{"id":3304,"date":"2022-07-13T09:30:00","date_gmt":"2022-07-13T07:30:00","guid":{"rendered":"https:\/\/cirpicme.org\/?page_id=3304"},"modified":"2022-07-26T13:07:28","modified_gmt":"2022-07-26T11:07:28","slug":"deep-learning-based-predictive-testing-strategy-in-the-automotive-industry","status":"publish","type":"page","link":"https:\/\/cirpicme.org\/index.php\/quality-metrology-testing\/deep-learning-based-predictive-testing-strategy-in-the-automotive-industry\/","title":{"rendered":"Deep Learning based Predictive Testing Strategy in the Automotive Industry"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><em>by Andreas Schoch, Robert Refflinghaus, Patrick Zivkovic (Germany)<\/em><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Abstract<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Deep neural networks revolutionized the field of artificial intelligence by successfully advancing the progress in complex problems. One such complex challenge in the automotive industry is the prediction of quality assurance tests in the vehicle assembly process. This paper presents a theoretical framework based on deep neural networks to predict the outcome of a quality assurance tests using vehicle configuration and process data. The insight gained by those predictions can be used to design highly individual and dynamic quality assurance tests. By applying the framework testing efficiency can be increased by 15%.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Keywords<\/strong>: Automotive Industry; Deep Learning; Quality Mangagement; Quality Assurance Tests; Predictive Testing<\/p>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Video presentation<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-video\"><video height=\"720\" style=\"aspect-ratio: 1280 \/ 720;\" width=\"1280\" controls src=\"https:\/\/cirpicme.org\/wp-content\/uploads\/2022\/06\/Andreas_Schoch.mp4\"><\/video><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Presenting author<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-table is-style-stripes\"><table class=\"has-background\" style=\"background-color:#f3f4f5\"><tbody><tr><td><\/td><td><\/td><td><\/td><\/tr><tr><td><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"150\" height=\"85\" class=\"wp-image-2748\" style=\"width: 150px;\" src=\"https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/06\/Andreas-Schoch-NVIDIA.jpeg?resize=150%2C85&#038;ssl=1\" alt=\"\" srcset=\"https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/06\/Andreas-Schoch-NVIDIA.jpeg?w=600&amp;ssl=1 600w, https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/06\/Andreas-Schoch-NVIDIA.jpeg?resize=300%2C169&amp;ssl=1 300w, https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/06\/Andreas-Schoch-NVIDIA.jpeg?resize=150%2C85&amp;ssl=1 150w\" sizes=\"auto, (max-width: 150px) 100vw, 150px\" \/><\/td><td><strong>Name:<\/strong><br><br><strong>Affiliation:<\/strong><br><br><strong>Email:<\/strong><\/td><td>Andreas Schoch<br><br>University of Kassel, Germany<br><br>andreas.schoch@bmw.de<\/td><\/tr><tr><td><\/td><td><\/td><td><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>by Andreas Schoch, Robert Refflinghaus, Patrick Zivkovic (Germany) Abstract Deep neural networks revolutionized the field of artificial intelligence by successfully advancing the progress in complex problems. One such complex challenge in the automotive industry is the prediction of quality assurance tests in the vehicle assembly process. This paper presents a&#8230;<\/p>\n<p> <a class=\"continue-reading-link\" href=\"https:\/\/cirpicme.org\/index.php\/quality-metrology-testing\/deep-learning-based-predictive-testing-strategy-in-the-automotive-industry\/\"><span>Continue reading<\/span><i class=\"crycon-right-dir\"><\/i><\/a> <\/p>\n","protected":false},"author":4,"featured_media":0,"parent":3668,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","meta":{"nf_dc_page":"","om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"class_list":["post-3304","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3304","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/comments?post=3304"}],"version-history":[{"count":2,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3304\/revisions"}],"predecessor-version":[{"id":3900,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3304\/revisions\/3900"}],"up":[{"embeddable":true,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3668"}],"wp:attachment":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/media?parent=3304"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}