Mechanism-Guided and Data-Driven Acceleration of Materials Development for Metal Additive Manufacturing
A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Additive Manufacturing".
Deadline for manuscript submissions: 21 March 2027 | Viewed by 61
Editors
Interests: machine learning and physics-informed optimization of metal additive manufacturing; including melt pool analysis and simulation; metal powder characterization; process monitoring; process optimization
Interests: metal additive manufacturing and microstructure control; micromechanics of plasticity and damage; in situ micro-/nanoscale mechanical characterization; metallic materials in extreme environments
Special Issue Information
Dear Colleagues,
Metal additive manufacturing (AM) has become a key enabling technology for producing high-performance metallic components with complicated design. However, the development of new printable materials and reliable processing strategies remains a significant challenge due to the complex interactions among feedstock characteristics, processing parameters, microstructure evolution, and final component performance. Traditional trial-and-error approaches are often costly and time-consuming, highlighting the need for more efficient and intelligent materials development strategies.
Recent advances in computational materials science, physics-based modeling, machine learning, and data-driven optimization are transforming the way metallic materials are designed and processed for additive manufacturing. A central challenge in this field is to establish quantitative material–process–structure–performance relationships across multiple spatial and temporal scales. These relationships are often highly nonlinear and difficult to resolve using experiments or simulations alone. Data-driven methods and advanced computational models offer powerful tools for identifying hidden correlations, exploring large compositional and processing spaces, and predicting microstructure and performance. At the same time, mechanistic understanding and physics-informed constraints can improve model interpretability, robustness, and transferability while reducing the amount of experimental data required.
By integrating experiments, multiscale modeling, materials informatics, and machine learning, closed-loop materials development frameworks can be established, in which predictive models guide alloy and powder design, parameter selection, and experimental validation, while newly generated data continuously refine the models. This Special Issue aims to provide a forum for the latest developments in mechanism-guided and data-driven materials development for metal additive manufacturing. We welcome original research articles and comprehensive reviews that advance the understanding and acceleration of materials and process development through integrated experimental, computational, and data-driven approaches.
Dr. Jiahui Zhang
Dr. Zhiying Liu
Guest Editors
Manuscript Submission Information
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Keywords
- metal additive manufacturing
- materials development acceleration
- data-driven materials design
- structure–property relationships
- mechanism-guided modeling
- alloy and powder design
- integrated computational–experimental approaches
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