Optimized Routine of Machining Distortion Characterization Based on Gaussian Surface Curvature

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



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Author Details

Destiny R. Garcia
  • Mechanical and Aerospace Engineering Department, University of California Davis, CA, USA
Barbara S. Linke
  • Mechanical and Aerospace Engineering Department, University of California Davis, CA, USA
Rida T. Farouki
  • Mechanical and Aerospace Engineering Department, University of California Davis, CA, USA

Acknowledgements

Thank you to Michael Hill, UC Davis for guiding the machining distortion research. Many thanks to Christopher D'Elia and Renan Ribeiro, both UC Davis, for designing and machining the samples. We express our sincere thanks to Jan Aurich, Benjamin Kirsch, and Daniel Weber at TU Kaiserslautern for discussing the distortion application. All above mentioned researchers are collaborators on the NSF/DFG Collaboration to Understand the Prime Factors Driving Distortion in Milled Aluminum Workpieces, funded through NSF Award No. 1663341 and DFG Project No. 351381681. We also thank Jörg Seewig from TU Kaiserslautern for the helpful discussions on metrology. We furthermore interact in the IRTG 2057 Physical Modeling for Virtual Manufacturing Systems and Processes, funded by DFG.

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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)
https://doi.org/10.4230/OASIcs.iPMVM.2020.5

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.

Subject Classification

ACM Subject Classification
  • Applied computing → Engineering
Keywords
  • Machining distortion
  • Metrology
  • Gaussian curvature

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