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The Parameters of Advanced Materials
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The Parameters of Advanced Materials

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

Deadline for manuscript submissions: 10 July 2026 | Viewed by 2567

Special Issue Editors

Dr. Grzegorz Tytko

E-Mail Website
Guest Editor
Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Gliwice, Poland
Interests: eddy current testing; electromagnetism; impedance analysis; analytical modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Zhiyuan Xu

E-Mail Website
Guest Editor
School of Instrument Science and Optical Engineering, Nanchang Hangkong University, Nanchang, China
Interests: electromagnetic nondestructive testing; electrical impedance tomography; magnetic sensors; ceramic matrix composites
Special Issues, Collections and Topics in MDPI journals
Dr. Jolanta Makowska

E-Mail Website
Guest Editor
Faculty of Science and Technology, Institute of Materials Engineering, University of Silesia, 75 Pułku Piechoty 1A, 41-500 Chorzow, Poland
Interests: ceramics; dielectric properties; impedance spectroscopy
Special Issues, Collections and Topics in MDPI journals
Dr. Mingyang Lu

E-Mail Website
Guest Editor
CNDE, Iowa State University, Ames, IA, USA
Interests: nondestructive evaluation; eddy current testing; electromagnetic modeling; materials evaluation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The characterization of advanced materials by determining the values ​​of their parameters is of great importance to many industrial applications. The results of such determinations allow us to observe significant correlations and elucidate the influence of structure on material properties. As a result, effective techniques for the manufacture, use, and protection of materials are increasingly being developed.

For this Special Issue, we encourage authors to submit papers on the following material parameters: physical, chemical, mechanical, thermal, electrical, and magnetic.

In addition, we welcome papers on the following topics:

  • Determining geometric dimensions (thickness, diameter, width, height);
  • Structure testing (granularity, porosity, roughness, crystal structure, chemical composition);
  • Assessing technical conditions (cracks, delamination, corrosion, material defects).

Papers on techniques of measuring material parameters using various devices such as meters, sensors, probes, and microscopes are also welcome.

Dr. Grzegorz Tytko
Dr. Zhiyuan Xu
Dr. Jolanta Makowska
Dr. Mingyang Lu
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 250 words) can be sent to the Editorial Office for assessment.

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

  • structure
  • physical parameters
  • chemical parameters
  • mechanical parameters
  • thermal parameters
  • electrical parameters
  • magnetic parameters
  • crystal structure

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

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16 pages, 5357 KB  
Article
Thermal Deformation in Non-Planar Large-Scale Additive Manufacturing of ABS: Experimental and Finite Element Analysis
by Mehmet Aladag, Engin Tek, Mehmet Ali Akeloglu, Adrian Dubicki, Izabela Zgłobicka, Omer Eyercioglu and Krzysztof J. Kurzydlowski
Materials 2026, 19(6), 1064; https://doi.org/10.3390/ma19061064 - 11 Mar 2026
Viewed by 327
Abstract
In this study, thermal deformation in non-planar, large-scale additive manufacturing (LSAM) was experimentally and numerically investigated. A Bézier-based non-planar build surface was fabricated by CNC machining, and a single layer of ABS was deposited using a hybrid LSAM system. Toolpaths with raster angles [...] Read more.
In this study, thermal deformation in non-planar, large-scale additive manufacturing (LSAM) was experimentally and numerically investigated. A Bézier-based non-planar build surface was fabricated by CNC machining, and a single layer of ABS was deposited using a hybrid LSAM system. Toolpaths with raster angles of 0° and 45° were generated for surface-conformal printing. Infrared thermography was employed to monitor the thermal history during deposition. A three-dimensional finite element model was developed to simulate transient heat transfer and thermally induced deformation. Experimental deformation was quantified by 3D scanning and compared with simulation results. The results show that the slope geometry strongly influences deformation direction: negative slopes promote contraction, whereas positive slopes lead to upward deflection. Maintaining the material temperature above the glass transition temperature significantly reduces skew deformation. The finite element method predictions demonstrate strong agreement with experimental measurements, with normalized root mean square errors (NRMSEs) of approximately 11% for thermal deformation and 10% for temperature history. The proposed framework enables prediction and mitigation of thermal warping in non-planar polymer additive manufacturing. Full article
(This article belongs to the Special Issue The Parameters of Advanced Materials)
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20 pages, 1801 KB  
Communication
Interpretable Machine Learning with Prediction Uncertainty Quantification for d33 in (K0.5Na0.5) NbO3-Based Lead-Free Piezoelectric Ceramics
by Xiaohui Yuan, Yalong Liang, Bang Lu, Gaochao Zhao and Pei Li
Materials 2026, 19(5), 948; https://doi.org/10.3390/ma19050948 - 28 Feb 2026
Viewed by 658
Abstract
The accelerated discovery of high-performance lead-free piezoelectric ceramics is hindered by the vast compositional space and the limited interpretability of conventional machine learning (ML) models. Here, we propose a physics-informed and interpretable ML framework with integrated uncertainty quantification to predict and understand the [...] Read more.
The accelerated discovery of high-performance lead-free piezoelectric ceramics is hindered by the vast compositional space and the limited interpretability of conventional machine learning (ML) models. Here, we propose a physics-informed and interpretable ML framework with integrated uncertainty quantification to predict and understand the piezoelectric coefficient d33 of (K0.5Na0.5) NbO3 (KNN)-based ceramics. A curated dataset of 1113 experimental samples is used to construct 65 descriptors by decoupling A-site and B-site ionic contributions. Pearson correlation analysis reduces these to an optimized 11-dimensional feature set for training deep neural networks, Wide & Deep networks, and residual networks. A Bayesian neural network further provides predictive uncertainty, which quantitatively reflects the confidence of machine-learning-based d33 predictions rather than experimental measurement uncertainty. To achieve physical interpretability, SHapley Additive exPlanations (SHAP) are combined with the Sure Independence Screening and Sparsifying Operator (SISSO) to derive a compact analytical descriptor revealing that sintering temperature, B-site electronic anisotropy, and A-site ionic displacement jointly govern d33. The proposed framework achieves high accuracy (R2 ≈ 0.81) while offering transparent design rules for next-generation lead-free piezoelectrics. Full article
(This article belongs to the Special Issue The Parameters of Advanced Materials)
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Review

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32 pages, 2496 KB  
Review
Stress Corrosion Cracking: Mechanisms, Materials Challenges, and Engineering Solutions
by Lincoln Pinoski, Subin Antony Jose and Pradeep L. Menezes
Materials 2026, 19(5), 898; https://doi.org/10.3390/ma19050898 - 27 Feb 2026
Viewed by 1150
Abstract
Stress corrosion cracking (SCC) is a critical failure mechanism that arises from the synergistic interaction between tensile stress and corrosive environments, leading to sudden and often catastrophic failures in structural components across various industries, including aerospace, nuclear energy, oil and gas, and marine [...] Read more.
Stress corrosion cracking (SCC) is a critical failure mechanism that arises from the synergistic interaction between tensile stress and corrosive environments, leading to sudden and often catastrophic failures in structural components across various industries, including aerospace, nuclear energy, oil and gas, and marine engineering. This review synthesizes current understanding of SCC mechanisms, including film rupture and anodic dissolution, hydrogen embrittlement, and adsorption-induced cleavage, and evaluates material susceptibility across steels, aluminum alloys, nickel-based alloys, titanium, and emerging high-entropy alloys. Environmental factors such as aqueous chemistry, temperature, pressure, pH, and dissolved gases are examined for their roles in SCC initiation and propagation. Advanced testing methodologies, including slow strain rate testing, bent-beam configurations, electrochemical monitoring, and high-resolution microscopy, are discussed for characterizing SCC behavior. Engineering mitigation strategies are presented, encompassing material selection, stress reduction, surface treatments, and environmental control. Case studies illustrate real-world SCC failures and inform best practices. Emerging trends highlight the potential of machine learning for predictive maintenance and the development of SCC-resistant materials through additive manufacturing and microstructural engineering. This comprehensive review provides mechanical engineers with actionable insights for designing, maintaining, and safeguarding components against SCC in demanding service environments. Full article
(This article belongs to the Special Issue The Parameters of Advanced Materials)
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