{"id":1977,"date":"2021-07-14T09:30:00","date_gmt":"2021-07-14T07:30:00","guid":{"rendered":"http:\/\/cirpicme.org\/?page_id=1977"},"modified":"2021-07-13T18:57:28","modified_gmt":"2021-07-13T16:57:28","slug":"previs-a-combined-machine-learning-and-visual-interpolation-approach-for-interactive-reverse-engineering-in-assembly-quality-control","status":"publish","type":"page","link":"https:\/\/cirpicme.org\/index.php\/assembly-battery-production\/previs-a-combined-machine-learning-and-visual-interpolation-approach-for-interactive-reverse-engineering-in-assembly-quality-control\/","title":{"rendered":"PREVIS – A Combined Machine Learning and Visual Interpolation Approach for Interactive Reverse Engineering in Assembly Quality Control"},"content":{"rendered":"\n

by Patrick Ruediger, Felix Claus, Viktor Leonhardt, Hans Hagen, Jan C. Aurich, Christoph Garth<\/em><\/em> (Germany)<\/em><\/p>\n\n\n\n

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Abstract<\/strong><\/p>\n\n\n\n

We present PREVIS, a visual analytics tool, enhancing machine learning performance analysis in engineering applications. The presented toolchain allows for a direct comparison of regression models. In addition, we provide a methodology to visualize the impact of regression errors on the underlying field of interest in the original domain, the part geometry, via exploiting standard interpolation methods. Further, we allow a real-time preview of user-driven parameter changes in the displacement field via visual interpolation. This allows for fast and accountable online change management. We demonstrate the effectiveness with an ex-ante optimization of an automotive engine hood.<\/p>\n\n\n\n

Keywords<\/strong>: Explainable machine learning, Quality control, Reverse engineering, Assembly process, Visualization<\/p>\n\n\n\n

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Video presentation<\/strong><\/p>\n\n\n\n