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Additive Manufacturing in Material Processing
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Additive Manufacturing in Material Processing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Additive Manufacturing Technologies".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 2906

Special Issue Editor

Dr. Erfan Maleki

E-Mail Website
Guest Editor
1. National Center for Additive Manufacturing Excellence (NCAME), Auburn University, Auburn, AL 36849, USA
2. Department of Mechanical Engineering, Auburn University, Auburn, AL 36849, USA
Interests: fatigue and fracture mechanics; additive manufacturing; crack initiation; surface treatments
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Additive manufacturing, often referred to as 3D printing, has revolutionized the landscape of material processing across various industries. This Special Issue aims to provide a platform for scholars, researchers, and practitioners to share their cutting-edge insights, innovations, and discoveries in the realm of additive manufacturing. We anticipate that this collection will offer valuable contributions to the ongoing discourse surrounding this transformative technology.

The scope of this Special Issue encompasses a wide range of topics related to additive manufacturing in material processing, including but not limited to:

  1. Advanced Materials: Exploration of novel materials and material combinations suitable for additive manufacturing, including metals, polymers, ceramics, and composites.
  2. Process Development: Research on optimizing additive manufacturing processes, including parameter tuning, printing techniques, and post-processing methodologies.
  3. Design and Optimization: Studies focused on design principles, topology optimization, and generative design techniques to harness the full potential of additive manufacturing.
  4. Quality Assurance: Investigations into quality control, defect detection, and metrology to ensure the reliability and repeatability of printed components.
  5. Applications and Case Studies: Real-world applications of additive manufacturing in various industries, such as aerospace, healthcare, automotive, and consumer goods.
  6. Sustainability: Assessments of the environmental impact of additive manufacturing processes and the development of sustainable practices within the field.
  7. Interdisciplinary Approaches: Collaborative research that bridges the gap between additive manufacturing and other disciplines, such as materials science, engineering, robotics, and artificial intelligence.

    We invite researchers from academia and industry to submit their original research articles, reviews, and case studies that align with the themes outlined above. Our goal is to present a diverse and comprehensive collection of contributions that will foster discussions, inspire new ideas, and advance our understanding of additive manufacturing's role in material processing.

    Authors are encouraged to submit their manuscripts through Applied Sciences’ online submission system, following the guidelines and formatting instructions available on the journal's website. Submissions will undergo a rigorous peer-review process to ensure the highest quality of published work.

    We anticipate that "Additive Manufacturing in Material Processing" will be a significant milestone in the field, and we look forward to working closely with you and the esteemed editorial team of Applied Sciences to bring this Special Issue to fruition.

Dr. Erfan Maleki
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.

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. Applied Sciences 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 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
  • material processing
  • mechanical properties
  • advanced materials
  • process optimization

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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17 pages, 27765 KiB  
Article
Influence of Graphene Oxide on Printability, Rheological and Mechanical Properties of Highly Filled Alumina Filaments and Sintered Parts Produced by FFF
by Ekaterina Kuznetsova, Anton Smirnov, Andrey V. Gusarov, Nestor Washington Solís Pinargote, Tatiana V. Tarasova and Sergey N. Grigoriev
Appl. Sci. 2024, 14(24), 11986; https://doi.org/10.3390/app142411986 - 21 Dec 2024
Viewed by 818
Abstract
The aim of the study was to investigate the effect of the addition of graphene oxide (GO) on the rheological and mechanical properties of extruded polyamide 12 (PA12) filaments with high aluminum oxide (Al2O3) content used for 3D printing [...] Read more.
The aim of the study was to investigate the effect of the addition of graphene oxide (GO) on the rheological and mechanical properties of extruded polyamide 12 (PA12) filaments with high aluminum oxide (Al2O3) content used for 3D printing using the fused filament fabrication (FFF) method. Firstly, Al2O3-based mixtures with 0.10, 0.25 and 0.50 vol.% GO content were prepared. These mixtures were dried and subsequently combined with paraffin wax (PW), stearic acid (SA) and polyamide 12 (PA12) in an organic solvent. After drying in a vacuum oven and sifting, powder compositions of 74 wt.% (Al2O3 + GO)/26 wt.% (PA12 + PW + SA) with different GO content were obtained. All compositions were successfully extruded into filaments for 3D printing. Rheological, microstructural and mechanical studies of the compositions and filaments were carried out. X-ray diffraction phase analysis and Raman spectroscopy were also performed. It was shown that 0.10 and 0.25% vol. GO proved to be a universal additive that resulted in an increase in the rheological and mechanical properties of the highly filled polymer and also improved its 3D printability, which ultimately helped obtain a ceramic product with complex shape using the FFF method. Full article
(This article belongs to the Special Issue Additive Manufacturing in Material Processing)
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20 pages, 1619 KiB  
Systematic Review
A Breakthrough in Producing Personalized Solutions for Rehabilitation and Physiotherapy Thanks to the Introduction of AI to Additive Manufacturing
by Emilia Mikołajewska, Dariusz Mikołajewski, Tadeusz Mikołajczyk and Tomasz Paczkowski
Appl. Sci. 2025, 15(4), 2219; https://doi.org/10.3390/app15042219 - 19 Feb 2025
Viewed by 1364
Abstract
The integration of artificial intelligence (AI) with additive manufacturing (AM) is driving breakthroughs in personalized rehabilitation and physical therapy solutions, enabling precise customization to individual patient needs. This article presents the current state of knowledge and perspectives of using personalized solutions for rehabilitation [...] Read more.
The integration of artificial intelligence (AI) with additive manufacturing (AM) is driving breakthroughs in personalized rehabilitation and physical therapy solutions, enabling precise customization to individual patient needs. This article presents the current state of knowledge and perspectives of using personalized solutions for rehabilitation and physiotherapy thanks to the introduction of AI to AM. Advanced AI algorithms analyze patient-specific data such as body scans, movement patterns, and medical history to design customized assistive devices, orthoses, and prosthetics. This synergy enables the rapid prototyping and production of highly optimized solutions, improving comfort, functionality, and therapeutic outcomes. Machine learning (ML) models further streamline the process by anticipating biomechanical needs and adapting designs based on feedback, providing iterative refinement. Cutting-edge techniques leverage generative design and topology optimization to create lightweight yet durable structures that are ideally suited to the patient’s anatomy and rehabilitation goals .AI-based AM also facilitates the production of multi-material devices that combine flexibility, strength, and sensory capabilities, enabling improved monitoring and support during physical therapy. New perspectives include integrating smart sensors with printed devices, enabling real-time data collection and feedback loops for adaptive therapy. Additionally, these solutions are becoming increasingly accessible as AM technology lowers costs and improves, democratizing personalized healthcare. Future advances could lead to the widespread use of digital twins for the real-time simulation and customization of rehabilitation devices before production. AI-based virtual reality (VR) and augmented reality (AR) tools are also expected to combine with AM to provide immersive, patient-specific training environments along with physical aids. Collaborative platforms based on federated learning can enable healthcare providers and researchers to securely share AI insights, accelerating innovation. However, challenges such as regulatory approval, data security, and ensuring equity in access to these technologies must be addressed to fully realize their potential. One of the major gaps is the lack of large, diverse datasets to train AI models, which limits their ability to design solutions that span different demographics and conditions. Integration of AI–AM systems into personalized rehabilitation and physical therapy should focus on improving data collection and processing techniques. Full article
(This article belongs to the Special Issue Additive Manufacturing in Material Processing)
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