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Documents authored by Farouki, Rida T.

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DOI: 10.4230/OASIcs.iPMVM.2020.5
Optimized Routine of Machining Distortion Characterization Based on Gaussian Surface Curvature

Authors: Destiny R. Garcia, Barbara S. Linke, and Rida T. Farouki

Published in: OASIcs, Volume 89, 2nd International Conference of the DFG International Research Training Group 2057 – Physical Modeling for Virtual Manufacturing (iPMVM 2020)

Abstract
Machining distortion presents a significant problem in products with high residual stresses from materials processing and re-equilibration after machining removes a large part of the material volume and is common in the aerospace industries. While many papers research on mechanisms of machining distortion, few papers report on the measurement, processing and characterization of distortion data. Oftentimes only line plot data is used to give a maximum distortion value. This paper proposes a method of measurement tool selection, measurement parameter selection, data processing through filtering and leveling, and use of Bézier Surfaces and Gaussian Curvature for distortion characterization. The method is demonstrated with three sample pieces of different pocket geometry from quenched aluminum. It is apparent that samples with machining distortion can have complex surface shapes, where Bézier Surfaces and Gaussian Curvature provide more information than the commonly used 2D line plot data.
Cite as

Destiny R. Garcia, Barbara S. Linke, and Rida T. Farouki. Optimized Routine of Machining Distortion Characterization Based on Gaussian Surface Curvature. In 2nd International Conference of the DFG International Research Training Group 2057 – Physical Modeling for Virtual Manufacturing (iPMVM 2020). Open Access Series in Informatics (OASIcs), Volume 89, pp. 5:1-5:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{garcia_et_al:OASIcs.iPMVM.2020.5,
  author =	{Garcia, Destiny R. and Linke, Barbara S. and Farouki, Rida T.},
  title =	{{Optimized Routine of Machining Distortion Characterization Based on Gaussian Surface Curvature}},
  booktitle =	{2nd International Conference of the DFG International Research Training Group 2057 – Physical Modeling for Virtual Manufacturing (iPMVM 2020)},
  pages =	{5:1--5:17},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-183-2},
  ISSN =	{2190-6807},
  year =	{2021},
  volume =	{89},
  editor =	{Garth, Christoph and Aurich, Jan C. and Linke, Barbara and M\"{u}ller, Ralf and Ravani, Bahram and Weber, Gunther H. and Kirsch, Benjamin},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.iPMVM.2020.5},
  URN =		{urn:nbn:de:0030-drops-137542},
  doi =		{10.4230/OASIcs.iPMVM.2020.5},
  annote =	{Keywords: Machining distortion, Metrology, Gaussian curvature}
}
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