Machining Quality Analysis of Powertrain Components Using Plane Strain Finite Element Cutting Models

Authors Abstract
Content
Finite Element Analysis (FEA) of metal cutting is largely the domain of research organizations. Despite significant advances towards accurately modelling metal machining processes, industrial adoption of these advances has been limited. Academic studies, which mainly focused on orthogonal cutting, fail to address this discrepancy. This article bridges the gap between simplistic orthogonal cutting models and the complex components typical in the manufacturing sector. This article outlines how to utilize results from orthogonal cutting simulations to predict industrially relevant performance measures efficiently. In this approach, using 2D FEA cutting models a range of feed, speed and rake angles are simulated. Cutting force coefficients are then fit to the predicted cutting forces. Using these coefficients, forces for 3D cutting geometries are calculated. In order to predict part behavior during cutting, these predicted forces are used as an input to 3D FEA models of the part and fixture. This approach allows 3D part deflections to be calculated, without the computational expense and uncertainty of a 3D cutting model. Since this approach does not require experiments to define the cutting force coefficients, the approach is well suited to the early design stages of components, when physical parts and tooling are not yet available. Compared to a complete 3D cutting model, a significant reduction in simulation time is shown as well as much better correlation to experimental values. Ford Motor Company’s Digital Manufacturing group uses this approach to bridge the gap between well-developed 2D cutting models, and the realities of machining complex prismatic components.
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DOI
https://doi.org/10.4271/05-11-02-0012
Pages
10
Citation
Ziada, Y., and Yang, J., "Machining Quality Analysis of Powertrain Components Using Plane Strain Finite Element Cutting Models," SAE Int. J. Mater. Manf. 11(2):113-122, 2018, https://doi.org/10.4271/05-11-02-0012.
Additional Details
Publisher
Published
May 7, 2018
Product Code
05-11-02-0012
Content Type
Journal Article
Language
English