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Microstructural and Mechanical Properties of Metallic Materials, Volume Ⅲ
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Microstructural and Mechanical Properties of Metallic Materials, Volume Ⅲ

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 4065

Special Issue Editor

Dr. Young-Min Kim

E-Mail Website
Guest Editor
Advanced Joining & Additive Manufacturing R&D Department, Korea Institute of Industrial Technology (KITECH), Incheon 21999, Republic of Korea
Interests: alloy design; thermodynamic modeling; welding metallurgy; cladding; high-entropy alloy; resistance spot welding
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Various metallic materials are applied to industrial fields and real life. The mechanical properties or behaviors of the material are often governed by their microstructural characteristics. It is also possible to improve the mechanical properties of the material through microstructural evolution using various methods, such as heat treatment, surface treatment, and plastic deformation. Therefore, analyzing the microstructural properties of metallic materials helps us to understand the mechanisms of mechanical behavior of the material and to optimize the manufacturing process for the material. This Special Issue covers all aspects of the microstructure and the mechanical properties of metallic materials, ranging from conventional ferrous and nonferrous alloys subjected to different processing methods. Studies focusing on the theoretical simulation and experimental analysis of the microstructural and mechanical behaviors of metallic materials are also welcome.

Dr. Young-Min Kim
Guest Editor

Manuscript Submission Information

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Keywords

  • metallic materials
  • microstructure
  • microstructure
  • deformation
  • annealing
  • strengthening mechanisms
  • material characterization
  • manufacturing process

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Published Papers (3 papers)

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Research

18 pages, 4418 KiB  
Article
Artificial Neural Network-Based Modelling for Yield Strength Prediction of Austenitic Stainless-Steel Welds
by Sukil Park, Cheolhee Kim and Namhyun Kang
Appl. Sci. 2024, 14(10), 4224; https://doi.org/10.3390/app14104224 - 16 May 2024
Cited by 1 | Viewed by 888
Abstract
This study aimed to develop an artificial neural network (ANN) model for predicting the yield strength of a weld metal composed of austenitic stainless steel and compare its performance with that of conventional multiple regression and machine learning models. The input parameters included [...] Read more.
This study aimed to develop an artificial neural network (ANN) model for predicting the yield strength of a weld metal composed of austenitic stainless steel and compare its performance with that of conventional multiple regression and machine learning models. The input parameters included the chemical composition of the nine effective elements (C, Si, Mn, P, S, Ni, Cr, Mo, and Cu) and the heat input per unit length. The ANN model (comprising five nodes in one hidden layer), which was constructed and trained using 60 data points, yielded an R2 value of 0.94 and a mean average percent error (MAPE) of 2.29%. During model verification, the ANN model exhibited superior prediction performance compared with the multiple regression and machine learning models, achieving an R2 value of 0.8644 and a MAPE of 3.06%. Consequently, the ANN model effectively predicted the variation in the yield strength and microstructure resulting from the thermal history and dilution during the welding of 3.5–9% Ni steels with stainless steel-based welding consumables. Furthermore, the application of the prediction model was demonstrated in the design of welding consumables and heat input for 9% Ni steel. Full article
(This article belongs to the Special Issue Microstructural and Mechanical Properties of Metallic Materials, Volume Ⅲ)
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13 pages, 9629 KiB  
Article
Corrosion Behavior of Zn-Al-Mg-Si Coatings in Sulfur Dioxide-Containing Environment
by Henryk Kania and Anżelina Marek
Appl. Sci. 2024, 14(5), 2120; https://doi.org/10.3390/app14052120 - 4 Mar 2024
Cited by 1 | Viewed by 1180
Abstract
Zn-Al-Mg-Si coatings are an excellent alternative to conventional hot-dip galvanizing coatings. Their high corrosion resistance in corrosive environments containing chlorides and CO2 is well recognized. But sulfur dioxide is also an important stimulator of corrosion in the atmospheric environment. This article presents [...] Read more.
Zn-Al-Mg-Si coatings are an excellent alternative to conventional hot-dip galvanizing coatings. Their high corrosion resistance in corrosive environments containing chlorides and CO2 is well recognized. But sulfur dioxide is also an important stimulator of corrosion in the atmospheric environment. This article presents the results of microstructure (SEM/EDS/XRD) and corrosion behavior tests of Zn-Al-Mg-Si coatings obtained by a double hot-dip method on HSLA steel. The corrosion resistance of the coatings was determined in the sulfur dioxide test with general condensation of moisture (EN ISO 6988). In the corrosion test, Zn-Al-Mg-Si coatings showed twofold smaller weight loss compared to conventional hot-dip zinc coatings. It was found that the corrosion behavior of coatings was influenced by the structural components revealed in the outer layer: Al-rich dendritic and interdendritic areas with Zn/MgZn2 eutectic, MgZn2 intermetallic and Si precipitates and their electrochemical nature. The increase in corrosion resistance was caused by the formation of beneficial corrosion products: layered double hydroxides (LDHs) based on divalent Mg2+ and Zn2+ cations, trivalent Al3+ cations and SO42− anions, and zinc hydroxysulfate—Zn4SO4(OH)6∙5H2O. The presence of Si precipitates could cause pitting corrosion of coatings. Full article
(This article belongs to the Special Issue Microstructural and Mechanical Properties of Metallic Materials, Volume Ⅲ)
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15 pages, 9731 KiB  
Article
Effects of Process Parameters on the Bead Shape in the Tandem Gas Metal Arc Welding of Aluminum 5083-O Alloy
by Gwang-Gook Kim, Taehoon Kang, Dong-Yoon Kim, Young-Min Kim, Jiyoung Yu and Junhong Park
Appl. Sci. 2023, 13(11), 6653; https://doi.org/10.3390/app13116653 - 30 May 2023
Cited by 1 | Viewed by 1533
Abstract
In gas metal arc welding (GMAW), the weld bead shape is an important factor that is directly related to the weld quality of welded joints. This study investigates the effects of process parameters, including welding speed (WS) and leading and trailing wire feed [...] Read more.
In gas metal arc welding (GMAW), the weld bead shape is an important factor that is directly related to the weld quality of welded joints. This study investigates the effects of process parameters, including welding speed (WS) and leading and trailing wire feed rates (WFR), on the weld bead shape, including the leg length and penetration depth, in the tandem GMAW of aluminum 5083-O alloy. An asynchronous direct current–direct current pulse tandem GMAW system and a tandem GMAW torch were designed and applied to improve welding productivity and welding quality. Response surface methodology was used to analyze the effects of the process parameters on the weld bead shape and to estimate regression models for predicting the weld bead shape. As a result of observing arc behavior using a high-speed camera, it was confirmed that the leading WFR affects the penetration depth and the trailing WFR affects the leg length. The coefficient of determination (R2) of the regression models was 0.9414 for the leg length and 0.9924 for the penetration depth. It was also validated that the estimated models were effective in predicting the weld bead shape (leg length and penetration depth) representative of weld quality in the tandem GMAW process. Full article
(This article belongs to the Special Issue Microstructural and Mechanical Properties of Metallic Materials, Volume Ⅲ)
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