{"id":4312,"date":"2023-09-28T10:28:34","date_gmt":"2023-09-28T15:28:34","guid":{"rendered":"https:\/\/cqfa.quebec\/?p=4312"},"modified":"2023-09-28T10:34:32","modified_gmt":"2023-09-28T15:34:32","slug":"evaluation-of-key-spatiotemporal-learners-for-print-track-anomaly-classification-using-melt-pool-image-streams","status":"publish","type":"post","link":"https:\/\/cqfa.quebec\/en\/evaluation-of-key-spatiotemporal-learners-for-print-track-anomaly-classification-using-melt-pool-image-streams\/","title":{"rendered":"Evaluation of Key Spatiotemporal Learners for Print Track Anomaly Classification Using Melt Pool Image Streams"},"content":{"rendered":"<p><em><span class=\"TextRun SCXW95173189 BCX0\" lang=\"FR-CA\" xml:lang=\"FR-CA\" data-contrast=\"auto\"><span class=\"NormalTextRun SCXW95173189 BCX0\">Cherif, L.; <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW95173189 BCX0\">Safdar<\/span><span class=\"NormalTextRun SCXW95173189 BCX0\">, M.; <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW95173189 BCX0\">Lamouche<\/span><span class=\"NormalTextRun SCXW95173189 BCX0\">, G.; <\/span><span class=\"NormalTextRun SCXW95173189 BCX0\">Wanjara<\/span><span class=\"NormalTextRun SCXW95173189 BCX0\">, P.; Paul, P.; Wood, G.; Zimmermann, M.; <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW95173189 BCX0\">Hannesen<\/span><span class=\"NormalTextRun SCXW95173189 BCX0\">, F.; Zhao, Y.F. (2023). <\/span><\/span><span class=\"TextRun Highlight SCXW95173189 BCX0\" lang=\"EN-CA\" xml:lang=\"EN-CA\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW95173189 BCX0\" data-ccp-charstyle=\"normaltextrun\" data-ccp-charstyle-defn=\"{&quot;ObjectId&quot;:&quot;a708ac02-62c9-492f-9ace-33c3dca10204|50&quot;,&quot;ClassId&quot;:1073872969,&quot;Properties&quot;:[469775450,&quot;normaltextrun&quot;,201340122,&quot;1&quot;,134233614,&quot;true&quot;,469778129,&quot;normaltextrun&quot;,335572020,&quot;1&quot;,469778324,&quot;Default Paragraph Font&quot;]}\">Evaluation of Key Spatiotemporal Learners for Print Track Anomaly Classification Using Melt Pool Image Streams. Preprints of the 22<\/span><\/span><span class=\"TextRun Highlight SCXW95173189 BCX0\" lang=\"EN-CA\" xml:lang=\"EN-CA\" data-contrast=\"none\"><span class=\"NormalTextRun Superscript SCXW95173189 BCX0\" data-fontsize=\"11\" data-ccp-charstyle=\"normaltextrun\">nd<\/span><\/span><span class=\"TextRun Highlight SCXW95173189 BCX0\" lang=\"EN-CA\" xml:lang=\"EN-CA\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW95173189 BCX0\" data-ccp-charstyle=\"normaltextrun\"> IFAC World Congress.<\/span><\/span><\/em><\/p>\n<p>&nbsp;<\/p>\n<p><span class=\"NormalTextRun SCXW250321475 BCX0\">Recent applications of machine learning in metal additive manufacturing (MAM) have <\/span><span class=\"NormalTextRun SCXW250321475 BCX0\">demonstrated<\/span><span class=\"NormalTextRun SCXW250321475 BCX0\"> significant potential in addressing critical barriers to the widespread adoption of MAM technology. Recent research in this field emphasizes the importance of <\/span><span class=\"NormalTextRun SCXW250321475 BCX0\">utilizing<\/span><span class=\"NormalTextRun SCXW250321475 BCX0\"> melt pool signatures for real-time defect prediction. While high-quality melt pool image data holds the promise of enabling precise predictions, there has been limited exploration into the <\/span><span class=\"NormalTextRun SCXW250321475 BCX0\">utilization<\/span><span class=\"NormalTextRun SCXW250321475 BCX0\"> of <\/span><span class=\"NormalTextRun SCXW250321475 BCX0\">cutting-edge<\/span><span class=\"NormalTextRun SCXW250321475 BCX0\"> spatiotemporal models that can harness the inherent transient and sequential characteristics of the additive manufacturing process. This research introduces and puts into practice some of the leading deep spatiotemporal learning models that can be adapted for the classification of melt pool image streams originating from various materials, systems, and applications. Specifically, it investigates two-stream networks <\/span><span class=\"NormalTextRun SCXW250321475 BCX0\">comprising<\/span><span class=\"NormalTextRun SCXW250321475 BCX0\"> spatial and temporal streams, a recurrent spatial network, and a factorized 3D convolutional neural network. The <\/span><span class=\"NormalTextRun SCXW250321475 BCX0\">capacity<\/span><span class=\"NormalTextRun SCXW250321475 BCX0\"> of these models to generalize when exposed to perturbations in melt pool image data is examined using data perturbation techniques grounded in real-world process scenarios. The implemented architectures <\/span><span class=\"NormalTextRun SCXW250321475 BCX0\">demonstrate<\/span><span class=\"NormalTextRun SCXW250321475 BCX0\"> the ability to capture the spatiotemporal features of melt pool image sequences. However, among these models, only the Kinetics400 pre-trained <\/span><span class=\"NormalTextRun SpellingErrorV2Themed SCXW250321475 BCX0\">SlowFast<\/span><span class=\"NormalTextRun SCXW250321475 BCX0\"> network, categorized as a two-stream network, <\/span><span class=\"NormalTextRun SCXW250321475 BCX0\">exhibits<\/span><span class=\"NormalTextRun SCXW250321475 BCX0\"> robust generalization capabilities in the presence of data perturbations.<\/span><\/p>\n<p>&nbsp;<\/p>\n<p class=\"link-btn-style btn-yellow\"><a href=\"https:\/\/arxiv.org\/ftp\/arxiv\/papers\/2308\/2308.14861.pdf\" target=\"_blank\" rel=\"noopener\">Read the publication<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Cherif, L.; Safdar, M.; Lamouche, G.; Wanjara, P.; Paul, P.; Wood, G.; Zimmermann, M.; Hannesen, F.; Zhao, Y.F. (2023). Evaluation of Key Spatiotemporal Learners for Print Track Anomaly Classification Using [&hellip;]<\/p>\n","protected":false},"author":80,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"footnotes":""},"categories":[43],"tags":[],"class_list":["post-4312","post","type-post","status-publish","format-standard","hentry","category-publications-academiques-quebecoises"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Evaluation of Key Spatiotemporal Learners for Print Track Anomaly Classification Using Melt Pool Image Streams - CQFA - Carrefour qu\u00e9b\u00e9cois de la fabrication additive<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/cqfa.quebec\/en\/evaluation-of-key-spatiotemporal-learners-for-print-track-anomaly-classification-using-melt-pool-image-streams\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Evaluation of Key Spatiotemporal Learners for Print Track Anomaly Classification Using Melt Pool Image Streams - CQFA - Carrefour qu\u00e9b\u00e9cois de la fabrication additive\" \/>\n<meta property=\"og:description\" content=\"Cherif, L.; Safdar, M.; Lamouche, G.; Wanjara, P.; Paul, P.; Wood, G.; Zimmermann, M.; Hannesen, F.; Zhao, Y.F. 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