Advances in Additive Manufacturing Technology

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

Deadline for manuscript submissions: closed (15 March 2022) | Viewed by 33299

Special Issue Editor

Faculty of Engineering Technology (ET), University of Twente (UT), P.O. Box 217, 7500 AE Enschede, The Netherlands
Interests: advanced manufacturing; 3D/4D printing; functional materials; modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Additive manufacturing (AM) has arisen as a flexible innovation platform and turned into an influential strategy for future modern manufacturing systems. Its applications have extended rapidly from visual prototypes in commercial industries (e.g., in aerospace, automotive, and biomedical fields) to electronic gadgets, tissue engineering, and high-performance metamaterials. Various methods of AM have been developed to meet the demands for the fabrication of complex structures with high resolution. However, there are still some challenges that could slow down the opportunities of AM. Therefore, demanding a clear understanding of causes and finding the possible solutions can accelerate the efficiency and productivity of AM technology in the future. In this respect, advanced characterization techniques, on the one hand, and simulation/modeling analysis methods, on the other, would be very helpful tools to identify the source of variations.

We invite the submission of full-length papers of original research contributions, review papers, and communications that are of high quality, impact, and novelty as well as being interesting for wide audiences of scientific and technological communities. Potential topics for this Special Issue include, but are not limited to, the following:

  • Novel AM approaches and technologies to improve quality and efficiency;
  • Recent developments in materials and their effects on AM process and part properties;
  • Theoretical and experimental research, knowledge, and new ideas in design for AM;
  • Research and development in 3D printing of advanced smart and multifunctional materials;
  • Mathematical modeling or numerical simulation to predict the optimal operational parameters for various AM techniques;
  • The latest development of test methods including mechanical and microstructural analysis of AM fabricated parts.

The journal Applied Sciences, published by MDPI, is a peer-reviewed, open access, and online-only journal.

Dr. Mehrshad Mehrpouya
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
  • 3D/4D printing
  • metal and polymer 3D printing
  • design for additive manufacturing
  • bio-inspired design
  • simulation and modeling

Published Papers (7 papers)

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Research

14 pages, 4413 KiB  
Article
Investigation of the Effect of Laser Fluence on Microstructure and Martensitic Transformation for Realizing Functionally Graded NiTi Shape Memory Alloy via Laser Powder Bed Fusion
by Paola Bassani, Jacopo Fiocchi, Ausonio Tuissi and Carlo Alberto Biffi
Appl. Sci. 2023, 13(2), 882; https://doi.org/10.3390/app13020882 - 09 Jan 2023
Cited by 2 | Viewed by 1411
Abstract
Nowadays, additive manufacturing (AM) of NiTi shape memory alloy is a challenging topic for the realization of 3D functional parts. Particularly, Laser Powder Bed Fusion (LPBF) of NiTi powder is one of the most challenging processes belonging to AM, thanks to its best [...] Read more.
Nowadays, additive manufacturing (AM) of NiTi shape memory alloy is a challenging topic for the realization of 3D functional parts. Particularly, Laser Powder Bed Fusion (LPBF) of NiTi powder is one of the most challenging processes belonging to AM, thanks to its best performances in terms of productivity and precision of geometrical complexity. The control of the functional performances in NiTi components requires a strong interaction between technological and metallurgical approaches. In fact, a strong correlation among the process conditions, the microstructure, and the final functional performances, beyond the defects associated with the process are needed to be understood and analyzed. In the present work, the correlation between the feasibility map of processability and the obtained microstructure, which can be tailored according to the use of different energy density values, of Ni-rich NiTi powder processed with LPBF is investigated. In detail, discrete energy density values, in the range 60–300 J/mm3, were correlated to microstructure, Ni:Ti ratio, and transformation temperatures of the martensitic transformation, analyzed with SEM, EBSD, EDX, and DSC characterizations, respectively. An increase in laser energy density was found to promote Ni evaporation, which induced a change of the microstructure from austenite to martensite at room temperature. A consequent shift of the transformation temperatures to higher values and a change in microstructural texture was achieved. These achievements can support the identification of the feasibility range for manufacturing functionally graded NiTi SMA, requiring tailored functional properties located in selected positions in the 3D parts. Full article
(This article belongs to the Special Issue Advances in Additive Manufacturing Technology)
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27 pages, 9182 KiB  
Article
A Comparative Study of Machine Learning Methods for Computational Modeling of the Selective Laser Melting Additive Manufacturing Process
by Shubham Chaudhry and Azzeddine Soulaïmani
Appl. Sci. 2022, 12(5), 2324; https://doi.org/10.3390/app12052324 - 23 Feb 2022
Cited by 5 | Viewed by 2143
Abstract
Selective laser melting (SLM) is a metal-based additive manufacturing (AM) technique. Many factors contribute to the output quality of SLM, particularly the machine and material parameters. Analysis of the parameters’ effects is critical, but using traditional experimental and numerical simulation can be expensive [...] Read more.
Selective laser melting (SLM) is a metal-based additive manufacturing (AM) technique. Many factors contribute to the output quality of SLM, particularly the machine and material parameters. Analysis of the parameters’ effects is critical, but using traditional experimental and numerical simulation can be expensive and time-consuming. This paper provides a framework to analyze the sensitivity and uncertainty in SLM input and output parameters, which can then be used to find the optimum parameters. The proposed data-driven approach combines machine learning algorithms with high-fidelity numerical simulations to study the SLM process more efficiently. We have considered laser speed, hatch spacing, layer thickness, Young modulus, and Poisson ratio as input variables, while the output variables are numerical predicted normal strains in the building part. A surrogate model was constructed with a deep neural network (DNN) or polynomial chaos expansion (PCE) to generate a response surface between the SLM output and the input variables. The surrogate model and the sensitivity analysis found that all five parameters were important in the process. The surrogate model was combined with non-intrusive optimization algorithms such as genetic algorithms (GA), differential evolution (DE), and particle swarm optimization (PSO) to perform an inverse analysis and find the optimal parameters for the SLM process. Of the three algorithms, the PSO performed well, and the DNN model was found to be the most efficient surrogate model compared to the PCE. Full article
(This article belongs to the Special Issue Advances in Additive Manufacturing Technology)
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23 pages, 34124 KiB  
Article
Three-Dimensional Modeling and 3D Printing of Biocompatible Orthodontic Power-Arm Design with Clinical Application
by Andrej Thurzo, Filip Kočiš, Bohuslav Novák, Ladislav Czako and Ivan Varga
Appl. Sci. 2021, 11(20), 9693; https://doi.org/10.3390/app11209693 - 18 Oct 2021
Cited by 23 | Viewed by 3812
Abstract
Three-dimensional (3D) printing with biocompatible resins offers new competition to its opposition—subtractive manufacturing, which currently dominates in dentistry. Removing dental material layer-by-layer with lathes, mills or grinders faces its limits when it comes to the fabrication of detailed complex structures. The aim of [...] Read more.
Three-dimensional (3D) printing with biocompatible resins offers new competition to its opposition—subtractive manufacturing, which currently dominates in dentistry. Removing dental material layer-by-layer with lathes, mills or grinders faces its limits when it comes to the fabrication of detailed complex structures. The aim of this original research was to design, materialize and clinically evaluate a functional and resilient shape of the orthodontic power-arm by means of biocompatible 3D printing. To improve power-arm resiliency, we have employed finite element modelling and analyzed stress distribution to improve the original design of the power-arm. After 3D printing, we have also evaluated both designs clinically. This multidisciplinary approach is described in this paper as a feasible workflow that might inspire application other individualized biomechanical appliances in orthodontics. The design is a biocompatible power-arm, a miniature device bonded to a tooth surface, translating significant bio-mechanical force vectors to move a tooth in the bone. Its design must be also resilient and fully individualized to patient oral anatomy. Clinical evaluation of the debonding rate in 50 randomized clinical applications for each power-arm-variant showed significantly less debonding incidents in the improved power-arm design (two failures = 4%) than in the original variant (nine failures = 18%). Full article
(This article belongs to the Special Issue Advances in Additive Manufacturing Technology)
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14 pages, 2448 KiB  
Article
Recycled Tire Rubber in Additive Manufacturing: Selective Laser Sintering for Polymer-Ground Rubber Composites
by Antoniya Toncheva, Loïc Brison, Philippe Dubois and Fouad Laoutid
Appl. Sci. 2021, 11(18), 8778; https://doi.org/10.3390/app11188778 - 21 Sep 2021
Cited by 17 | Viewed by 3724
Abstract
Natural and synthetic rubber is gaining increased interest in various industrial applications and daily life sectors (automotive industry, acoustic and electrical isolators, adhesives, impermeable surfaces, and others) due to its remarkable physicomechanical properties, excellent durability, and abrasive resistance. These great characteristics are accompanied [...] Read more.
Natural and synthetic rubber is gaining increased interest in various industrial applications and daily life sectors (automotive industry, acoustic and electrical isolators, adhesives, impermeable surfaces, and others) due to its remarkable physicomechanical properties, excellent durability, and abrasive resistance. These great characteristics are accompanied by some recycling difficulties of the final products, particularly originated from the tire waste rubber industry. In this study, recycled tire rubber was incorporated in polymer matrices using selective laser sintering as 3D printing technology. Two polymers were used-polyamide and thermoplastic polyurethane, for their rigid and elastomeric properties, respectively. Polymer composites containing various tire powder amounts, up to 40 wt.%, were prepared by physical blending. The final materials’ morphological characteristics, mechanical properties, and thermal stability were evaluated. The proposed ambitious additive manufacturing approach looked over also some of the major aspects to be considered during the 3D printing procedure. In addition, examples of printed prototypes with potential applications were also proposed revealing the potential of the recycled tire rubber-loaded composites. Full article
(This article belongs to the Special Issue Advances in Additive Manufacturing Technology)
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21 pages, 37280 KiB  
Article
A Novel Slicing Strategy to Print Overhangs without Support Material
by Michael Wüthrich, Maurus Gubser, Wilfried J. Elspass and Christian Jaeger
Appl. Sci. 2021, 11(18), 8760; https://doi.org/10.3390/app11188760 - 20 Sep 2021
Cited by 11 | Viewed by 10021
Abstract
Fused deposition modeling (FDM) 3D printers commonly need support material to print overhangs. A previously developed 4-axis printing process based on an orthogonal kinematic, an additional rotational axis around the z-axis and a 45° tilted nozzle can print overhangs up to 100° without [...] Read more.
Fused deposition modeling (FDM) 3D printers commonly need support material to print overhangs. A previously developed 4-axis printing process based on an orthogonal kinematic, an additional rotational axis around the z-axis and a 45° tilted nozzle can print overhangs up to 100° without support material. With this approach, the layers are in a conical shape and no longer parallel to the printing plane; therefore, a new slicer strategy is necessary to generate the paths. This paper describes a slicing algorithm compatible with this 4-axis printing kinematics. The presented slicing strategy is a combination of a geometrical transformation with a conventional slicing software and has three basic steps: Transformation of the geometry in the .STL file, path generation with a conventional slicer and back transformation of the G-code. A comparison of conventionally manufactured parts and parts produced with the new process shows the feasibility and initial results in terms of surface quality and dimensional accuracy. Full article
(This article belongs to the Special Issue Advances in Additive Manufacturing Technology)
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14 pages, 3869 KiB  
Article
A Prediction Model for Additive Manufacturing of Inconel 718 Superalloy
by Bharath Bhushan Ravichander, Atabak Rahimzadeh, Behzad Farhang, Narges Shayesteh Moghaddam, Amirhesam Amerinatanzi and Mehrshad Mehrpouya
Appl. Sci. 2021, 11(17), 8010; https://doi.org/10.3390/app11178010 - 30 Aug 2021
Cited by 10 | Viewed by 4497
Abstract
Inconel 718 is a nickel-based superalloy and an excellent candidate for the aerospace, oil, and gas industries due to its high strength and corrosion resistance properties. The machining of IN718 is very challenging; therefore, the application of additive manufacturing (AM) technology is an [...] Read more.
Inconel 718 is a nickel-based superalloy and an excellent candidate for the aerospace, oil, and gas industries due to its high strength and corrosion resistance properties. The machining of IN718 is very challenging; therefore, the application of additive manufacturing (AM) technology is an effective approach to overcoming these difficulties and for the fabrication of complex geometries that cannot be manufactured by the traditional techniques. Selective laser melting (SLM), which is a laser powder bed fusion method, can be applied for the fabrication of IN718 samples with high accuracy. However, the process parameters have a high impact on the properties of the manufactured samples. In this study, a prediction model is developed for obtaining the optimal process parameters, including laser power, hatch spacing, and scanning speed, in the SLM process of the IN718 alloy. For this purpose, artificial neural network (ANN) modeling with various algorithms is employed to estimate the process outputs, namely, sample height and surface hardness. The modeling results fit perfectly with the experimental output, and this consequently proves the benefit of ANN modeling for predicting the optimal process parameters. Full article
(This article belongs to the Special Issue Advances in Additive Manufacturing Technology)
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20 pages, 8838 KiB  
Article
Multi-Resolution SPH Simulation of a Laser Powder Bed Fusion Additive Manufacturing Process
by Mohamadreza Afrasiabi, Christof Lüthi, Markus Bambach and Konrad Wegener
Appl. Sci. 2021, 11(7), 2962; https://doi.org/10.3390/app11072962 - 26 Mar 2021
Cited by 38 | Viewed by 6093
Abstract
This paper presents an efficient mesoscale simulation of a Laser Powder Bed Fusion (LPBF) process using the Smoothed Particle Hydrodynamics (SPH) method. The efficiency lies in reducing the computational effort via spatial adaptivity, for which a dynamic particle refinement pattern with an optimized [...] Read more.
This paper presents an efficient mesoscale simulation of a Laser Powder Bed Fusion (LPBF) process using the Smoothed Particle Hydrodynamics (SPH) method. The efficiency lies in reducing the computational effort via spatial adaptivity, for which a dynamic particle refinement pattern with an optimized neighbor-search algorithm is used. The melt pool dynamics is modeled by resolving the thermal, mechanical, and material fields in a single laser track application. After validating the solver by two benchmark tests where analytical and experimental data are available, we simulate a single-track LPBF process by adopting SPH in multi resolutions. The LPBF simulation results show that the proposed adaptive refinement with and without an optimized neighbor-search approach saves almost 50% and 35% of the SPH calculation time, respectively. This achievement enables several opportunities for parametric studies and running high-resolution models with less computational effort. Full article
(This article belongs to the Special Issue Advances in Additive Manufacturing Technology)
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