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Advanced Materials and Manufacturing Processes
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Advanced Materials and Manufacturing Processes

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: 20 September 2024 | Viewed by 1088

Special Issue Editors

Prof. Dr. Sung Yi

E-Mail Website
Guest Editor
Mechanical and Materials Engineering Department, Portland State University, Post Box 751, Portland, OR 97207-0751, USA
Interests: nanomaterials; biomaterials; semiconductor materials and devices
Prof. Dr. Soong-Keun Hyun

E-Mail Website
Guest Editor
Department of Materials Science and Engineering, Inha University, Inharo 100, Michuholgu, Incheon, South Korea
Interests: porous materials; light metals; micro-joining

Special Issue Information

Dear Colleagues,

Advanced materials and manufacturing have become pivotal in revolutionizing product fabrication, with a primary focus on optimizing efficiency, precision, and the ability to produce complex, high-quality, and tailor-made components or products.

The influence of advanced materials and manufacturing is profoundly transforming a spectrum of emerging industries, each characterized by a commitment to pioneering solutions, operational efficiency, and the creation of exceptional, tailor-made products. Within these burgeoning sectors, the synergy of state-of-the-art materials and manufacturing methodologies is propelling innovation, facilitating the production of intricate, high-quality, and customized items. These advancements not only refine production processes but also expand the horizons of possibilities in these dynamic and promising fields.

This Special Issue focuses on various aspects of materials science related to advanced manufacturing processes and applications. Topics can include, but are not limited to, Additive Manufacturing (Metal 3D Printing, Bioprinting, and Multi-material Printing), Advanced Materials, Artificial Intelligence (AI) and Machine Learning, Nanomanufacturing, Smart Manufacturing, Sustainable Manufacturing, Precision Machining, and so on.

It is our pleasure to invite you to submit your work to this Special Issue. Research papers, reviews, and communications are welcome.

Prof. Dr. Sung Yi
Prof. Dr. Soong-Keun Hyun
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 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.

Keywords

  • additive manufacturing (metal 3D printing, bioprinting, and multi-material printing)
  • advanced materials
  • artificial intelligence (AI) and machine learning
  • nanomanufacturing
  • smart manufacturing
  • sustainable manufacturing
  • precision machining

Published Papers (2 papers)

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Research

37 pages, 21095 KiB  
Article
Artificial Neural Networks and Experimental Analysis of the Resistance Spot Welding Parameters Effect on the Welded Joint Quality of AISI 304
by Marwan T. Mezher, Alejandro Pereira, Tomasz Trzepieciński and Jorge Acevedo
Materials 2024, 17(9), 2167; https://doi.org/10.3390/ma17092167 - 6 May 2024
Viewed by 381
Abstract
The automobile industry relies primarily on spot welding operations, particularly resistance spot welding (RSW). The performance and durability of the resistance spot-welded joints are significantly impacted by the welding quality outputs, such as the shear force, nugget diameter, failure mode, and the hardness [...] Read more.
The automobile industry relies primarily on spot welding operations, particularly resistance spot welding (RSW). The performance and durability of the resistance spot-welded joints are significantly impacted by the welding quality outputs, such as the shear force, nugget diameter, failure mode, and the hardness of the welded joints. In light of this, the present study sought to determine how the aforementioned welding quality outputs of 0.5 and 1 mm thick austenitic stainless steel AISI 304 were affected by RSW parameters, such as welding current, welding time, pressure, holding time, squeezing time, and pulse welding. In order to guarantee precise evaluation and experimental analysis, it is essential that they are supported by a numerical model using an intelligent model. The primary objective of this research is to develop and enhance an intelligent model employing artificial neural network (ANN) models. This model aims to provide deeper knowledge of how the RSW parameters affect the quality of optimum joint behavior. The proposed neural network (NN) models were executed using different ANN structures with various training and transfer functions based on the feedforward backpropagation approach to find the optimal model. The performance of the ANN models was evaluated in accordance with validation metrics, like the mean squared error (MSE) and correlation coefficient (R2). Assessing the experimental findings revealed the maximum shear force and nugget diameter emerged to be 8.6 kN and 5.4 mm for the case of 1–1 mm, 3.298 kN and 4.1 mm for the case of 0.5–0.5 mm, and 4.031 kN and 4.9 mm for the case of 0.5–1 mm. Based on the results of the Pareto charts generated by the Minitab program, the most important parameter for the 1–1 mm case was the welding current; for the 0.5–0.5 mm case, it was pulse welding; and for the 0.5–1 mm case, it was holding time. When looking at the hardness results, it is clear that the nugget zone is much higher than the heat-affected zone (HZ) and base metal (BM) in all three cases. The ANN models showed that the one-output shear force model gave the best prediction, relating to the highest R and the lowest MSE compared to the one-output nugget diameter model and two-output structure. However, the Levenberg–Marquardt backpropagation (Trainlm) training function with the log sigmoid transfer function recorded the best prediction results of both ANN structures. Full article
(This article belongs to the Special Issue Advanced Materials and Manufacturing Processes)
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16 pages, 5411 KiB  
Article
An Investigation of the Efficient–Precise Continuous Electrochemical Grinding Process of Ti–6Al–4V
by Guangbin Yang, Pingmei Ming, Shen Niu, Ge Qin, Huan Liu, Dongdong Li and Anchao Zhang
Materials 2024, 17(8), 1729; https://doi.org/10.3390/ma17081729 - 10 Apr 2024
Viewed by 517
Abstract
Titanium alloys have many excellent characteristics, and they are widely used in aerospace, biomedicine, and precision engineering. Meanwhile, titanium alloys are difficult to machine and passivate readily. Electrochemical grinding (ECG) is an ideal technology for the efficient–precise machining of titanium alloys. In the [...] Read more.
Titanium alloys have many excellent characteristics, and they are widely used in aerospace, biomedicine, and precision engineering. Meanwhile, titanium alloys are difficult to machine and passivate readily. Electrochemical grinding (ECG) is an ideal technology for the efficient–precise machining of titanium alloys. In the ECG process of titanium alloys, the common approach of applying high voltage and active electrolytes to achieve high efficiency of material removal will lead to serious stray corrosion, and the time utilized for the subsequent finishing will be extended greatly. Therefore, the application of ECG in the field of high efficiency and precision machining of titanium alloys is limited. In order to address the aforementioned issues, the present study proposed an efficient–precise continuous ECG (E-P-C-ECG) process for Ti–6Al–4V applying high-pulsed voltage with an optimized duty cycle and low DC voltage in the efficient ECG stage and precise ECG stage, respectively, without changing the grinding wheel. According to the result of the passivation properties tests, the ideal electrolyte was selected. Optimization of the process parameters was implemented experimentally to improve the processing efficiency and precision of ECG of Ti–6Al–4V. Utilizing the process advantages of the proposed process, a thin-walled structure of Ti–6Al–4V was obtained with high efficiency and precision. Compared to the conventional mechanical grinding process, the compressive residual stress of the machined surface and the processing time were reduced by 90.5% and 63.3% respectively, and both the surface roughness and tool wear were obviously improved. Full article
(This article belongs to the Special Issue Advanced Materials and Manufacturing Processes)
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