovation)

   Abstract There are two broad modeling paradigms in scientific applications: forward and inverse. While forward modeling estimates the observations based on known causes, inverse modeling attempts to infer the causes given the observations. Inverse problems are usually more critical as well as difficult in scientific applications as they seek to explore the causes that cannot be directly observed. Inverse problems are used extensively in various scientific fields, such as geophysics, health care and materials science. Exploring the relationships from properties to microstructures is one of the inverse problems in material science. It is challenging to solve the microstructure discovery inverse problem, because it usually needs to learn a one-to-many nonlinear mapping. Given a target property, there are multiple different microstructures that exhibit the target property, and their discovery also requires significant computing time. Further, microstructure discovery becomes even more difficult because the dimension of properties (input) is much lower than that of microstructures (output). In this work, we propose a framework consisting of generative adversarial networks and mixture density networks for inverse modeling of structure–property linkages in materials, i.e., microstructure discovery for a given property. The results demonstrate that compared to baseline methods, the proposed framework can overcome the above-mentioned challenges and discover multiple promising solutions in an efficient manner. 

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5. Bridging Data Gaps: A Federated Learning Approach to Heat Emission Prediction in Laser Powder Bed Fusion

   https://doi.org/10.1115/1.4065888  

   Lei, Rong; Guo, Y B; Yan, Jiwang; Guo, Weihong “Grace” (October 2024, Journal of Manufacturing Science and Engineering)

   Abstract Deep learning has impacted defect prediction in additive manufacturing (AM), which is important to ensure process stability and part quality. However, its success depends on extensive training, requiring large, homogeneous datasets—remaining a challenge for the AM industry, particularly for small- and medium-sized enterprises (SMEs). The unique and varied characteristics of AM parts, along with the limited resources of SMEs, hamper data collection, posing difficulties in the independent training of deep learning models. Addressing these concerns requires enabling knowledge sharing from the similarities in the physics of the AM process and defect formation mechanisms while carefully handling privacy concerns. Federated learning (FL) offers a solution to allow collaborative model training across multiple entities without sharing local data. This article introduces an FL framework to predict section-wise heat emission during laser powder bed fusion (LPBF), a vital process signature. It incorporates a customized long short-term memory (LSTM) model for each client, capturing the dynamic AM process's time-series properties without sharing sensitive information. Three advanced FL algorithms are integrated—federated averaging (FedAvg), FedProx, and FedAvgM—to aggregate model weights rather than raw datasets. Experiments demonstrate that the FL framework ensures convergence and maintains prediction performance comparable to individually trained models. This work demonstrates the potential of FL-enabled AM modeling and prediction where SMEs can improve their product quality without compromising data privacy. 

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