Institute Of Materials Science Research Articles

Very high cycle fatigue of austenitc stainless steels and their welds for reactor internals at ambient trmperature and 300 °C

The fatigue assessment of safety relevant components is of importance for ageing management with regard to safety and reliability of nuclear power plants. Austenitic stainless steels are often used for reactor internals due to their excellent mechanical and technological properties as well as their corrosion resistance. During operation reactor internals are subject to mechanical and thermo-mechanical loading which induce low cycle (LCF), high cycle (HCF) and even very high cycle (VHCF) fatigue. While the LCF behavior of austenitic steels is already well investigated the fatigue behavior in the VHCF regime has not been characterized in detail so far. Accordingly, the fatigue curves in the applicable international design codes have been extended from originally 106 to the range of highest load cycles up to 1011 load cycles by extrapolation. Nevertheless, the existing data base for load cycles above 107 is still highly insufficient. The aim of the cooperative project of the Institute of Materials Science and Engineering (WKK) at RPTU Kaiserslautern-Landau, Materials Testing Institute (MPA) Stuttgart and Framatome GmbH, Germany is to create a comprehensive database up to the highest load cycles N = 2·109 for austenitic stainless steels and their welds at ambient and elevated temperature.

A unified creep and fatigue life prediction approach for 316 austenitic stainless steel using machine and deep learning

Abstract316 Austenitic stainless steel (AusSS) is extensively utilized in high‐temperature industrial applications such as boiler tubes and nuclear reactor pressure vessels. These components commonly experience failure under high‐temperature and high‐pressure conditions, attributed to either creep or fatigue. Existing classical models for creep and fatigue life prediction focus on a singular failure mode (either creep or fatigue) and consider physical features only. This study aims to develop a unified life prediction model for both creep and fatigue phenomena. It synthesizes information from 12 additional unexplored chemical and microstructural features from the National Institute of Materials Science (NIMS), Japan database, and previously published literature. Machine learning (such as decision tree, random forest, and XGBoost) and deep learning (like deep neural network) algorithms are employed in the modeling process. The trained models have been cross‐validated against unseen creep and fatigue life data, demonstrating superior prediction accuracy of 96.1% for deep neural network compared with classical models.

Preface

43rd International Deep Drawing Research Group (IDDRG) Conference Sustainable Sheet Forming - Circular Economy Melbourne, Australia 1. Introduction The 43rd International Deep Drawing Research Group (IDDRG) conference was held in Melbourne, Australia, on 12–15 March. The conference was co-organised by Deakin University and the Korean Society for Technology of Plasticity. The conference chairs were: Prof. Bernard Rolfe (Deakin University), A/Prof. Matthias Weiss (Deakin University), and Prof Jeong Whan Yoon (KAIST/Deakin University).During IDDRG 2024, all engineering aspects of sheet metal forming were discussed, such as behaviour of workpiece materials, formability, tools, tribology, process robustness, simulation methods and experiments. The general theme of IDDRG 2024 was Sustainable Sheet Forming – Circular Economy. International Deep Drawing Research Group The International Deep Drawing Research Group (IDDRG) was started in the late 1950s as an organization of national groups devoted to the study of sheet metal forming including forming processes, materials, formability issues, tooling, tribology and many other interesting aspects of sheet metal forming research and industrial practices.At the IDDRG, the leading sheet metal specialists and researchers from all over the world gather for fruitful discussions on challenging technical topics in forming technology. The conference is designed to provide an open and inspiring environment for knowledge development, discussions and interactions. Conference topics The theme of the IDDRG 2024 conference was Sustainable Sheet Forming – Circular Economy. In addition to the main theme of the conference, topical sessions with the focus on the challenges and current trends in the field of sheet metal forming, such as: fossil-free steel, sustainable/advanced/smart materials, multi-material concepts/solutions, forming/formability, lightweight structures, digital transformation, digitalization, smart plants, smart press shops, cold and hot forming and trimming (incl. single and multi-step forming and trimming), tribology, simulations (incl. material models, topology optimization, tribology, springback compensation, and geometry assurance), testing, testbeds, springback, shape accuracy, geometry assurance, surface quality, tools and dies, and additive manufacturing.There were six additional mini-symposiums organised by internationally renowned experts held at IDDRG 2024:• 1. Material, Process Monitoring and Quality Control in Roll Forming Processes, organized by Matthias Weiss (Deakin University, Australia);• 2. Forming, Joining and Failure of Fuel Cell and Battery Materials, organized by Michael Pereira (Deakin University, Australia), and Jeong Yoon (KAIST/Deakin University, Korea/Australia);• 3. Machine Learning Driven Forming, organized by Hyunsung Choi (Korea Institute of Materials Science, Korea);• 4. Flexible Metal Forming, organized by Namsu Park (Korea Institute of Industrial Technology, Korea);• 5. Innovative Metal Forming Technology, organized by Professor Richard Yang (Western Sydney University, Australia);• 6. Advanced Materials Characterization, organised by Madhav Baral (University of Kentucky, USA).It was the first time to consider fuel cell and battery cell forming, however, given the global conversion to electric vehicles, it is assumed this forming topic may continue at future IDDRG conferences.List of Keynotes, Venue, Chairs, Organising committee, Scientific Committee, Editors, Number of presentations, published scientific papers/first authors, and participants, Technical/Industrial tours, Acknowledgements and Sponsors and exhibitors are available in this pdf.

Two Teams Are Better Than One: Where Theory and Experiment Successfully Interact

AbstractThis invited Team Profile was created by Prof. Judy I. Wu, Department of Chemistry, University of Houston (USA) and Prof. Michael M. Haley, Department of Chemistry & Biochemistry and the Materials Science Institute, University of Oregon, Eugene (USA). The X‐ray structure of tetra‐t‐butyl‐s‐indacene was reported in 1986 to possess a symmetric, delocalized D2h geometry, which went against conventional wisdom that this antiaromatic should exhibit a bond‐localized C2h structure. Computational studies over the ensuing 35+ years yielded conflicting outcomes as to the preferred geometry. A computational reexamination of this structure by our team found experimental proton chemical shifts matched best with a computed C2h symmetry, confirming a bond‐localized geometry. “Tetra‐tert‐butyl‐s‐indacene is a Bond‐Localized C2h Structure and a Challenge for Computational Chemistry”, L. J. Karas, S. Jalife, R. V. Viesser, J. V. Soares, M. M. Haley, J. I. Wu, Angew. Chem. Int. Ed. 2023, 62, e202307379.

Two Teams Are Better Than One: Where Theory and Experiment Successfully Interact.

This invited Team Profile was created by Prof. Judy I. Wu, Department of Chemistry, University of Houston (USA) and Prof. Michael M. Haley, Department of Chemistry & Biochemistry and the Materials Science Institute, University of Oregon, Eugene (USA). The X-ray structure of tetra-t-butyl-s-indacene was reported in 1986 to possess a symmetric, delocalized D2h geometry, which went against conventional wisdom that this antiaromatic should exhibit a bond-localized C2h structure. Computational studies over the ensuing 35+ years yielded conflicting outcomes as to the preferred geometry. A computational reexamination of this structure by our team found experimental proton chemical shifts matched best with a computed C2h symmetry, confirming a bond-localized geometry. "Tetra-tert-butyl-s-indacene is a Bond-Localized C2h Structure and a Challenge for Computational Chemistry", L. J. Karas, S. Jalife, R. V. Viesser, J. V. Soares, M. M. Haley, J. I. Wu, Angew. Chem. Int. Ed. 2023, 62, e202307379.

BAM Data Store

As a partner in several NFDI consortia, the Bundesanstalt für Materialforschung und -prüfung (BAM, German federal institute for materials science and testing) contributes to research data standardization efforts in various domains of materials science and engineering (MSE). To implement a central research data management (RDM) infrastructure that meets the requirements of MSE groups at BAM, we initiated the Data Store pilot project in 2021. The resulting infrastructure should enable researchers to digitally document research processes and store related data in a standardized and interoperable manner. As a software solution, we chose openBIS, an open-source framework that is increasingly being used for RDM in MSE communities. The pilot project was conducted for one year with five research groups across different organizational units and MSE disciplines. The main results are presented for the use case “nanoPlattform”. The group registered experimental steps and linked associated instruments and chemicals in the Data Store to ensure full traceability of data related to the synthesis of ~400 nanomaterials. The system also supported researchers in implementing RDM practices in their workflows, e.g., by automating data import and documentation and by integrating infrastructure for data analysis. Based on the promising results of the pilot phase, we will roll out the Data Store as the central RDM infrastructure of BAM starting in 2023. We further aim to develop openBIS plugins, metadata standards, and RDM workflows to contribute to the openBIS community and to foster RDM in MSE.

Mechanical Properties of Ultra-Thin Nb3Sn Composite Wires

Flexible Rutherford cables are needed to realize high field superconducting magnets with A15 conductors based on the react-and-wind (R&W) technology. Aiming such an application, ultra-thin A15 composite wires with a diameter of 0.03–0.05 mm have been developed by the National Institute for Materials Science (NIMS). Mechanical properties of such ultra-thin Nb <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> Sn wires were evaluated to determine the cabling parameters and mechanical analysis of twisted cables. Tensile tests were performed at room temperature for 0.05 mm-thick Nb <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> Sn wires before and after heat treatment for the first time. Basic mechanical parameters such as 0.2% proof strength and fracture strength were evaluated from a stress–strain curve. Young's modulus of such a thin wire was determined from unlading and reloading slopes of a load–stroke curve for the specimens with different gauge lengths. Fracture strain was estimated without using extensometers and strain gauges by correcting for machine deformation. Based on these results, we concluded that a simple technique to measure stress–strain curves for ultra-thin Nb <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> Sn wires was able to be established.

A creep life prediction model of P91 steel coupled with back-propagation artificial neural network (BP-ANN) and θ projection method

In order to predict the long-term creep life of P91 steel, this work proposed a coupled model, combining the error-trained back-propagation artificial neural network (BP-ANN) and an improved θ model. Both the short-term rupture life obtained by creep experiments and the related creep data of National Institute for Materials Science (NIMS) database were used to validate the above model. In this model, creep parameters (temperature, stress) were used as the input and relative error of 5 θ model extrapolations as the output. Then, the prediction errors of BP-ANN method were introduced to correct the 5 θ model extrapolation results to achieve a more accurate prediction. Consequently, the long-term creep life of P91 steel was predicted. It demonstrated that with the proposed model, the prediction capability for long-term creep life of P91 steel with 200,000 h is much better than those of other θ methods, which has a prediction uncertainty less than 5%.

Hydrogen Liquefaction by Active Magnetic Regenerative Refrigeration

The article summarizes the research and development of magnetic refrigeration for hydrogen liquefaction, and the Active Magnetic Regenerative Refrigeration (AMRR) developed by National Institute for Materials Science (NIMS). The NIMS-AMR consists of optimized AMR beds, a superconducting solenoid with correction coils, and a dedicated liquid hydrogen vessel with a liquid level sensor. This article also reports the first successful demonstration of hydrogen liquefaction with the AMRR.

Magnetic Refrigeration System Group, National Institute for Materials Science

Magnetic Refrigeration System Group, National Institute for Materials Science

Gigacycle Fatigue Properties of Wrought Aluminum Alloys

Fatigue data sheets of the National Institute for Materials Science disclose the results of gigacycle fatigue tests conducted on wrought aluminum alloys. The materials tested were hot-rolled plates and extruded round bars of A5083P-O (two heats), A7075-T6 (three heats), and A6061-T6 (three heats). The fatigue testing methods were rotating-bending at 100 Hz, ultrasonic at 20 kHz, and uniaxial at 100 Hz. The rotating-bending fatigue tests were conducted for 3 years to reach 1010 cycles. The ultrasonic and uniaxial fatigue tests were also conducted at high stress ratios. This study analyzed the fatigue life data, clarifying frequency effects, presence or absence of fatigue limits, and stress ratio effects. The three types of fatigue tests revealed equivalent results, meaning that frequency effects and stress gradient effects were negligible in the wrought aluminum alloys. Frequency effects were visible in several cast aluminum alloys owing to the influence of humidity, but the wrought aluminum alloys were observed to be insensitive to humidity. Many specimens were fractured at >107 cycles, whereas the degradation of fatigue strength after 107 cycles was very small in A5083P-O but large in A7075-T6 and A6061-T6. A5083P-O revealed fatigue limits at around 107 cycles. The heat treatment effects determined the presence or absence of fatigue limits because the equivalent tensile strengths between A6061-T6 and A5083P-O excluded the possibility of tensile strength effects. The stress ratio effects on gigacycle fatigue strengths of the wrought aluminum alloys are reasonable and can be estimated in conventional ways.

Shaping the Future of Macromolecular Chemistry: A Successful Path from the Start

Shaping the Future of Macromolecular Chemistry: A Successful Path from the Start

On material-agnostic fatigue life prediction using Buckingham Pi Theorem

A robust, quantitative analysis is needed to prevent frequent, catastrophic failure of components in powerplants and aircraft, to cite but two examples, due to cyclic loading that could lead to fatigue failure. The model presented in this research will be useful in the reliable design and safe operation of components and equipment. A fatigue life (the dependent variable) prediction model, which employs both stress-based, and stress–strain based criteria is presented that uses the Buckingham Pi Theorem with the experimental parameters influencing fatigue behaviour taken as the independent variables. The validation of the model is done using reliable, frequently used uniaxial fatigue data, culled from the NIMS (National Institute for Materials Science, Tsukuba, Japan) fatigue database on important industrial alloys that experience fatigue loading in service. (No reliable data on such a scale were available to test the validity of this model under multi-axial fatigue loading conditions.) It is demonstrated that the model predicts the experimental results accurately. The accuracy of prediction is examined for every case and compared with that of some other existing methods. Moreover, the potential applicability of this analysis in areas not yet validated is examined.

Small Solar Installations

Abstract: The paper considers small solar installations, which are developed and created at the Institute of Materials Science of the Academy of Sciences of the Republic of Uzbekistan. The authors of the article operate a Large Solar Furnace with a thermal power of 1000 kW and have extensive experience in operating and building solar high-temperature furnaces. Small solar installations are simple, cheap and easy to operate.

Better utilization of materials’ compositions for predicting their properties: Material composition visualization network

Owing to the complexity and diversity of advanced high-performance materials, it is challenging to comprehensively understand a material’s composition–process–structure–performance relationship. Data-driven approaches have been regarded as the fourth paradigm of new materials R&D. However, the complexity of constituent elements in many material datasets leads to very sparse compositional features, posing a tremendous challenge to machine learning models. In this study, a data mapping scheme based on fundamental atomic features was used to visualize the chemical composition characteristics mapped into two-dimensional grayscale image data to solve the problem of sparse material composition matrix. Based on this, a material composition visualization network (MCVN) is proposed and applied to predict the mechanical properties of steel and classify amorphous alloy materials. We compared the MCVN to other machine learning methods. The MCVN had an average R2 value improvement of 4% on the four targets in the National Institute for Materials Science’s (NIMS’s) steel dataset, where other models already get an average R2 of 0.92, and it achieved an R2 of 0.835 on the cross-sectional shrinkage target in the Shanghai Research Institute of Materials’(SRIM’s) steel dataset where the other models only had an average R2 of 0.64. For the unbalanced amorphous alloy material dataset, the MCVN improved the average Recall of the small-class crystalline alloy (CRA) from 0.58 to 0.78. The method based on expanding the material chemical composition information is universal and provides a new paradigm for material property prediction.

Visualization of Materials Science Topics in Publications of Institutional Repository using Natural Language Processing

SAMURAI (NIMS 2022), a directory service of the National Institute for Materials Science (NIMS) researchers in Japan was launched in 2009 following the development of NIMS institutional repository (Tanifuji et al. 2019). The concept is to synchronize between profile information of researchers and their publications which are self-archived in the repository system. The SAMURAI was renewed in 2017 with interoperable functions with ORCID. SAMURAI supports various links to not only individual articles and patents, but also to databases such as KAKEN (Database of Grants-in-Aid for Scientific Research by NII). The service has yielded fully identified authors of journal articles from research members of NIMS by implementing a unique ResearcherID. Through this directory, NIMS is promoting materials research, supporting management of its researchers activities, and introducing NIMS researchers and their work to the public. In this work, we present an application to describe each researcher's output topics automatically from the archived research papers in the repository, by implementing materials science specific natural language processing developed in our study (Dieb et al. 2021) that visualizes the research trend of each SAMURAI researchers. The approach can maximize information absorbance for general audience and fully corresponds to open science policy. A list of publications' digital object identifiers (DOIs DOI 2022) for each researcher was constructed from his profile in SAMURAI. (In SAMURAI, the DOIs are stored in a PostgreSQL database). Using the DOI, recent publications were retrieved from NIMS text data mining platform (TDM-PF) in their XML format which were mainly available from 2003. Representative topic terms from their research publications that are related to materials science and engineering were extracted. We utilize term frequency analysis and automatic extraction for materials names to extract these necessary informative terms. Additionally, domain knowledge resources such as dictionaries were used. Data was preprocessed using noise reduction such as removing general English language stop words and physical units filtering. Such words do not have significance on their own. Word cloud approach was used for visualization (Fig. 1). This work brings us an opportunity to apply our NLP experience to mine information from research papers for public knowledge as a step towards data-driven materials science.

Transdisciplinary Research in Materials Science − Activities of the Frontier Research Institute for Materials Science at Nagoya Institute of Technology

Transdisciplinary Research in Materials Science − Activities of the Frontier Research Institute for Materials Science at Nagoya Institute of Technology

Critical Characteristics of Ultrafine Nb3Al Superconducting Wires Under Conduction Cooling Conditions

In recent years, an ultrafine Nb₃Al wires with a diameter of less than 50 &#x03BC;m has been developed by a research group at the National Institute for Materials Science (NIMS) in Japan. Therefore, we are planning to use the developed high performance Nb₃Al wires for superconducting applications operated by cryocooler. In order to design the superconducting applications cooled by cryocooler, it is necessary to measure the temperature and magnetic field dependence of the critical current of Nb₃Al wires under conduction cooling conditions. Single and seven-stranded Nb₃Al wires with different diameters (&#x03D5;67 &#x03BC;m and &#x03D5;81 &#x03BC;m) fabricated by the Jelly-rolled method were prepared. The barrier material of the two samples is the same Nb, but the core materials are Nb (&#x03D5;67 &#x03BC;m) and Ta (&#x03D5;81 &#x03BC;m), respectively. The Cu&#x002F;nonCu ratio of the two samples is 1. In this study, the critical currents of sample wires were measured by a four-probe method with current sweep and constant current, and they were compared to define a reasonable critical current value under conduction cooling. Under conduction cooling, it was confirmed that the critical current measurement by the constant current method was more accurate than the current sweep method.

Ultrathin Magnesium Metal Anode – An Essential Component for High‐Energy‐Density Magnesium Battery Materialization

AbstractInvited for this month's cover picture is the group from National Institute for Materials Science, Japan. The cover picture shows the rechargeable magnesium batteries incorporating ultrathin magnesium metal foils with the energy densities of 72 Wh kg−1 based on the weight of both cathodes and anodes. Read the full text of the article at 10.1002/batt.202200153

Identifying New Classes of High Temperature Superconductors With Convolutional Neural Networks

Applying machine learning to aid the search for high temperature superconductors has recently been a topic of significant interest due to the broad applications of these materials but is challenging due to the lack of a quantitative microscopic model. Here we analyze over 33,000 entries from the Superconducting Materials Database, maintained by the National Institute for Materials Science of Japan, assigning crystal structures to each entry by correlation with Materials project and other structural databases. These augmented inputs are combined with material-specific properties, including critical temperature, to train convolutional neural networks (CNNs) to identify superconductors. Classification models achieve accuracy &amp;gt;95% and regression models trained to predict critical temperature achieve R2 &amp;gt;0.92 and mean absolute error ≈ 5.6 K. A crystal-graph representation whereby an undirected graph encodes atom sites (graph vertices) and their bonding relationships (graph edges), is used to represent materials’ periodic crystal structure to the CNNs. Trained networks are used to search though 130,000 crystal structures in the Materials Project for high temperature superconductor candidates and predict their critical temperature; several materials with model-predicted TC &amp;gt;30 K are proposed, including rediscovery of the recently explored infinite layer nickelates.