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Metals
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Light Alloys and Composites
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Light Alloys and Composites

A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Metal Casting, Forming and Heat Treatment".

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 9330

Special Issue Editors

Dr. Aihan Feng

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Guest Editor
Department of Material Science and Engineering, Tongji University, Shanghai, China
Interests: titanium alloys; intermetallic alloys; characterization
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hao Wang

E-Mail Website
Guest Editor
School of Materials and Chemistry/Interdisciplinary Center for Additive Manufacturing, University of Shanghai for Science and Technology, Shanghai, China
Interests: materials genome; advanced materials; additive manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the evolving landscape of light alloys and composites, focusing on their pivotal role in modern engineering. We invite contributions that delve into the latest advancements, novel applications, and emerging trends in the field. Articles covering diverse aspects, from fundamental properties and processing techniques to the innovative utilization and future prospects of these materials, are encouraged. Additionally, we welcome interdisciplinary studies showcasing the integration of light alloys and composites across industries, fostering a deeper understanding of their multifaceted contributions to technological advancements. Join us in this endeavor to pursue the forefront of research, and push the boundaries of these materials’ capabilities and their transformative impacts on various sectors of industry and technology.

Dr. Aihan Feng
Prof. Dr. Hao Wang
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. Metals 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.

Keywords

  • light alloys
  • composites
  • mechanical property
  • forming
  • microstructure
  • modeling and simulation
  • characterization

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Published Papers (5 papers)

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Research

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14 pages, 2941 KiB  
Article
Application of Machine Learning in Constitutive Relationship Prediction of Porous Titanium Materials for Artificial Bone
by Chengzhi Tan, Chunjin Li and Zhiqiang Liu
Metals 2024, 14(6), 634; https://doi.org/10.3390/met14060634 - 27 May 2024
Viewed by 891
Abstract
Artificial bone porous titanium materials are widely used in orthopedic implants. However, the traditional constitutive model is often limited by the complexity and accuracy of the model, and it is difficult to accurately and efficiently describe the constitutive relationship of porous titanium materials. [...] Read more.
Artificial bone porous titanium materials are widely used in orthopedic implants. However, the traditional constitutive model is often limited by the complexity and accuracy of the model, and it is difficult to accurately and efficiently describe the constitutive relationship of porous titanium materials. In this study, structured data were established based on experimental data from published papers, and goodness of fit (R2), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to evaluate the model. The prediction effect of random forest (RF), multi-layer perceptron (MLPR) and support vector machine (SVR) on the constitutive relationship of porous titanium materials was discussed. Through comprehensive comparison, it can be seen that the RF model with max_depth of 24 and n_estimators of 160 has the best performance in prediction, and the average absolute percentage error is less than 4.4%, which means it can accurately predict the temperature sensitivity and strain rate sensitivity of porous titanium materials. And its predictive ability is better than that of the traditional constitutive model, which provides a new idea and method for the constitutive modeling of porous titanium materials. Full article
(This article belongs to the Special Issue Light Alloys and Composites)
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11 pages, 2488 KiB  
Article
Aging Treatment Induces the Preferential Crystallographic Orientation of αs in the Near-α Titanium Alloy Ti60
by Bin Liu, Chenglu Liu, Xuewen Li, Hao Wu, Kesong Miao, He Wu and Rengeng Li
Metals 2024, 14(5), 602; https://doi.org/10.3390/met14050602 - 20 May 2024
Viewed by 819
Abstract
In this article, we subjected the Ti60 alloy to solid-solution treatment at 1020 °C and aging treatment at 600 °C, respectively, achieving a bimodal microstructure. The microstructures obtained after aging treatment showed no significant difference in the primary α-phase content, size, and width [...] Read more.
In this article, we subjected the Ti60 alloy to solid-solution treatment at 1020 °C and aging treatment at 600 °C, respectively, achieving a bimodal microstructure. The microstructures obtained after aging treatment showed no significant difference in the primary α-phase content, size, and width of the lamellar α phase. This suggests that the final microstructure morphology is primarily determined by the solid-solution temperature, with the aging process exerting less pronounced effects on microstructural alterations. Furthermore, we investigated the effect of solid-solution and aging treatment on the crystallographic orientation evolution of the secondary α phase (αs) in the near-α titanium alloy Ti60. The αs phase displays a random orientation in solid-solution treatment sample, while it demonstrated a preferential {0 1 −1 0} orientation after aging treatment. This interesting phenomenon is attributed to the enhanced variant selection resulting from the dissolution of variant near 60° and 90° during aging. Furthermore, the αs with {0 1 −1 0} orientation nucleated at the grain boundary and coalesced into larger αs lath with increasing aging time, further contributing to the αs {0 1 −1 0} texture. Full article
(This article belongs to the Special Issue Light Alloys and Composites)
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21 pages, 61471 KiB  
Article
Predictability of Different Machine Learning Approaches on the Fatigue Life of Additive-Manufactured Porous Titanium Structure
by Shuailong Gao, Xuezheng Yue and Hao Wang
Metals 2024, 14(3), 320; https://doi.org/10.3390/met14030320 - 11 Mar 2024
Cited by 5 | Viewed by 1515
Abstract
Due to their outstanding mechanical properties and biocompatibility, additively manufactured titanium porous structures are extensively utilized in the domain of medical metal implants. Implants frequently undergo cyclic loading, underscoring the significance of predicting their fatigue performance. Nevertheless, a fatigue life model tailored to [...] Read more.
Due to their outstanding mechanical properties and biocompatibility, additively manufactured titanium porous structures are extensively utilized in the domain of medical metal implants. Implants frequently undergo cyclic loading, underscoring the significance of predicting their fatigue performance. Nevertheless, a fatigue life model tailored to additively manufactured titanium porous structures is currently absent. This study employs multiple linear regression, artificial neural networks, support vector machines, and random forests machine learning models to assess the impact of structural and mechanical factors on fatigue life. Four standard maximum likelihood models were trained, and their predictions were compared with fatigue experiments to validate the efficacy of the machine learning models. The findings suggest that the fatigue life is governed by both the fatigue stress and the overall yield stress of the porous structures. Furthermore, it is recommended that the optimal combination of hyperparameters involves setting the first hidden layer of the artificial neural network model to three or four neurons, establishing the gamma value of the support vector machine model at 0.0001 with C set to 30, and configuring the n_estimators of the random forest model to three with max_depth set to seven. Full article
(This article belongs to the Special Issue Light Alloys and Composites)
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Review

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23 pages, 9867 KiB  
Review
Effect of Hot Deformation and Heat Treatment on the Microstructure and Properties of Spray-Formed Al-Zn-Mg-Cu Alloys
by Lingfei Cao, Xiaomin Lin, Zhenghao Zhang, Min Bai and Xiaodong Wu
Metals 2024, 14(4), 451; https://doi.org/10.3390/met14040451 - 11 Apr 2024
Cited by 1 | Viewed by 1348
Abstract
Spray forming is a manufacturing process that enables the production of high-performance metallic materials with exceptional properties. Due to its rapid solidification nature, spray forming can produce materials that exhibit fine, uniform, and equiaxed microstructures, with low micro-segregation, high solubility, and excellent workability. [...] Read more.
Spray forming is a manufacturing process that enables the production of high-performance metallic materials with exceptional properties. Due to its rapid solidification nature, spray forming can produce materials that exhibit fine, uniform, and equiaxed microstructures, with low micro-segregation, high solubility, and excellent workability. Al-Zn-Mg-Cu alloys have been widely used in the aerospace field due to their excellent properties, i.e., high strength, low density, and outstanding machinability. The alloy manufactured by spray forming has a combination of better impact properties and higher specific strength, due to its higher cooling rate, higher solute concentration, and lower segregation. In this manuscript, the recent development of spray-formed Al-Zn-Mg-Cu alloys is briefly reviewed. The influence of hot working, i.e., hot extrusion, hot forging, and hot rolling, as well as different heat treatments on the property and microstructure of spray-formed Al-Zn-Mg-Cu alloys is introduced. The second phases and their influence on the microstructure and mechanical properties are summarized. Finally, the potential in high-temperature applications and future prospects of spray-formed aluminum alloys are discussed. Full article
(This article belongs to the Special Issue Light Alloys and Composites)
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26 pages, 7000 KiB  
Review
Machine Learning Design for High-Entropy Alloys: Models and Algorithms
by Sijia Liu and Chao Yang
Metals 2024, 14(2), 235; https://doi.org/10.3390/met14020235 - 15 Feb 2024
Cited by 6 | Viewed by 4204
Abstract
High-entropy alloys (HEAs) have attracted worldwide interest due to their excellent properties and vast compositional space for design. However, obtaining HEAs with low density and high properties through experimental trial-and-error methods results in low efficiency and high costs. Although high-throughput calculation (HTC) improves [...] Read more.
High-entropy alloys (HEAs) have attracted worldwide interest due to their excellent properties and vast compositional space for design. However, obtaining HEAs with low density and high properties through experimental trial-and-error methods results in low efficiency and high costs. Although high-throughput calculation (HTC) improves the design efficiency of HEAs, the accuracy of prediction is limited owing to the indirect correlation between the theoretical calculation values and performances. Recently, machine learning (ML) from real data has attracted increasing attention to assist in material design, which is closely related to performance. This review introduces common and advanced ML models and algorithms which are used in current HEA design. The advantages and limitations of these ML models and algorithms are analyzed and their potential weaknesses and corresponding optimization strategies are discussed as well. This review suggests that the acquisition, utilization, and generation of effective data are the key issues for the development of ML models and algorithms for future HEA design. Full article
(This article belongs to the Special Issue Light Alloys and Composites)
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