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.
Skip Nav Destination
Article navigation
March 2017
Research-Article
Computation of Midsurface by Feature-Based Simplification–Abstraction–Decomposition
Yogesh H. Kulkarni,
Yogesh H. Kulkarni
Department of Mechanical Engineering,
College of Engineering Pune,
Pune 411005, Maharashtra, India
e-mail: kulkarniyh12.mech@coep.ac.in
College of Engineering Pune,
Pune 411005, Maharashtra, India
e-mail: kulkarniyh12.mech@coep.ac.in
Search for other works by this author on:
Anil Sahasrabudhe,
Anil Sahasrabudhe
Department of Mechanical Engineering,
College of Engineering Pune,
Pune 411005, Maharashtra, India
e-mail: anil.sahasrabudhe@gmail.com
College of Engineering Pune,
Pune 411005, Maharashtra, India
e-mail: anil.sahasrabudhe@gmail.com
Search for other works by this author on:
Mukund Kale
Mukund Kale
Search for other works by this author on:
Yogesh H. Kulkarni
Department of Mechanical Engineering,
College of Engineering Pune,
Pune 411005, Maharashtra, India
e-mail: kulkarniyh12.mech@coep.ac.in
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
College of Engineering Pune,
Pune 411005, Maharashtra, India
e-mail: anil.sahasrabudhe@gmail.com
Mukund Kale
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. Mar 2017, 17(1): 011006 (13 pages)
Published Online: November 7, 2016
Article history
Received:
January 16, 2016
Revised:
July 11, 2016
Citation
Kulkarni, Y. H., Sahasrabudhe, A., and Kale, M. (November 7, 2016). "Computation of Midsurface by Feature-Based Simplification–Abstraction–Decomposition." ASME. J. Comput. Inf. Sci. Eng. March 2017; 17(1): 011006. https://doi.org/10.1115/1.4034130
Download citation file:
Get Email Alerts
Cited By
Digital Twins and Civil Engineering Phases: Reorienting Adoption Strategies
J. Comput. Inf. Sci. Eng (October 2024)
Network Analysis of Two-Stage Customer Decisions with Preference-Guided Market Segmentation
J. Comput. Inf. Sci. Eng
A Framework of Real-Time Knowledge Capture and Formalization for Model-Based Design With Spoken Annotation and Design Operations
J. Comput. Inf. Sci. Eng (October 2024)
Data Privacy Preserving for Centralized Robotic Fault Diagnosis With Modified Dataset Distillation
J. Comput. Inf. Sci. Eng (October 2024)
Related Articles
Integrated, Synchronous Multi-User Design and Analysis
J. Comput. Inf. Sci. Eng (September,2015)
A Computer-Aided Design Module to Analyze Manufacturing Configurations of Bent and Hydroformed Tubes
J. Manuf. Sci. Eng (October,2007)
FEAsy: A Sketch-Based Tool for Finite Element Analysis
J. Comput. Inf. Sci. Eng (September,2017)
Identification of Similar and Complementary Subparts in B-Rep Mechanical Models
J. Comput. Inf. Sci. Eng (December,2017)
Related Proceedings Papers
Related Chapters
CAD/CAE Simulation Optimization
Taguchi Methods: Benefits, Impacts, Mathematics, Statistics and Applications
Datum Targets
Geometric Dimensioning and Tolerancing Handbook: Applications, Analysis & Measurement
Datum Targets
Geometric Dimensioning and Tolerancing: Applications, Analysis, Gauging and Measurement [per ASME Y14.5-2018]