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Coatings
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Novel Materials and Processes for Metal Additive Manufacturing
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Novel Materials and Processes for Metal Additive Manufacturing

A special issue of Coatings (ISSN 2079-6412).

Deadline for manuscript submissions: closed (15 November 2020) | Viewed by 8575

Special Issue Editor

Dr. Atieh Moridi

E-Mail Website
Guest Editor
Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USA
Interests: additive manufacturing; material design; microstructure, mechanical properties

Special Issue Information

Dear Colleagues,

Additive manufacturing (AM) is a new paradigm for the design and production of high-performance components for aerospace, medical, energy, and automotive applications. The goal of this Special Issue is to highlight research on two frontiers. The first one concerns the materials used in additive manufacturing. Conventional alloys are not optimized for AM process and they often lead to inferior properties of cast or wrought alloys. However, the intrinsic properties of AM (i.e., rapid solidification, melt pool dynamic, cyclic heat treatment) can provide a unique opportunity to design novel materials. The second frontier regards novel/modified processes that address some of the limitations/challenges associated with melt-based metal additive manufacturing. Strategies to mitigate residual stress, hinder defect formation, and reduce anisotropy in printed parts are especially important to transform the technology.

In particular, the topics of interest include, but are not limited to:

  • Novel additive manufacturing processes, including solid-state AM (e.g., cold spray, friction stir welding), hybrid AM processes, etc.
  • The relationship between AM process parameters, microstructure, and the resulting properties
  • Alloy design for additive manufacturing or design of new alloys with additive manufacturing

Dr. Atieh Moridi
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. Coatings is an international peer-reviewed open access monthly 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.

Published Papers (2 papers)

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Research

29 pages, 5344 KiB  
Article
Modeling and Optimization Approaches of Laser-Based Powder-Bed Fusion Process for Ti-6Al-4V Alloy
by Behzad Fotovvati, Madhusudhanan Balasubramanian and Ebrahim Asadi
Coatings 2020, 10(11), 1104; https://doi.org/10.3390/coatings10111104 - 18 Nov 2020
Cited by 38 | Viewed by 4292
Abstract
Laser-based powder-bed fusion (L-PBF) is a widely used additive manufacturing technology that contains several variables (processing parameters), which makes it challenging to correlate them with the desired properties (responses) when optimizing the responses. In this study, the influence of the five most influential [...] Read more.
Laser-based powder-bed fusion (L-PBF) is a widely used additive manufacturing technology that contains several variables (processing parameters), which makes it challenging to correlate them with the desired properties (responses) when optimizing the responses. In this study, the influence of the five most influential L-PBF processing parameters of Ti-6Al-4V alloy—laser power, scanning speed, hatch spacing, layer thickness, and stripe width—on the relative density, microhardness, and various line and surface roughness parameters for the top, upskin, and downskin surfaces are thoroughly investigated. Two design of experiment (DoE) methods, including Taguchi L25 orthogonal arrays and fractional factorial DoE for the response surface method (RSM), are employed to account for the five L-PBF processing parameters at five levels each. The significance and contribution of the individual processing parameters on each response are analyzed using the Taguchi method. Then, the simultaneous contribution of two processing parameters on various responses is presented using RSM quadratic modeling. A multi-objective RSM model is developed to optimize the L-PBF processing parameters considering all the responses with equal weights. Furthermore, an artificial neural network (ANN) model is designed and trained based on the samples used for the Taguchi method and validated based on the samples used for the RSM. The Taguchi, RSM, and ANN models are used to predict the responses of unseen data. The results show that with the same amount of available experimental data, the proposed ANN model can most accurately predict the response of various properties of L-PBF components. Full article
(This article belongs to the Special Issue Novel Materials and Processes for Metal Additive Manufacturing)
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22 pages, 12057 KiB  
Article
Material Allowable Generation and AM Process Parameters Effect on Porosity
by Frank Abdi, Parviz Yavari, Vasyl Harik and Cody Godines
Coatings 2020, 10(7), 625; https://doi.org/10.3390/coatings10070625 - 30 Jun 2020
Cited by 4 | Viewed by 3260
Abstract
Additive manufacturing (AM) process methods such as powder bed fusion (LPBF) of metal powder layers can produce layered material systems with designed microstructures, which may exhibit scatter in mechanical properties (e.g., lower yield and lower failure strain), corrosion due to porosity and print [...] Read more.
Additive manufacturing (AM) process methods such as powder bed fusion (LPBF) of metal powder layers can produce layered material systems with designed microstructures, which may exhibit scatter in mechanical properties (e.g., lower yield and lower failure strain), corrosion due to porosity and print anomalies. This study shows the development of AM process simulation to predict As-built material characteristic and their scatter comparing with experimental test data. ICME (Integrated Computational Materials Engineering) was used to simulate yield, ultimate, strain, and reduction of the area of sample AM. The method was extended to predict oxidation and damage of as-built parts. The samples were fabricated horizontally and vertically in multiple and scatter directions to find the effect on the mechanical properties such as ultimate tensile strength (UTS) and yield strength (YS). The probabilistic sensitivities show that in order for the next-generation technology to improve the strength of 3D printed materials, they must control the void volume fraction (trapped gas) and orientation of voids. The studied 3D print modality processes: (a) LPBF of AlSi10Mg, and (b) Electron Beam (EBM) of Ti-6Al-4V materials are shown to be over 99.99% reliable. The statistics of 3D printed Ti-6Al-4V have been observed for room and high temperature (RT/HT). The ICME Material Characterization and Qualification (MCQ) software material model prediction capabilities were used to predict (a) Material Allowable, a variation in Stress Strain Curves Characteristic Points and Residual Stress due to air particle (void/defect) shape and size and orientation. The probabilistic simulation computes Cumulative Distribution Function (CDF) and probabilistic sensitivities for YS, UTS, and %Elongation as well as A and B basis allowable of the As-Built 3D printed material and; and (b) Fracture Control Plan fracture toughness determination, and fatigue crack growth vs. stress intensity. Full article
(This article belongs to the Special Issue Novel Materials and Processes for Metal Additive Manufacturing)
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