2 Search Results for "D’Elia, Christopher R."

Document
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-dev.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}
}
Document
DOI: 10.4230/OASIcs.iPMVM.2020.11
Finite Element Simulation Combination to Predict the Distortion of Thin Walled Milled Aluminum Workpieces as a Result of Machining Induced Residual Stresses

Authors: Daniel Weber, Benjamin Kirsch, Christopher R. Chighizola, Julianne E. Jonsson, Christopher R. D’Elia, Barbara S. Linke, Michael R. Hill, and Jan C. Aurich

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 induced residual stresses (MIRS) are a main driver for distortion of monolithic thin walled aluminum workpieces. A typical machining process for manufacturing such geometries for the aerospace industry is milling. In order to avoid high costs due to remanufacturing or part rejection, a simulation combination, consisting of two different finite element method (FEM) models, is developed to predict the part distortion due to MIRS. First, a 3D FEM cutting simulation is developed to predict the residual stresses due to machining. This simulation avoids cost intensive residual stress measurements. The milling process of the aluminum alloy AA7050-T7451 with a regular end mill is simulated. The simulation output, MIRS, forces and temperatures, is validated by face milling experiments on aluminum. The model takes mechanical dynamic effects, thermomechanical coupling, material properties and a damage law into account. Second, a subsequent finite element simulation, characterized by a static, linear elastic model, where the simulated MIRS from the cutting model are used as an input and the distortion of the workpiece is calculated, is presented. The predicted distortion is compared to an additional experiment, where a 1 mm thick wafer was removed at the milled surface of the aluminum workpiece. Furthermore, a thin walled component that represents a down scaled version of an aerospace component is manufactured and its distortion is analyzed. The results show that MIRS could be forecasted with moderate accuracy, which leads to the conclusion that the FEM cutting model needs to be improved in order to use the MIRS for a correct prediction of the distortion with the help of the linear elastic FEM model. The linear elastic model on the other hand is able to predict the part distortion with higher accuracy when using measured data instead of MIRS from the cutting simulation.
Cite as

Daniel Weber, Benjamin Kirsch, Christopher R. Chighizola, Julianne E. Jonsson, Christopher R. D’Elia, Barbara S. Linke, Michael R. Hill, and Jan C. Aurich. Finite Element Simulation Combination to Predict the Distortion of Thin Walled Milled Aluminum Workpieces as a Result of Machining Induced Residual Stresses. 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. 11:1-11:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{weber_et_al:OASIcs.iPMVM.2020.11,
  author =	{Weber, Daniel and Kirsch, Benjamin and Chighizola, Christopher R. and Jonsson, Julianne E. and D’Elia, Christopher R. and Linke, Barbara S. and Hill, Michael R. and Aurich, Jan C.},
  title =	{{Finite Element Simulation Combination to Predict the Distortion of Thin Walled Milled Aluminum Workpieces as a Result of Machining Induced Residual Stresses}},
  booktitle =	{2nd International Conference of the DFG International Research Training Group 2057 – Physical Modeling for Virtual Manufacturing (iPMVM 2020)},
  pages =	{11:1--11:21},
  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-dev.dagstuhl.de/entities/document/10.4230/OASIcs.iPMVM.2020.11},
  URN =		{urn:nbn:de:0030-drops-137604},
  doi =		{10.4230/OASIcs.iPMVM.2020.11},
  annote =	{Keywords: Machining induced residual stresses, distortion, Finite element method simulation}
}
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