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Research Papers

Computation of Midsurface by Feature-Based Simplification–Abstraction–Decomposition

[+] Author and Article Information
Yogesh H. Kulkarni

Department of Mechanical Engineering,
College of Engineering Pune,
Pune 411005, Maharashtra, India
e-mail: kulkarniyh12.mech@coep.ac.in

Anil Sahasrabudhe

Department of Mechanical Engineering,
College of Engineering Pune,
Pune 411005, Maharashtra, India
e-mail: anil.sahasrabudhe@gmail.com

Mukund Kale

Siemens PLM,
Pune 411057, Maharashtra, India
e-mail: mukund_kale@hotmail.com

1Corresponding author.

Contributed by the Computers and Information Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received January 16, 2016; final manuscript received July 11, 2016; published online November 7, 2016. Assoc. Editor: Jitesh H. Panchal.

J. Comput. Inf. Sci. Eng 17(1), 011006 (Nov 07, 2016) (13 pages) Paper No: JCISE-16-1023; doi: 10.1115/1.4034130 History: Received January 16, 2016; Revised July 11, 2016

Computer-aided design (CAD) models of thin-walled solids such as sheet metal or plastic parts are often reduced dimensionally to their corresponding midsurfaces for quicker and fairly accurate results of computer-aided engineering (CAE) analysis. Computation of the midsurface is still a time-consuming and mostly, a manual task due to lack of robust and automated techniques. Most of the existing techniques work on the final shape (typically in the form of boundary representation, B-rep). Complex B-reps make it hard to detect subshapes for which the midsurface patches are computed and joined, forcing usage of hard-coded heuristic rules, developed on a case-by-case basis. Midsurface failures manifest in the form of gaps, overlaps, nonmimicking input model, etc., which can take hours or even days to correct. The research presented here proposes to address these problems by leveraging feature-information available in the modern CAD models, and by effectively using techniques like simplification, abstraction, and decomposition. In the proposed approach, first, the irrelevant features are identified and removed from the input FbCAD model to compute its simplified gross shape. Remaining features then undergo abstraction to transform into their corresponding generic Loft-equivalents, each having a profile and a guide curve. The model is then decomposed into cellular bodies and a graph is populated, with cellular bodies at the nodes and fully overlapping-surface-interfaces at the edges. The nodes are classified into midsurface-patch generating nodes (called “solid cells” or sCells) and interaction-resolving nodes (“interface cells” or iCells). In a sCell, a midsurface patch is generated either by offset or by sweeping the midcurve of the owner-Loft-feature's profile along with its guide curve. Midsurface patches are then connected in the iCells in a generic manner, thus resulting in a well-connected midsurface with minimum failures. Output midsurface is then validated topologically for correctness. At the end of this paper, real-life parts are used to demonstrate the efficacy of the proposed approach.

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Figures

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Fig. 1

Midsurface with errors (Aparicio [5]. Copyright 2015 by MSC Software Corporation. Used with permission.)

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Fig. 2

Expectations about midsurfaces: (a) model, (b) gradual, (c) mimicking, and (d) disjoint

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Fig. 3

Feature-based cellular midsurface

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Fig. 4

Medial computation techniques

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Fig. 6

Sheet metal features taxonomy (icons source: Autodesk Inventor [28])

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Fig. 8

Phase II: remnant feature volumes: (a) remnant and consumed portions of feature f2 and (b) formation of clusters

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Fig. 10

Abstraction/generalization

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Fig. 11

Generic loft feature

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Fig. 12

Feature-based cellular decomposition: (a) model, (b) cells, (c) before decomposition, and (d) after decomposition

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Fig. 14

Midsurface patches: (a) patches, 2D view and (b) patches, 3D view

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Fig. 15

Improvement over Bayazit's algorithm

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Fig. 16

Midcurves segment joining

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Fig. 17

Midcurves benchmarking

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Fig. 18

Resolving interactions in the iCell: (a) adjustments, (b) sCell–iCell, and (c) iCell–iCell

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Fig. 19

iCell resolving in overlap case: (a) before resolving and (b) after resolving

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Fig. 20

Computation of midsurface of a bracket: (a) dormant piercing and (b) final output

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Fig. 22

Edge classification

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Fig. 23

Comparison of midsurface outputs: (a) original part and (b) commercial part

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Fig. 27

Midsurface computation: (a) semi-final midsurface and (b) dormant piercing

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Fig. 28

Input and output comparison: (a) original part and (b) this research

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Fig. 29

The rectangular clip bracket part

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Fig. 30

Input the U bracket clip part

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