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Advancements in the Characterization and Selection of Materials for Industrial Applications

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

Deadline for manuscript submissions: closed (20 September 2023) | Viewed by 4917

Special Issue Editors


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Guest Editor
Department of Engineering Science, Guglielmo Marconi University, 00193 Rome, Italy
Interests: lean six sigma; design for X; axiomatic design; healthcare system design; design methodologies

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Guest Editor
Department of Industrial, Electronic and Mechanical Engineering, Roma Tre University, 00146 Rome, Italy
Interests: axiomatic design; material selection; superalloys; mechanical design; design theories; design for six sigma; reliability analysis; magneto-rheological fluids
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Engineering Science, Guglielmo Marconi University, 00193 Rome, Italy
Interests: design for Six Sigma; Lean Six Sigma 4.0; axiomatic design; design of experiments
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The progressive development of increasingly high-performance materials, the evolution of production processes, and the problems related to the sustainability of products require a solid experimental and analytical scientific basis in the characterization and selection of materials. In this context, one challenge is the correct evaluation of the resistance characteristics of materials and, more generally, their functional reliability. The complexity of these needs has led to the development of a multi-dimensional approach and tools able to help in the path to identify suitable and optimal solutions.

This Special Issue aims to explore new advancements in materials' mechanical and functional characterization, considering innovative production processes, sustainability and smart functionality. Material selection approaches and their applications are also topics covered.

Submissions to the Special Issue may relate to, but are not limited to, the following topics:

  • Characterization and properties of innovative materials for industrial applications;
  • Materials for additive manufacturing or other innovative production processes;
  • Smart materials development and application;
  • Material for circular design;
  • Material selection approaches and tools

Prof. Dr. Paolo Citti
Prof. Dr. Alessandro Giorgetti
Prof. Dr. Gabriele Arcidiacono
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. 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

  • mechanical characterization
  • functional reliability
  • materials selection
  • MADM
  • additive manufacturing
  • circular design
  • smart materials

Published Papers (3 papers)

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Research

17 pages, 1627 KiB  
Article
A Supervised Machine Learning Model for Regression to Predict Melt Pool Formation and Morphology in Laser Powder Bed Fusion
by Niccolò Baldi, Alessandro Giorgetti, Alessandro Polidoro, Marco Palladino, Iacopo Giovannetti, Gabriele Arcidiacono and Paolo Citti
Appl. Sci. 2024, 14(1), 328; https://doi.org/10.3390/app14010328 - 29 Dec 2023
Viewed by 833
Abstract
In the additive manufacturing laser powder bed fusion (L-PBF) process, the optimization of the print process parameters and the development of conduction zones in the laser power (P) and scanning speed (V) parameter spaces are critical to meeting production quality, productivity, and volume [...] Read more.
In the additive manufacturing laser powder bed fusion (L-PBF) process, the optimization of the print process parameters and the development of conduction zones in the laser power (P) and scanning speed (V) parameter spaces are critical to meeting production quality, productivity, and volume goals. In this paper, we propose the use of a machine learning approach during the process parameter development to predict the melt pool dimensions as a function of the P/V combination. This approach turns out to be useful in speeding up the identification of the printability map of the material and defining the conduction zone during the development phase. Moreover, a machine learning method allows for an accurate investigation of the most promising configurations in the P-V space, facilitating the optimization and identification of the P-V set with the highest productivity. This approach is validated by an experimental campaign carried out on samples of Inconel 718, and the effects of some additional parameters, such as the layer thickness (in the range of 30 to 90 microns) and the preheating temperature of the building platform, are evaluated. More specifically, the experimental data have been used to train supervised machine learning models for regression using the KNIME Analytics Platform (version 4.7.7). An AutoML (node for regression) tool is used to identify the most appropriate model based on the evaluation of R2 and MAE scores. The gradient boosted tree model also performs best compared to Rosenthal’s analytical model. Full article
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13 pages, 6513 KiB  
Article
Effect of Calcium Hydroxide on Compressive Strength and Microstructure of Geopolymer Containing Admixture of Kaolin, Fly Ash, and Red Mud
by Tien Dung Cong, Thao Phuong, Minh Thanh Vu and Thi Huong Nguyen
Appl. Sci. 2023, 13(8), 5034; https://doi.org/10.3390/app13085034 - 17 Apr 2023
Cited by 2 | Viewed by 1795
Abstract
The current study aims to investigate the effect of calcium hydroxide on the geopolymer derived from an admixture of the natural mineral (kaolin) and industrial by-products (fly ash, red mud). The compressive strength and microstructure were studied using compressive strength tests, X-ray diffraction, [...] Read more.
The current study aims to investigate the effect of calcium hydroxide on the geopolymer derived from an admixture of the natural mineral (kaolin) and industrial by-products (fly ash, red mud). The compressive strength and microstructure were studied using compressive strength tests, X-ray diffraction, infrared spectroscopy, BET method, and scanning electron microscopy. For the investigated NaOH activator concentrations ranging from 4 M to 10 M, the compressive strength of the geopolymer first increases, then decreases with the increase of calcium hydroxide content. The optimal content of calcium hydroxide, which can give the highest compressive strength of the geopolymer prepared, is about 13% wt. of solid raw materials. The geopolymer materials produced at the 8 M NaOH activator have higher compressive strength than those prepared at 4 M, 6 M, and 10 M NaOH. There is a coexistence of geopolymerization gel and C-S-H/C-A-S-H gel in the materials prepared. Both porosity and the formation of N-A-S-H/C-S-H/C-A-S-H during the polymerization process are important for the mechanical properties of materials. Full article
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21 pages, 7140 KiB  
Article
Effect of Ti-B Grain Refiners on Wear and Corrosion of the A332 Alloy with Sr Modification
by Bruno E. Arendarchuck, Andre R. Mayer, Willian R. de Oliveira, Anderson G. M. Pukasiewicz, Luciano A. Lourençato, Hipolito D. C. Fals and Eduardo Martínez-Cámara
Appl. Sci. 2023, 13(1), 430; https://doi.org/10.3390/app13010430 - 29 Dec 2022
Cited by 4 | Viewed by 1571
Abstract
Grain refiners play a critical role in changing characteristics and properties of casting aluminum alloys. The Al-Si alloy (A332) is one of the most popular hypoeutectic alloys with a large range of industrial applications. It has a varied phase and morphology; however, it [...] Read more.
Grain refiners play a critical role in changing characteristics and properties of casting aluminum alloys. The Al-Si alloy (A332) is one of the most popular hypoeutectic alloys with a large range of industrial applications. It has a varied phase and morphology; however, it features problems with acicular-shaped eutectic phase, and generally exhibits dendritic cast grain type. To change this situation, the Sr element acts as a modifier of eutectic, which, along with a grain refiner may increase mechanical properties. In this work, two different grain refiners (Al5Ti1B, Al5Ti2B) were applied to the A332 alloy modified with Sr, and analyzed in relation to grain size, hardness, corrosion resistance, and wear behavior. Corrosion tests in 3.5 wt.% NaCl solution, nanoindentations, and Heyn’s method to analyze grain size and microhardness as optical and SEM images were made to examine the changes caused by grain refiners. A reduction in grain size was achieved, and the influence in size and hardness of the β-Fe phase was verified in the wear and corrosion analyses. Full article
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