{"id":3156,"date":"2022-07-13T09:30:00","date_gmt":"2022-07-13T07:30:00","guid":{"rendered":"https:\/\/cirpicme.org\/?page_id=3156"},"modified":"2022-07-26T13:07:28","modified_gmt":"2022-07-26T11:07:28","slug":"interpretation-framework-of-predictive-quality-models-for-process-and-product-oriented-decision-support","status":"publish","type":"page","link":"https:\/\/cirpicme.org\/index.php\/quality-metrology-testing\/interpretation-framework-of-predictive-quality-models-for-process-and-product-oriented-decision-support\/","title":{"rendered":"Interpretation Framework of Predictive Quality Models for Process- and Product-oriented Decision Support"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><em>by Daniel Buschmann, Tobias Schulze, Chrismarie Enslin, Robert H. Schmitt (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\">In the context of predictive quality in production, there is a need to explain and understand the predictive models used, as well as the dependencies present in the underlying data. For this purpose, we develop a framework for model-independent interpretation of predictive models to enable data-driven, process- and product-oriented decisions. This framework combines different model-agnostic interpretation methods and structures them into two modules, one for a process-oriented view and the other for a product-oriented view. In addition, an implementation concept for the two modules in a machine learning pipeline is also provided.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Keywords<\/strong>: Predictive Quality; Data-driven Decisions; Interpretable Machine Learning; Production Management; Quality Management<\/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=\"1080\" style=\"aspect-ratio: 1920 \/ 1080;\" width=\"1920\" controls src=\"https:\/\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Daniel_Buschmann.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=\"150\" class=\"wp-image-591\" style=\"width: 150px;\" src=\"https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2020\/07\/Daniel_Buschmann_Photo.jpg?resize=150%2C150&#038;ssl=1\" alt=\"\" srcset=\"https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2020\/07\/Daniel_Buschmann_Photo.jpg?w=550&amp;ssl=1 550w, https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2020\/07\/Daniel_Buschmann_Photo.jpg?resize=300%2C300&amp;ssl=1 300w, https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2020\/07\/Daniel_Buschmann_Photo.jpg?resize=150%2C150&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>Daniel Buschmann<br><br>RWTH Aachen University, Germany<br><br>d.buschmann@wzl.rwth-aachen.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 Daniel Buschmann, Tobias Schulze, Chrismarie Enslin, Robert H. Schmitt (Germany) Abstract In the context of predictive quality in production, there is a need to explain and understand the predictive models used, as well as the dependencies present in the underlying data. For this purpose, we develop a framework for&#8230;<\/p>\n<p> <a class=\"continue-reading-link\" href=\"https:\/\/cirpicme.org\/index.php\/quality-metrology-testing\/interpretation-framework-of-predictive-quality-models-for-process-and-product-oriented-decision-support\/\"><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-3156","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3156","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=3156"}],"version-history":[{"count":1,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3156\/revisions"}],"predecessor-version":[{"id":3157,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3156\/revisions\/3157"}],"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=3156"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}