added two articles

_posts/2023-12-01-Toward-a-digital-materials-mechanical-testing-lab.md

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+---
+layout: post
+title: Toward a digital materials mechanical testing lab
+subtitle: The digitalization of mechanical testing laboratories
+categories: ['Materials Science and Engineering', 'Semantic Web Technologies', 'Publication']
+tags: ['linked open data', 'materials science', 'ontology', 'semantic web']
+---
+
+## Toward a digital materials mechanical testing lab [^fn1]
+
+ This paper introduces a method to digitize mechanical testing laboratories to aid the growth of Industry 4.0 technologies. It uses detailed and standard-compliant materials testing knowledge graphs and tools for efficient ontology development, converting heterogeneous mechanical testing data into machine-readable, uniform, and standardized structures. The mechanical testing ontology (MTO) is based on ISO 23718 and ISO/IEC 21838-2 standards for representing mechanical testing data. The approach was successfully applied to a trial digitalization of a materials testing lab, converting various forms of data into standardized RDF formats. The method enables industries to access reliable, traceable data from various sources, facilitating faster and more cost-effective product development and improved product performance in engineering and ecological aspects.
+
+[^fn1]: [source](https://doi.org/10.1016/j.compind.2023.104016)

_posts/2024-01-18-Enhancing-Reproducibility-in-Precipitate-Analysis_A-FAIR-Approach.md

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+---
+layout: post
+title: Enhancing Reproducibility in Precipitate Analysis
+subtitle: A FAIR Approach with Automated Dark-Field Transmission Electron Microscope Image Processing
+categories: ['Materials Science and Engineering', 'Semantic Web Technologies', 'Publication']
+tags: ['linked open data', 'materials science', 'ontology', 'semantic web']
+---
+
+## Enhancing Reproducibility in Precipitate Analysis: A FAIR Approach with Automated Dark-Field Transmission Electron Microscope Image Processing [^fn1]
+
+ A New method for analyzing the microstructural changes in high-strength aluminum alloys, commonly used in aerospace and automotive industries, that deteriorate over time due to aging. The traditional manual analysis of these changes is replaced with an automated approach that provides more objective and reproducible results. The method involves using dark-field transmission electron microscopy images, generating and evaluating precipitation contours, and converting the results into semantic data structures. The adoption of Jupyter Notebooks and Semantic Web technologies in this process enhances the reproducibility and comparability of the findings, serving as a model for FAIR image and research data management
+
+[^fn1]: [source](https://link.springer.com/article/10.1007/s40192-023-00331-5)