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    <responseDate>2026-10-14T07:49:18Z</responseDate>
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                <identifier>oai:data.mendeley.com/r8d69gst88.1</identifier>
                <datestamp>2024-09-12T10:33:17Z</datestamp>
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    <dc:creator>Dong, ruizi</dc:creator>
    <dc:title>Lightning strike damage images of CFRP laminates Part2</dc:title>
    <dc:publisher>Mendeley Data</dc:publisher>
    <dc:description>This is the second part of the dataset. For the first part, please see &quot;Lightning strike damage images of CFRP laminates Part1&quot;.
Due to the complex and uncertain physics of lightning strike on carbon fiber-reinforced polymer (CFRP) laminates, conventional numerical simulation methods for assessing the residual strength of lightning-damaged CFRP laminates are highly time-consuming and far from pretty. To overcome these challenges, this study proposes a new prediction method for residual strength of CFRP laminates based on machine learning. A diverse dataset is gained and augmented from photographs of lightning strike damage areas, C-scan images, mechanical performance data, layup details, and lightning current parameters. </dc:description>
    <dc:subject>Fiber-Reinforced Composite</dc:subject>
    <dc:subject>Residual Strength</dc:subject>
    <dc:subject>Injury Due to Lightning</dc:subject>
    <dc:type>Dataset</dc:type>
    <dc:identifier>doi:10.17632/r8d69gst88.1</dc:identifier>
    <dc:identifier>oai:data.mendeley.com/r8d69gst88.1</dc:identifier>
    <dc:rights>Creative Commons Attribution 4.0 International</dc:rights>
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    <dc:date>2024-09-12T10:33:17Z</dc:date>
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