Results 141 to 150 of about 405,500 (194)

Electrospinning Technology, Machine Learning, and Control Approaches: A Review

Advanced Engineering Materials, EarlyView.
Electrospinning produces micro‐ and nanoscale fibers, holding great promise in biomedical engineering. Industrial adoption faces challenges in controlling fiber properties, reproducibility, and scalability. This review explores electrospinning techniques, modeling, and machine learning for process optimization.
Arya Shabani, Gorkem Anil Al, Nael Berri, Bernardo Castro‐Dominguez, Hannah S. Leese, Uriel Martinez‐Hernandez   +5 more
wiley   +1 more source

Semantic Orchestration and Exploitation of Material Data: A Dataspace Solution Demonstrated on Steel and Copper Applications

Advanced Engineering Materials, EarlyView.
This article introduces the Dataspace Management System (DSMS), a methodological framework realized in software, designed as a technology stack to power dataspaces with a focus on advanced knowledge management in materials science and manufacturing. DSMS leverages heterogeneous data through semantic integration, linkage, and visualization, aligned with
Yoav Nahshon, Lukas Morand, Matthias Büschelberger, Dirk Helm, Kiran Kumaraswamy, Paul Zierep, Matthias Weber, Pablo de Andrés   +7 more
wiley   +1 more source

Ontologies for FAIR Data in Additive Manufacturing: A Use Case‐Based Evaluation

Advanced Engineering Materials, EarlyView.
An ontology‐based approach for generating findable, accessible, interoperable, reusable data in additive manufacturing is explored, focusing on powder bed fusion. The article highlights the benefits of enhanced data findability and digital twin enablement, while addressing challenges like data integration complexity and the need for specialized ...
Thomas Bjarsch, Klaus Drechsler, Johannes Schilp   +2 more
wiley   +1 more source

Ontology‐Based Digital Infrastructure for Data‐Driven Glass Development

Advanced Engineering Materials, EarlyView.
This work addresses the inefficiencies in traditional glass development by implementing an ontology‐based digital infrastructure coupled with a high‐throughput robotic melting system. The approach integrates machine learning models, predictive tools, and a semantic database.
Ya‐Fan Chen, Felix Arendt, Hansjörg Bornhöft, Andréa S. S. de Camargo, Joachim Deubener, Andreas Diegeler, Shravya Gogula, Altair T. Contreras Jaimes, Sebastian Kempf, Martin Kilo, René Limbach, Ralf Müller, Rick Niebergall, Zhiwen Pan, Frank Puppe, Stefan Reinsch, Gerhard Schottner, Simon Stier, Tina Waurischk, Lothar Wondraczek, Marek Sierka   +20 more
wiley   +1 more source

KupferDigital: Ontology‐Based Digital Representation for the Copper Life Cycle

Advanced Engineering Materials, EarlyView.
It is demonstrated how data from different stages in the life cycle of a copper product can be stored in an ontology‐based data space. In order to showcase the methods and tools used in the project, four use cases are presented that show how data from different sources can be semantically described and queried from a shared data space.
Miriam Eisenbart, Thomas Hanke, Felix Bauer, Hossein Beygi Nasrabadi, Kurt Junghanns, Gordian Dziwis, Ladji Tikana, Ashak Mahmud Parvez, Karl Gerald van den Boogaart, Mohsin Sajjad, Valerie Friedmann, Johannes Preußner, Anantha Narayanan Ramakrishnan, Sandy Klengel, Lars‐Peter Meyer, Michael Martin, Ulrich Ernst Klotz, Birgit Skrotzki, Matthias Weber   +18 more
wiley   +1 more source

A Workflow‐Centric Approach to Generating FAIR Data Objects for Computationally Generated Microstructure‐Sensitive Mechanical Data

Advanced Engineering Materials, EarlyView.
A novel metadata schema is introduced to address the sparsity of microstructure‐sensitive mechanical data. The design follows the material modeling workflow, as each run results in the definition of unique data objects. The schema includes categorical elements to ensure findability, accessibility, interoperability, and reusability (FAIR). This approach
Ronak Shoghi, Alexander Hartmaier
wiley   +1 more source

StahlDigital: Ontology‐Based Workflows for the Steel Industry

Advanced Engineering Materials, EarlyView.
The strength of the steel industry is based on the mastery of microstructure–property relationships. Digital workflows contribute to this aim by making the complexity of workflows reproducible and their execution user independent. The tools and workflows developed in the project StahlDigital as part of the German MaterialDigital initiative are ...
Franz Roters, Ahmed Aslam, Yang Bai, Matthias Büschelberger, Kirill Bulert, Alexander Butz, Tilmann Hickel, Tushar Jogi, Silke Klitschke, Michael Martin, Lars‐Peter Meyer, Lukas Morand, Yoav Nahshon, Norman Radtke, Ujjal Saikia, Andreas Trondl, Alexander Wessel, Paul Zierep, Dirk Helm   +18 more
wiley   +1 more source

Advancing Digital Transformation in Material Science: The Role of Workflows Within the MaterialDigital Initiative

Advanced Engineering Materials, EarlyView.
The MaterialDigital initiative drives the digital transformation of material science by promoting findable, accessible, interoperable, and reusable principles and enhancing data interoperability. This article explores the role of scientific workflows, highlights challenges in their adoption, and introduces the Workflow Store as a key tool for sharing ...
Simon Bekemeier, Celso Ricardo Caldeira Rêgo, Han Lin Mai, Ujjal Saikia, Osamu Waseda, Markus Apel, Felix Arendt, Alexander Aschemann, Bernd Bayerlein, Robert Courant, Gordian Dziwis, Florian Fuchs, Ulrich Giese, Kurt Junghanns, Mohamed Kamal, Lukas Koschmieder, Sebastian Leineweber, Marc Luger, Marco Lukas, Jürgen Maas, Jana Mertens, Björn Mieller, Ludger Overmeyer, Norbert Pirch, Jan Reimann, Sebastian Schröck, Philipp Schulze, Jörg Schuster, Alexander Seidel, Oleg Shchyglo, Marek Sierka, Frank Silze, Simon Stier, Marvin Tegeler, Jörg F. Unger, Matthias Weber, Tilmann Hickel, Jörg Schaarschmidt   +37 more
wiley   +1 more source

A Physically‐Based Machine Learning Approach Inspires an Analytical Model for Spider Silk Supercontraction

Advanced Functional Materials, EarlyView.
Starting from an experimental database, a physically‐based machine learning approach is used to gain new insights into spider silk supercontraction. These insights inspire a microstructure‐based analytical model that quantitatively predicts supercontraction based on protein primary structure properties. New experiments to further elucidate the physical
Vincenzo Fazio, Ali D. Malay, Keiji Numata, Nicola M. Pugno, Giuseppe Puglisi   +4 more
wiley   +1 more source

Recent Advances in Highly Luminescent Carbon Dots

Advanced Functional Materials, EarlyView.
Here, a comprehensive summary of the advancements is provided in highly luminescent carbon dots across various luminescence bands since their discovery in 2004. It systematically covers the synthesis, structural characteristics, preparation strategies, and essential purification of these carbon dots, along with their applications in efficient ...
Yupeng Liu, Zhujun Huang, Xuanye Wang, Yiming Hao, Junxiang Yang, Hui Wang, Songnan Qu   +6 more
wiley   +1 more source