论文标题
PREVIS - 用于组装质量控制的互动反向工程的机器学习和视觉插值方法
PREVIS -- A Combined Machine Learning and Visual Interpolation Approach for Interactive Reverse Engineering in Assembly Quality Control
论文作者
论文摘要
我们介绍了一种视觉分析工具Previs，并在工程应用中增强了机器学习绩效分析。提出的工具链可以直接比较回归模型。此外，我们还提供了一种方法，以通过利用标准插值方法来可视化回归误差对原始域（部分几何形状）感兴趣领域的影响。此外，我们允许通过视觉插值实时预览位移字段中用户驱动的参数更改。这允许快速，负责任的在线变更管理。我们通过对汽车引擎罩的前优化进行了优化，证明了有效性。
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.
