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3D Printing Technologies and Additive Manufacturing: Recent Advances and Applications

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

Deadline for manuscript submissions: 20 July 2025 | Viewed by 10091

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Special Issue Editors

Dr. Gabriella Tarantino

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Guest Editor

Laboratory of Mechanics Paris-Saclay (LMPS), Université Paris-Saclay, CentraleSupélec, ENS Paris-Saclay, CNRS, 4 Av. des Sciences, 91190 Gif-sur-Yvette, France
Interests: additive-manufacturing; composite materials; plasticity and damage mechanics

Dr. Erfan Maleki

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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
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Dear Colleagues,

Additive manufacturing (AM) is a computer-aided fabrication technology that allows the creation of a physical object from a digital model. It is the opposite of conventional, i.e., subtractive manufacturing as it relies on adding successive layers of material to build parts of any geometric shape. AM offers several advantages over traditional manufacturing. These include higher design flexibility and simplified fabrication, alongside reduced material usage and waste, for example. Today, AM has created a paradigm shift across several sectors of industry and academic research, enabling the design of more sustainable engineering components and products. Examples comprise, but are not limited to, weight-saving parts for the aerospace and automotive industry, patient-specific medical implants and new-to-market energy storage devices.

This Special Issue aims to cover recent advances and applications in the broad field of AM. Contributions, either in the form of reviews and research articles, are invited. Topics can focus on, but are not restricted to, the following:

AM materials (e.g. multi-graded, functionally graded, active and biochemical materials);
Technologies for 3D printing (e.g., powder bed fusion, direct energy deposition, and material jetting);
Applications of AM (e.g., in energy storage, healthcare, and structural lightweighting);
Topology optimization for AM (e.g., by artificial intelligence, AI);
The experimental characterization of additively manufactured materials and components (e.g., via X-ray tomography, in-situ testing, and acoustic emission);
The modeling and simulation of AM processes.

Dr. Gabriella Tarantino
Dr. Erfan Maleki
Guest Editors

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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

lightweighting
energy storage
sustainability
healthcare
3D-printing technology
X-ray tomography
inverse design
modeling
topology optimization

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Published Papers (7 papers)

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Research

26 pages, 7480 KiB

Open AccessArticle

Shaping and Characterization of Additively Manufactured Geopolymer Materials for Underwater Applications

by Anton Frederik Becher, Henning Zeidler, Szymon Gądek and Kinga Korniejenko

Appl. Sci. 2025, 15(7), 3449; https://doi.org/10.3390/app15073449 - 21 Mar 2025

Cited by 1 | Viewed by 207

Abstract

Additive manufacturing brings many benefits to the building industry, one of them being automatization and the possibility to work in harsh environments, including underwater applications. In addition, this technology enables faster infrastructure repairs and adjustments to the scope of work to specific damage caused by, for example, biocorrosion processes. The main aim of this article is to investigate the development of geopolymers as a printable material for civil engineering, including underwater applications. For that purpose, the process of the material extrusion will be modified, and material properties will be improved. In the first step, the raw materials were investigated (SEM, EDS, XRF, particle size analysis) and the proper additives were selected based on literature analysis. Next, geopolymer paste was synthesized and fresh paste properties were investigated, including time for curing samples and workflow. The mixture composition was modified to obtain the required printable parameters through the application of different additives and the modification of the proportion of components, especially alkali solution. Finally, small-scale additive manufacturing trials were conducted in the air and with submerged containers. Additionally, samples were prepared using the casting method to compare the mechanical properties and microstructure. The obtained results show that additives such as xanthan gum and superplasticizer improve the rheological properties of the paste efficiently. With the help of additive manufacturing, geopolymer samples with compressive strengths of up to 7.5 MPa and flexural strengths of up to 4.15 MPa after 28 respectively were achieved. Compared to the average of the cast samples, the compressive strength of the printed samples was at least 5% lower, while the flexural strength was at least 38% lower for printed samples. The 3D-printed samples showed strong anisotropy between the tested orientations of the flexural strength samples. Full article

(This article belongs to the Special Issue 3D Printing Technologies and Additive Manufacturing: Recent Advances and Applications)

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24 pages, 9132 KiB

Open AccessArticle

Development of a 3D-Printed Chest Phantom with Simulation of Lung Nodules for Studying Ultra-Low-Dose Computed Tomography Protocols

by Jenna Silberstein, Steven Tran, Yin How Wong, Chai Hong Yeong and Zhonghua Sun

Appl. Sci. 2025, 15(1), 309; https://doi.org/10.3390/app15010309 - 31 Dec 2024

Viewed by 1545

Abstract

This study aimed to 3D print a patient-specific chest phantom simulating multiple lung nodules to optimise low-dose Computed Tomography (CT) protocols for lung cancer screening. The chest phantom, which was developed from a single patient’s chest CT images, was fabricated using a variety of materials, including polylactic acid (PLA), Glow-PLA, acrylonitrile butadiene styrene (ABS), and polyurethane resin. The phantom was scanned under different low-dose (LDCT) and ultra-low-dose CT (ULDCT) protocols by varying the kilovoltage peak (kVp) and milliampere-seconds (mAs). Subjective image quality of each scan (656 images) was evaluated by three radiologists using a five-point Likert scale, while objective image quality was assessed using signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Anatomical conformance was assessed by comparing tissue diameters of the phantom and patient scans using Bland–Altman analysis. The phantom’s lung tissue, lung nodules, and diaphragm demonstrated radiation attenuation comparable to patient tissue, as measured in Hounsfield Units (HU). However, significant variations in HU were observed for the skin, subcutaneous fat, muscle, bone, heart, lung vessels, and blood vessels compared to patient tissues, with values ranging from 93.9 HU to −196 HU (p < 0.05). Both SNR and CNR decreased as the effective dose was reduced, with a strong positive linear correlation (r = 0.927 and r = 0.931, respectively, p < 0.001, Jamovi, version 2.3.28). The median subjective image quality score from radiologists was 4, indicating good diagnostic confidence across all CT protocols (κ = −0.398, 95% CI [−0.644 to −0.152], p < 0.002, SPSS Statistics, version 30). An optimal protocol of 80 kVp and 30 mAs was identified for lung nodule detection, delivering a dose of only 0.23 mSv, which represents a 96% reduction compared to standard CT protocols. The measurement error between patient and phantom scans was −0.03 ± 0.14 cm. These findings highlight the potential for significant dose reductions in lung cancer screening programs. Further studies are recommended to improve the phantom by selecting more tissue-equivalent materials. Full article

(This article belongs to the Special Issue 3D Printing Technologies and Additive Manufacturing: Recent Advances and Applications)

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16 pages, 3160 KiB

Open AccessArticle

Medical- and Non-Medical-Grade Polycaprolactone Mesh Printing for Prolapse Repair: Establishment of Melt Electrowriting Prototype Parameters

by Maria F. R. Vaz, Joana A. P. Martins, Fábio Pinheiro, Nuno M. Ferreira, Sofia Brandão, Jorge L. Alves, António A. Fernandes, Marco P. L. Parente and Maria E. T. Silva

Appl. Sci. 2024, 14(21), 9670; https://doi.org/10.3390/app14219670 - 23 Oct 2024

Cited by 2 | Viewed by 1046

Abstract

Pelvic organ prolapse (POP) occurs due to inadequate support of female pelvic organs and is often treated with synthetic implants. However, complications like infections, mesh shrinkage, and tissue erosion can arise due to biomechanical incompatibilities with native tissue. This study aimed to optimize the melt electrowriting process using medical-grade biodegradable Poly(ε-caprolactone) (PCL) with a pellet extruder to print meshes that mimic the mechanical properties of vaginal tissue. Square and diagonal mesh designs with filament diameters of 80 µm, 160 µm, and 240 µm were produced and evaluated through mechanical testing, comparing them to a commercial mesh and sheep vaginal tissue. The results showed that when comparing medical-grade with non-medical-grade square meshes, there was a 54% difference in the Secant modulus, with the non-medical-grade meshes falling short of matching the properties of vaginal tissue. The square-shaped medical-grade PCL mesh closely approximated vaginal tissue, showing only a 13.7% higher Secant modulus and a maximum stress of 0.29 MPa, indicating strong performance. Although the diagonal-shaped mesh exhibited a 14% stress difference, its larger Secant modulus discrepancy of 45% rendered it less suitable. In contrast, the commercial mesh was significantly stiffer, measuring 77.5% higher than vaginal tissue. The diagonal-shaped mesh may better match the stress–strain characteristics of vaginal tissue, but the square-shaped mesh offers stronger support due to its higher stress–strain curve. Overall, meshes printed with medical-grade PCL show superior performance compared to non-medical-grade meshes, suggesting that they are a promising avenue for future advancements in the field of POP repair. Full article

(This article belongs to the Special Issue 3D Printing Technologies and Additive Manufacturing: Recent Advances and Applications)

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18 pages, 4575 KiB

Open AccessArticle

Enhancing Printability Through Design Feature Analysis for 3D Food Printing Process Optimization

by Mohammed Alghamdy, Iris He, Guru Ratan Satsangee, Hadi Keramati and Rafiq Ahmad

Appl. Sci. 2024, 14(20), 9587; https://doi.org/10.3390/app14209587 - 21 Oct 2024

Cited by 1 | Viewed by 1891

Abstract

We present a novel, systematic method for evaluating design printability in 3D food printing using a scoring system based on the Design for Additive Manufacturing (DfAM) guidelines. This study addresses a gap in the current literature by proposing a structured approach to assess and enhance the printability of 3D food designs. Our framework consists of a set of nine critical questions derived from the multi-level DfAM guidelines, focusing on key printability factors including unsupported features, geometric accuracy, and surface finish. The evaluation process converts qualitative assessments into numerical values, resulting in a comprehensive printability score that categorizes designs into high, moderate, or low printability levels. To validate the effectiveness of this method, we conducted a case study involving five different designs. The scoring system successfully explores the design space and maximizes the printability of 3D food products. This method alleviates the challenges in design evaluation compared with traditional trial-and-error approaches. The results demonstrate the practicality and efficiency of our framework’s output. The proposed methodology provides a structured approach to design evaluation, offering practical insights and a valuable tool for improving the success rate of 3D printed food products. This research contributes to the field by offering a systematic framework for assessing and enhancing the printability of 3D food designs, potentially accelerating the adoption and effectiveness of 3D food printing technology in various applications. Full article

(This article belongs to the Special Issue 3D Printing Technologies and Additive Manufacturing: Recent Advances and Applications)

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18 pages, 6795 KiB

Open AccessArticle

A Meshless Method of Radial Basis Function-Finite Difference Approach to 3-Dimensional Numerical Simulation on Selective Laser Melting Process

by Chieh-Li Chen, Cheng-Hsuan Wu and Cha’o-Kuang Chen

Appl. Sci. 2024, 14(15), 6850; https://doi.org/10.3390/app14156850 - 5 Aug 2024

Cited by 1 | Viewed by 1334

Abstract

Selective laser melting (SLM) is a rapidly evolving technology that requires extensive knowledge and management for broader industrial adoption due to the complexity of phenomena involved. The selection of parameters and numerical analysis for the SLM process are both costly and time-consuming. In this paper, a three-dimensional radial basis function-finite difference (RBF-FD) meshless model is introduced to accurately and efficiently simulate the molten pool size and temperature distribution during the SLM process for austenitic stainless steel (AISI 316L). Two different volumetric moving heat source models were presented, namely the ray-tracing method heat source model and the double-ellipsoidal shape heat source model. The temperature-dependent material properties and phase change process were also considered, based on experiments and effective models. Results of the model for the molten pool size were validated with those of the literature. The proposed approach can be used to predict the effect of different laser power and scan speed on the molten pool size and temperature gradient along the depth direction. The result reveals that the depth of the molten pool is more sensitive to laser power than scan speed. Under the same scan speed, a 22% change in laser power (45 ± 10 W) affects the maximum temperature proportionally by about 9%. The developed algorithm is computationally efficient and suitable for industrial applications. Full article

(This article belongs to the Special Issue 3D Printing Technologies and Additive Manufacturing: Recent Advances and Applications)

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16 pages, 6184 KiB

Open AccessArticle

Modeling and Prediction of Surface Roughness in Hybrid Manufacturing–Milling after FDM Using Artificial Neural Networks

by Strahinja Djurović, Dragan Lazarević, Bogdan Ćirković, Milan Mišić, Milan Ivković, Bojan Stojčetović, Martina Petković and Aleksandar Ašonja

Appl. Sci. 2024, 14(14), 5980; https://doi.org/10.3390/app14145980 - 9 Jul 2024

Cited by 12 | Viewed by 1722

Abstract

Three-dimensional printing, or additive manufacturing, represents one of the fastest growing branches of the industry, and fused deposition modeling (FDM) is one of most frequently used technologies. Three-dimensional printing does not provide high-quality surfaces, so finishing is required, and milling is one of the best methods for improving surface quality. The combination of 3D printing and traditional manufacturing technologies is known as hybrid manufacturing. In order to improve quality and determine optimal machining parameters, researchers increasingly use artificial intelligence methods. In the context of manufacturing technologies, both multiple regression analysis (MRA) and artificial neural networks (ANNs) have proven to be highly reliable in predicting and optimizing machining processes. This study focuses on the use of MRA and an ANN to analyze the influence of machining parameters such as feed rate, depth of cut, and spindle speed on the surface roughness of a 3D-printed part in a milling process. The study compares the measured results with the outcomes obtained through MRA and the ANN to assess their effectiveness in predicting and optimizing surface roughness. The results show that higher accuracy was obtained from the ANN method. Full article

(This article belongs to the Special Issue 3D Printing Technologies and Additive Manufacturing: Recent Advances and Applications)

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11 pages, 2045 KiB

Open AccessArticle

Investigation of the Photothermal Performance of the Composite Scaffold Containing Light-Heat-Sensitive Nanomaterial SiO₂@Fe₃O₄

by Changpeng Shan, Yan Xu and Shengkai Li

Appl. Sci. 2024, 14(11), 4911; https://doi.org/10.3390/app14114911 - 5 Jun 2024

Viewed by 1210

Abstract

The objective of this investigation was to fabricate a photothermally responsive composite bone scaffold aimed at facilitating bone tissue regeneration and remedying bone defects via mild thermal stimulation. The photothermal-sensitive nanomaterial SiO₂ coated Fe₃O₄ (SiO₂@Fe₃O₄), synthesized through the hydrolysis–condensation process of tetraethyl orthosilicate (TEOS), displayed a uniform distribution of SiO₂ coating, effectively preventing the aggregation of Fe₃O₄ particles within the scaffold matrix. The composite scaffold containing 5% mass fraction of photothermal-sensitive nanoparticles exhibited evenly dispersed microstructural porosity, a compressive strength of 5.722 MPa, and a water contact angle of 58.3°, satisfying the mechanical property requisites of cancellous bone while demonstrating notable hydrophilic characteristics. Upon exposure to near-infrared light at ambient temperature, the 5% composite scaffold underwent a temperature elevation of 3–6 °C within 40–45 s, attaining a temperature range (40–43 °C) conducive to fostering osteogenic differentiation. Experimental findings validated that the SiO₂@Fe₃O₄/polyvinyl alcohol (PVA)/hydroxyapatite (HA)/polycaprolactone (PCL)/β-tricalcium phosphate (β-TCP) bone scaffold showcased outstanding mechanical and photothermal attributes, thereby presenting a pioneering avenue for advancing bone tissue cell proliferation and addressing bone defect rehabilitation. Full article

(This article belongs to the Special Issue 3D Printing Technologies and Additive Manufacturing: Recent Advances and Applications)

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