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Semantic Representation of Low‐Cycle‐Fatigue Testing Data Using a Fatigue Test Ontology and ckan.kupferdigital Data Management System

open access: yesAdvanced Engineering Materials, Volume 27, Issue 8, April 2025.
This article introduces an automated approach for converting the raw research data (use case of low‐cycle‐fatigue testing dataset) to machine‐readable resource description framework ones and storing them in an open digital repository. As two main prerequisites for this data digitalization process, the development of fatigue testing ontology and ckan ...
Hossein Beygi Nasrabadi   +2 more
wiley   +1 more source

Soft‐Layered Composites with Wrinkling‐Activated Multi‐Linear Elastic Behavior, Stress Mitigation, and Enhanced Strain Energy Storage

open access: yesAdvanced Engineering Materials, EarlyView.
In this study, exciting new bi‐/multi‐linear elastic behavior of soft elastic composites that accompany the activation of wrinkling in the embedded interfacial layers is analyzed. The new features and performance of these composite materials, including dramatic enhancements in energy storage, can be tailored by the concentration of interfacial layers ...
Narges Kaynia   +2 more
wiley   +1 more source

An Automatized Simulation Workflow for Powder Pressing Simulations Using SimStack

open access: yesAdvanced Engineering Materials, Volume 27, Issue 8, April 2025.
The implementation of Workflow active Nodes (WaNos) for the convenient execution and automated evaluation of discrete element method calculations of powder pressing is showcased. Purposeful combination of WaNos creates timesaving and resource‐effective computational workflows.
Bjoern Mieller   +2 more
wiley   +1 more source

USEFULNESS AND LIMITATION OF DIGITAL RECTAL EXAMINATION AND IMAGING STUDIES IN STAGING PROSTATE CANCER

open access: bronze, 1994
Shinichi Egawa   +7 more
openalex   +2 more sources

Digital Methods for the Fatigue Assessment of Engineering Steels

open access: yesAdvanced Engineering Materials, Volume 27, Issue 8, April 2025.
The use of engineering steels is often limited by their fatigue strength. In the sake of a faster product development, the fatigue behavior can be predicted by machine learning (ML). In this work, ML is applied on a heterogeneous database, covering a wide range of steel types.
Sascha Fliegener   +7 more
wiley   +1 more source

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