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Characterisation and Testing of Materials: Advancing the State of the Art

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Advanced Materials Characterization".

Deadline for manuscript submissions: closed (10 February 2024) | Viewed by 3096

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Guest Editor
School of Engineering and Materials Science, Queen Mary University of London, London E14NS, UK
Interests: performance of materials; characterisation of materials; testing of materials; failure investigation; materials selection; materials in design
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Special Issue Information

Dear Colleagues,

The characterisation and testing of materials is a critical field that underpins the development of new materials, enhances the properties of existing materials, and supports their safe and efficient use. The developments reported upon in Materials have been fundamental to human society, and in the future will continue to be essential to solving the world's most significant problems.

Recent years have seen significant advances in the tools and methods available to scientists and engineers and those able to characterise and test materials. As our understanding of the physical, chemical, and biological fundamentals of materials improves, so does our ability to develop new instrumentation and techniques. When these traditional areas of advancement are coupled with artificial intelligence and computational methods, novel opportunities and challenges emerge at an unprecedented pace.

This Special Issue of Materials seeks to capture a broad overview of the latest advancements in a variety of areas related to the characterisation and testing of materials. We particularly encourage the submission of articles that explore the integration of AI and computational methods with traditional characterisation techniques. We also welcome submissions from any materials-related discipline, regardless of the specific techniques applied. Topics of particular interest include the following:

  • Additive manufacturing
  • Nanoscale characterisation techniques
  • The application of AI and computational methods
  • In situ testing
  • Standards and regulations
  • Sustainability
  • Clean energy
  • The hydrogen economy

The editorial team encourages researchers working in all areas of materials science and engineering to contribute to this Special Issue, as long as the work has a strong focus on characterization and testing. We do not require all work to be fully mature; rather, we welcome breaking and new developments. All papers will be reviewed by expert referees and will be published in this Special Issue of Materials.

Dr. Andrew Spowage
Guest Editor

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

  • materials
  • characterisation
  • testing
  • AI
  • future challenges

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Published Papers (2 papers)

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Research

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15 pages, 4128 KiB  
Article
Defect Classification for Additive Manufacturing with Machine Learning
by Mika León Altmann, Thiemo Benthien, Nils Ellendt and Anastasiya Toenjes
Materials 2023, 16(18), 6242; https://doi.org/10.3390/ma16186242 - 16 Sep 2023
Cited by 3 | Viewed by 1828
Abstract
Additive manufacturing offers significant design freedom and the ability to selectively influence material properties. However, conventional processes like laser powder bed fusion for metals may result in internal defects, such as pores, which profoundly affect the mechanical characteristics of the components. The extent [...] Read more.
Additive manufacturing offers significant design freedom and the ability to selectively influence material properties. However, conventional processes like laser powder bed fusion for metals may result in internal defects, such as pores, which profoundly affect the mechanical characteristics of the components. The extent of this influence varies depending on the specific defect type, its size, and morphology. Furthermore, a single component may exhibit various defect types due to the manufacturing process. To investigate these occurrences with regard to other target variables, this study presents a random forest tree model capable of classifying defects in binary images derived from micrographs. Our approach demonstrates a classification accuracy of approximately 95% when distinguishing between keyhole and lack of fusion defects, as well as process pores. In contrast, unsupervised models yielded prediction accuracies below 60%. The model’s accuracy in differentiating between lack of fusion and keyhole defects varies based on the manufacturing process’s parameters, primarily due to the irregular shapes of keyhole defects. We provide the model alongside this paper, which can be utilized on a standard computer without the need for in situ monitoring systems during the additive manufacturing process. Full article
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Review

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28 pages, 13227 KiB  
Review
Nanoindentation Test of Ion-Irradiated Materials: Issues, Modeling and Challenges
by Hailiang Ma, Ping Fan, Qiuyu Qian, Qiaoli Zhang, Ke Li, Shengyun Zhu and Daqing Yuan
Materials 2024, 17(13), 3286; https://doi.org/10.3390/ma17133286 - 3 Jul 2024
Cited by 2 | Viewed by 1019
Abstract
Exposure of metals to neutron irradiation results in an increase in the yield strength and a significant loss of ductility. Irradiation hardening is also closely related to the fracture toughness temperature shift or the ductile-to-brittle transition temperature (DBTT) shift in alloys with a [...] Read more.
Exposure of metals to neutron irradiation results in an increase in the yield strength and a significant loss of ductility. Irradiation hardening is also closely related to the fracture toughness temperature shift or the ductile-to-brittle transition temperature (DBTT) shift in alloys with a body-centered cubic (bcc) crystal structure. Ion irradiation is an indispensable tool in the study of the radiation effects of materials for nuclear energy systems. Due to the shallow damage depth in ion-irradiated materials, the nanoindentation test is the most commonly used method for characterizing the changes in mechanical properties after ion irradiation. Issues that affect the analysis of irradiation hardening may arise due to changes in the surface morphology and mechanical properties, as well as the inherent complexities in nanoscale indentation. These issues, including changes in surface roughness, carbon contamination, the pile-up effect, and the indentation size effect, with corresponding measures, were reviewed. Modeling using the crystal plasticity finite element method of the nanoindentation of ion-irradiated materials was also reviewed. The challenges in extending the nanoindentation test to high temperatures and to multiscale simulation were addressed. Full article
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