Computational and Data-Driven Optimization of Materials for Additive Manufacturing
A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Materials Simulation and Design".
Deadline for manuscript submissions: 20 November 2026 | Viewed by 186
Editors
Interests: 3D printing; machine learning; process modeling and optimization
Special Issues, Collections and Topics in MDPI journals
Interests: 3D printing; additive manufacturing; advanced manufacturing; powder materials
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Additive manufacturing (AM) has evolved into a cornerstone of industrial innovation, enabling the fabrication of high-performance components across multiple scales—from micro-electronics to large-scale structural parts. However, achieving superior material integrity remains a significant challenge, as the final properties are fundamentally dictated by the complex physical, thermal, and chemical interactions during the printing process. To move beyond empirical trial-and-error, the field is increasingly adopting computational and data-driven optimization of both materials and their underlying processing conditions.
The synergy between material science and process engineering is central to AM advancement. Recent breakthroughs in materials informatics, high-fidelity modeling, and artificial intelligence (AI) are unlocking new pathways to optimize energy and mass delivery, processing history, and spatial deposition trajectories. These data-centric strategies not only accelerate the discovery of novel feedstocks, but also ensure that extrusion, scanning, or jetting parameters are precisely tuned to minimize defects such as warpage and porosity, refine microstructures, and guarantee the reliability of printed materials.
Given this critical intersection, this Special Issue of Materials is dedicated to “Computational and Data-Driven Optimization of Materials for Additive Manufacturing.” We invite contributions that demonstrate how advanced computational frameworks can bridge the gap between process control and material performance.
This Special Issue welcomes original research articles, reviews, and case studies on topics including, but not limited to, the following:
- Data-Driven Process Optimization: AI and machine learning frameworks for optimizing deposition rates, extrusion temperatures, power/mass input, and spatial toolpaths to enhance print quality.
- Material–Process Synergy: Computational modeling of the "Process–Structure–Property" relationship to predict and control material behavior.
- Physics-Informed Neural Networks (PINNs): Hybrid models for real-time monitoring of fluid dynamics, polymer rheology, or phase transformations.
- Computational Material Discovery: Informatics-based design of novel filaments, alloys, resins, and inks tailored for specific AM process windows.
- In situ Monitoring and Closed-Loop Control: Utilizing data analytics and smart sensing to adaptively optimize processes for material consistency and dimensional accuracy.
- Multiscale Modeling of Material Evolution: Simulating microstructure formation, crystallization, grain growth, and interface dynamics during the AM processing cycle.
- Uncertainty Quantification (UQ): Stochastic analysis of how processing fluctuations (e.g., flow instability, energy variability) impact the reliability of material properties.
Generative Design and Topology Optimization: Optimization of structures with spatially tailored properties enabled by intelligent process planning
Dr. Haining Zhang
Dr. Joon Phil Choi
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Materials is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- computational modeling
- machine learning
- additive manufacturing
- process optimization
- material design
- artificial intelligence
- material development
- digital twins
- data-driven modeling
- advanced manufacturing
- sustainable manufacturing
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