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arXiv · 2201.10257

PREVIS -- A Combined Machine Learning and Visual Interpolation Approach for Interactive Reverse Engineering in Assembly Quality Control

Patrick Ruediger·Felix Claus·Viktor Leonhardt·Hans Hagen·Jan C. Aurich·Christoph Garth
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Abstract

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.

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Patrick Ruediger, Felix Claus, Viktor Leonhardt, Hans Hagen, Jan C. Aurich, Christoph Garth. 2022-01-25. PREVIS -- A Combined Machine Learning and Visual Interpolation Approach for Interactive Reverse Engineering in Assembly Quality Control. https://doi.org/10.1016/j.procir.2022.09.053

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