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Recent Progress in the Additive Manufacturing of Smart Materials

A Special Issue of Materials (ISSN 1996-1944) belonging to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 732

Editors


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Guest Editor
1. School of Mechanical and Manufacturing Engineering, Dublin City University, D09 V209 Dublin, Ireland
2. DCU Institute for Advanced Processing Technology (DCU APT), D09 V209 Dublin, Ireland
3. I-Form Advanced Manufacturing Research Centre, D04 C1P1 Dublin, Ireland
Interests: additive manufacturing; laser processing; smart material processing; material design and functionalisation
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Industrial Engineering, University of Rome Tor Vergata, 00133 Rome, Italy
Interests: smart polymers and composites; hybrid and nano-composites; smart material processing; surface treatments
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue presents recent advances in the additive manufacturing (AM) of smart materials, highlighting emerging strategies for fabricating. By enabling precise control over geometry, composition, and internal architecture, additive manufacturing offers unique opportunities to engineer materials and devices that actively respond to external stimuli such as temperature, stress, electric or magnetic fields, and chemical environments.

The contributions in this Special Issue explore a broad range of smart material systems, including stimuli-responsive polymers, multifunctional composites, piezoelectric materials, and metallic systems. Particular attention is given to shape memory alloys, especially nitinol, whose thermomechanical behavior makes them attractive for functional and adaptive components. Recent progress in AM techniques for processing such alloys is discussed, with emphasis on the control of microstructure, phase transformation behavior, and functional performance. Developments in 4D printing—where additively manufactured structures are designed to change shape or functionality over time in response to environmental triggers—are also highlighted, as well as structures with integrated sensing, actuation, and adaptive capabilities

Several articles further address advances in multi-material printing, process optimization, and microstructural tailoring that enable enhanced functionality and reliability. The application areas covered include biomedical devices, soft robotics, sensing systems, and aerospace structures. Collectively, the papers in this Special Issue illustrate the growing potential of additive manufacturing to enable next-generation smart materials and adaptive systems, while also identifying key challenges and future research directions for the field.

Prof. Dr. Dermot Brabazon
Dr. Denise Bellisario
Guest Editors

Manuscript Submission Information

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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

  • additive manufacturing
  • smart materials
  • shape memory alloys (nitinol)
  • 4D printing
  • stimuli-responsive materials
  • functional and adaptive structures

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Published Papers (1 paper)

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Research

30 pages, 44241 KB  
Article
Physics-Guided Decision-Support Framework for Melt Pool Prediction and Process Stability in Laser Powder Bed Fusion of Nitinol
by Sampreet Rangaswamy, Merve Nur Doğu, Camille Rubio, Hengfeng Gu, Abdul Khader Khan, Chong Teng, Inam Ul Ahad and Dermot Brabazon
Materials 2026, 19(17), 3696; https://doi.org/10.3390/ma19173696 - 30 Aug 2026
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Abstract
Powder bed fusion–laser beam (PBF-LB) of nickel–titanium (NiTi) has attracted increasing interest in aerospace, biomedical, and energy applications owing to its shape memory and superelastic properties, combined with the capability to fabricate complex geometries. However, the strong sensitivity of NiTi to thermal history [...] Read more.
Powder bed fusion–laser beam (PBF-LB) of nickel–titanium (NiTi) has attracted increasing interest in aerospace, biomedical, and energy applications owing to its shape memory and superelastic properties, combined with the capability to fabricate complex geometries. However, the strong sensitivity of NiTi to thermal history and process variability makes predictive modeling and process parameter selection challenging. In this work, a physics-guided decision-support framework is developed for melt pool prediction and stability assessment during the PBF-LB processing of NiTi. A high-fidelity thermal finite element model incorporating CALPHAD-derived, temperature-dependent material properties was calibrated using a subset of experimental measurements and independently validated against additional experimental melt pool data. The calibrated model demonstrated good agreement with experiments, yielding mean absolute percentage errors of 4.22% and 5.63% for melt pool width and depth, respectively, on the validation dataset. A multi-output random forest surrogate trained on the validated simulation dataset enabled rapid prediction of melt pool geometric features, achieving test-set R2 values exceeding 0.95, together with low MAE and RMSE values, while five-fold cross-validation confirmed robust predictive performance. The proposed framework integrates surrogate predictions with physics-based melt pool stability criteria to rapidly identify physically feasible processing conditions, thereby providing a computationally efficient foundation for future supervisory process control strategies. Full article
(This article belongs to the Special Issue Recent Progress in the Additive Manufacturing of Smart Materials)
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