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Additive Manufacturing and Machining Technologies for Engineering Materials

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Materials Science and Engineering".

Deadline for manuscript submissions: 20 January 2026 | Viewed by 163

Special Issue Editor


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Guest Editor
ENEA, Laboratory Smart Components and Systems for Sustainable Manufacturing, Department for Sustainability, Division Technologies and Materials for Sustainable Manufacturing Industry, 80055 Portici, Italy
Interests: additive manufacturing; advanced materials; energy conversion; materials chemistry

Special Issue Information

Dear Colleagues,

The development of new materials with remarkable properties has led to the enhancement of traditional machining technologies. Moreover, changing market dynamics have shifted the focus from mass production to customized mass production, and from the product to the end consumer. As a result, the optimization of production is becoming increasingly fundamental. Additive manufacturing is rapidly emerging as a key element for manufacturing companies in this evolving landscape. Advances in hybrid machining processes are on the rise, combining the benefits of both methods—the efficiency of traditional machining and the precision of modern techniques—enabling applications in aerospace, biomedical, and automotive industries. Additionally, future research is expected to focus on AI-driven process control, novel composite materials, and further integration of Industry 4.0 technologies.

This Special Issue aims to address the current trends and research gaps regarding novel materials, emerging technologies, advanced applications, and innovations in process and post-processing for additive manufacturing.

Dr. Pierpaolo Iovane
Guest Editor

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 100 words) can be sent to the Editorial Office for announcement on this website.

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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 2400 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
  • engineering materials
  • 3D printing
  • process optimization

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Published Papers (1 paper)

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Research

17 pages, 1635 KiB  
Article
Predicting Relative Density of Pure Magnesium Parts Produced by Laser Powder Bed Fusion Using XGBoost
by Kristijan Šket, Snehashis Pal, Janez Gotlih, Mirko Ficko and Igor Drstvenšek
Appl. Sci. 2025, 15(15), 8592; https://doi.org/10.3390/app15158592 (registering DOI) - 2 Aug 2025
Abstract
In this work, Laser Powder Bed Fusion (LPBF), an additive manufacturing (AM) process, was optimised to produce pure magnesium components. The focus of the presented work is on the prediction of the relative product density using the machine learning model XGBoost to improve [...] Read more.
In this work, Laser Powder Bed Fusion (LPBF), an additive manufacturing (AM) process, was optimised to produce pure magnesium components. The focus of the presented work is on the prediction of the relative product density using the machine learning model XGBoost to improve the production process and thus the usability of the material for practical use. Experimental tests with different parameters, laser power, scanning speed and layer thickness, and fixed parameters, track overlapping and hatching distance, were analysed and resulted in relative material densities between 89.29% and 99.975%. The XGBoost model showed high predictive power, achieving an R2 test result of 0.835, a mean absolute error (MAE) of 0.728 and a root mean square error (RMSE) of 0.982. Feature importance analysis showed that the interaction of laser power and scanning speed had the largest influence on the predictions at 35.9%, followed by laser power × layer thickness at 29.0%. The individual contributions were laser power (11.8%), scanning speed (10.7%), scanning speed × layer thickness (9.0%) and layer thickness (3.6%). These results provide a data-based method for LPBF parameter settings that improve manufacturing efficiency and component performance in the aerospace, automotive and biomedical industries and identify optimal parameter regions for a high density, serving as a pre-optimisation stage. Full article
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