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Multiscale Design and Optimisation for Metal Additive Manufacturing
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Multiscale Design and Optimisation for Metal Additive Manufacturing

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 1791

Special Issue Editors

Dr. Bartłomiej Wysocki

E-Mail Website
Guest Editor
Multidisciplinary Research Center, Cardinal Stefan Wyszynski University in Warsaw, Warsaw, Poland
Interests: additive manufacturing; titanium alloys; chemical polishing; cellular structures; implants; bioengineering; AI/ML
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Joseph Buhagiar

E-Mail Website
Guest Editor
Department of Metallurgy and Materials Engineering, Faculty of Engineering, University of Malta, Msida, Malta
Interests: biomaterials; biodegradable metals; surface engineering; corrosion; S-phase; corrosion–wear
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jian Chen

E-Mail Website
Guest Editor
School of Materials Science and Engineering, Southeast University, Nanjing, China
Interests: surface engineering; plasma treatments; nanomechanics; wear; corrosion; medical implants; batteries

Special Issue Information

Dear Colleagues,

Metal additive manufacturing (AM) enables the fabrication of complex, high-performance components with unprecedented geometric freedom. However, achieving reliable and application-specific properties requires a deep understanding of material behaviour across multiple length and time scales—from atomic interactions and phase transformations, through melt-pool dynamics and microstructure evolution, to the macroscopic performance of final parts.

This Special Issue, “Multiscale Design and Optimisation for Metal Additive Manufacturing”, aims to collect cutting-edge research focused on multiscale modelling, simulation-driven design, and physics-based as well as data-driven optimisation strategies for metal AM processes. Contributions addressing atomistic and mesoscale modelling, phase-field and CFD simulations, thermal–mechanical process modelling, and integrated multiscale frameworks linking process parameters with microstructure and mechanical performance are particularly encouraged.

Special attention will be given to advanced optimisation methods, including artificial intelligence and machine-learning-assisted modelling, process monitoring, parameter optimisation, and design of alloys and structures tailored for metal AM. Publications addressing post-processing treatments (e.g., heat treatment, surface engineering, chemical and mechanical finishing) as well as the application of novel AM equipment and machine architectures are also within the scope of this Special Issue. Studies combining numerical simulations with in situ monitoring, µ-CT, and experimental validation for process optimisation and quality control are highly welcome.

By bridging fundamental materials science with process engineering, artificial intelligence, and computational optimisation, this Special Issue seeks to advance predictive, simulation-driven, and multiscale design approaches that enable robust, efficient, and property-tailored metal additive manufacturing.

Dr. Bartłomiej Wysocki
Prof. Dr. Joseph Buhagiar
Prof. Dr. Jian Chen
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-blind 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

  • metal additive manufacturing
  • multiscale modelling
  • atomistic simulation
  • process simulation
  • microstructure evolution
  • process optimisation
  • artificial intelligence
  • machine learning
  • physics-based modelling
  • data-driven optimisation
  • post-processing
  • heat treatment
  • surface engineering
  • in situ monitoring
  • µ-CT
  • advanced AM systems

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Published Papers (1 paper)

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Research

28 pages, 6311 KB  
Article
Machine Learning-Assisted Optimisation of the Laser Beam Powder Bed Fusion (PBF-LB) Process Parameters of H13 Tool Steel Fabricated on a Preheated to 350 ∘C Building Platform
by Katsiaryna Kosarava, Paweł Widomski, Michał Ziętala, Daniel Dobras, Marek Muzyk and Bartłomiej Adam Wysocki
Materials 2026, 19(1), 210; https://doi.org/10.3390/ma19010210 - 5 Jan 2026
Viewed by 1438
Abstract
This study presents the first application of Machine Learning (ML) models to optimise Powder Bed Fusion using Laser Beam (PBF-LB) process parameters for H13 steel fabricated on a 350 °C preheated building platform. A total of 189 cylindrical specimens were produced for training [...] Read more.
This study presents the first application of Machine Learning (ML) models to optimise Powder Bed Fusion using Laser Beam (PBF-LB) process parameters for H13 steel fabricated on a 350 °C preheated building platform. A total of 189 cylindrical specimens were produced for training and testing machine learning (ML) models using variable process parameters: laser power (250–350 W), scanning speed (1050–1300 mm/s), and hatch spacing (65–90 μm). Eight ML models were investigated: 1. Support Vector Regression (SVR), 2. Kernel Ridge Regression (KRR), 3. Stochastic Gradient Descent Regressor, 4. Random Forest Regressor (RFR), 5. Extreme Gradient Boosting (XGBoost), 6. Extreme Gradient Boosting with limited depth (XGBoost LD), 7. Extra Trees Regressor (ETR) and 8. Light Gradient Boosting Machine (LightGBM). All models were trained using the Fast Library for Automated Machine Learning & Tuning (FLAML) framework to predict the relative density of the fabricated samples. Among these, the XGBoost model achieved the highest predictive accuracy, with a coefficient of determination R2=0.977, mean absolute percentage error MAPE = 0.002, and mean absolute error MAE = 0.017. Experimental validation was conducted on 27 newly fabricated samples using ML predicted process parameters. Relative densities exceeding 99.6% of the theoretical value (7.76 g/cm3) for all models except XGBoost LD and KRR. The lowest MAE = 0.004 and the smallest difference between the ML-predicted and PBF-LB validated density were obtained for samples made with LightGBM-predicted parameters. Those samples exhibited a hardness of 604 ± 13 HV0.5, which increased to approximately 630 HV0.5 after tempering at 550 °C. The LightGBM optimised parameters were further applied to fabricate a part of a forging die incorporating internal through-cooling channels, demonstrating the efficacy of machine learning-guided optimisation in achieving dense, defect-free H13 components suitable for industrial applications. Full article
(This article belongs to the Special Issue Multiscale Design and Optimisation for Metal Additive Manufacturing)
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