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Special Issue "Recent Advances in Metal Powder Based Additive Manufacturing"

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Special Issue Editors

Prof. Dr. Hong Wu
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Guest Editor

State Key Laboratory of Powder Metallurgy, Central South University, Changsha, China
Interests: additive manufacturing; biomedical materials
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Dr. Yingtao Tian

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Guest Editor

Department of Engineering, Lancaster University, Lancaster, UK
Interests: additive manufacturing; digital manufacturing; materials joining; advanced laser processing
Special Issues, Collections and Topics in MDPI journals

Dr. Alberto Orozco Caballero

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Guest Editor

Department of Mechanical Engineering, Chemistry and Industrial Design, Universidad Politécnica de Madrid, Ronda de Valencia, 3, 28012 Madrid, Spain
Interests: deformation micromechanics; creep; additive manufacturing; light alloys; materials characterisation

Special Issue Information

Dear Colleagues,

Metal-powder-based additive manufacturing (AM) consists of a number of emerging technologies, such as powder bed fusion or the direct deposition of powders by lasers or electron beams. These enable us to produce load-bearing components or structures with extremely complex geometries, as well as providing unique opportunities for the tailored microstructure and design of new materials. In contrast to conventional manufacturing technologies, such as casting, forging and hot rolling, AM offers much greater degrees of freedom in manufacturing and can also significantly reduce the production steps and material waste. However, conventional materials may not be suitable for AM processes, and the lack of processable materials has hindered its wider adoption. Understanding the evolution of microstructure and the resulting material’s behavior is key for developing novel materials for AM processes.

This Special Issue welcomes original research and high-quality comprehensive reviews on recent advances in metal-powder-based additive manufacturing. The focus of this topic includes the design of new alloy compositions, developing the understanding of microstructure evolution and the impacts on mechanical properties. Material systems of interest include, but are not limited to, structural materials, different types of steels, aluminium, titanium, nickel, copper, cobalt-based alloys, refractory metals, shape-memory alloys, high-entropy alloys, and bulk metallic glasses.

Contributing papers are solicited in the following fields:

Novel alloy design tailored for AM;
Novel metal powder AM processes;
Multi-materials processing in AM;
Microstructural evolution during the AM processes;
Microstructure and property relationships of AM components;
Microstructural response of AM components to post-processing conditions.

Prof. Dr. Hong Wu
Dr. Yingtao Tian
Dr. Alberto Orozco Caballero
Guest Editors

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. Materials 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 2300 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

metal powder materials
powder bed fusion
direct energy deposition
binder jetting
microstructures and properties
post-processing of AM components

Published Papers (2 papers)

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Research

Open AccessArticle

Structure and Mechanical Properties of Cu–Al–Mn Alloys Fabricated by Electron Beam Additive Manufacturing

Evgeny Moskvichev

Nikolay Shamarin

and

Alexey Smolin

Materials 2023, 16(1), 123; https://doi.org/10.3390/ma16010123 - 22 Dec 2022

Viewed by 625

Abstract

In this work, the method of electron beam additive manufacturing (EBAM) was used to fabricate a Cu-based alloy possessing a shape memory effect. Electron beam additive technology is especially relevant for copper and its alloys since the process is carried out in a vacuum, which makes it possible to circumvent oxidation. The main purpose of the study was to establish the influence of the printing parameters on the structure of the obtained products, their phase composition, mechanical properties, dry friction behavior, and the structure-phase gradient that formed in Cu–Al–Mn alloy samples during electron beam layer-by-layer printing. The results of the study allowed us to reveal that the structure-phase composition, the mechanical properties, and the tribological performance of the fabricated material are mainly affected by the magnitude of heat input during electron beam additive printing of Cu–Al–Mn alloy. High heat input values led to the formation of the β1′ + α decomposed structure. Low heat input values enabled the suppression of decomposition and the formation of an ordered 1 structure. The microhardness values were distributed on a gradient from 2.0 to 2.75 GPa. Fabricated samples demonstrated different behaviors in friction and wear depending on their composition and structure, with the value of the friction coefficient lying in the range between 0.1 and 0.175. Full article

(This article belongs to the Special Issue Recent Advances in Metal Powder Based Additive Manufacturing)

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Open AccessEditor’s ChoiceArticle

Programmable Density of Laser Additive Manufactured Parts by Considering an Inverse Problem

and

Materials 2022, 15(20), 7090; https://doi.org/10.3390/ma15207090 - 12 Oct 2022

Viewed by 678

Abstract

In this Article, the targeted adjustment of the relative density of laser additive manufactured components made of AlSi10Mg is considered. The interest in demand-oriented process parameters is steadily increasing. Thus, shorter process times and lower unit costs can be achieved with decreasing component densities. Especially when hot isostatic pressing is considered as a post-processing step. In order to be able to generate process parameters automatically, a model hypothesis is learned via artificial neural networks (ANN) for a density range from 70% to almost 100%, based on a synthetic dataset with equally distributed process parameters and a statistical test series with 256 full factorial combined instances. This allows the achievable relative density to be predicted from given process parameters. Based on the best model, a database approach and supervised training of concatenated ANNs are developed to solve the inverse parameter prediction problem for a target density. In this way, it is possible to generate a parameter prediction model for the high-dimensional result space through constraints that are shown with synthetic test data sets. The presented concatenated ANN model is able to reproduce the origin distribution. The relative density of synthetic data can be predicted with an R²-value of 0.98. The mean build rate can be increased by 12% with the formulation of a hint during the backward model training. The application of the experimental data shows increased fuzziness related to the big data gaps and a small number of instances. For practical use, this algorithm could be trained on increased data sets and can be expanded by properties such as surface quality, residual stress, or mechanical strength. With knowledge of the necessary (mechanical) properties of the components, the model can be used to generate appropriate process parameters. This way, the processing time and the amount of scrap parts can be reduced. Full article

(This article belongs to the Special Issue Recent Advances in Metal Powder Based Additive Manufacturing)

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Special Issue "Recent Advances in Metal Powder Based Additive Manufacturing"

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