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    <responseDate>2026-10-12T21:27:34Z</responseDate>
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    <identifier>10.57760/sciencedb.29073</identifier>
    <datestamp>2025-08-15T09:15:24Z</datestamp>
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  <dc:date>2025-08-15</dc:date>
  <dc:title>Multi-track Multi-layer Laser Additive Manufacturing Quality Monitoring Dataset</dc:title>
  <dc:identifier>doi:10.57760/sciencedb.29073</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>Real-time visual data on molten pool dynamics, spatters, and plumes are captured using a CF8/5 MX high-speed CCD camera, operating at 768 &amp;times; 494 resolution and 50 frames per second, positioned coaxially with the laser beam. To construct the dataset, 48 multi-track and multi-layer specimens were fabricated using 30CrNi2MoVA steel powder under varied process parameters. The folder &amp;quot;pic_all&amp;quot; contains all the CCD image data collected during the printing process of these 48 sample sets, with each experimental file named according to the format &amp;quot;Power-Speed&amp;quot;.A local-to-local spatiotemporal mapping strategy was adopted to generate a labeled dataset, leveraging multi-track, multi-layer melt pool image sequences to characterize the localized porosity states within the part. Specifically, a 0.4-second sampling time window was employed to continuously extract molten pool image data along the temporal axis of the laser AM process, while simultaneously sliding along the specimen&amp;rsquo;s length to annotate the corresponding input data with the total volume of local defects within each spatial window. Each sample corresponds to the data from a single 0.4-second sampling window. This approach preserves the local dynamic variations of the molten pool and establishes a spatiotemporal correlation between melt pool evolution and defect formation. The final dataset consists of 4360 samples, each comprising a sequence of 1800 melt pool images (20 images per track across 3 tracks and 30 layers). Therefore, the folder &amp;quot;pic_all_txt_4360&amp;quot; contains the image names corresponding to each sample.Based on the total pore volume within the corresponding sample regions as determined by X-ray CT, these samples were classified into three defect size&amp;ndash;related states: negligible (label 0), medium-sized (label 1), and large-sized (label 2). The file &amp;quot;all_samples_3_4360_V.xlsx&amp;quot; contains the three-class labels, with the first column as &amp;quot;Sample_ID&amp;quot;, the second column as &amp;quot;Total_Volume&amp;quot;, and the third column as &amp;quot;label&amp;quot;.</dc:description>
  <dc:subject>Laser Additive Manufacturing; Multi-track Multi-layer; Online monitoring; In-situ defect prediction</dc:subject>
  <dc:creator>Kaiyu Niu</dc:creator>
  <dc:rights>RESTRICTED</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:relation>http://www.doi.org/10.1016/j.amf.2026.200325</dc:relation>
  <dc:publisher>Science Data Bank</dc:publisher>
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