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Research Papers

Efficient Contact Modeling in Nonrigid Variation Simulation

[+] Author and Article Information
Björn Lindau

Department 81720, Geopl. PVÖE 101,
Volvo Cars,
Göteborg SE-405 31, Sweden
e-mail: bjorn.lindau@volvocars.com

Samuel Lorin

Mem. ASME
Fraunhofer-Chalmers Centre for
Industrial Mathematics,
Chalmers University of Technology
Chalmers Science Park,
Göteborg SE-412 88, Sweden
e-mail: samuel.lorin@fcc.chalmers.se

Lars Lindkvist

Associate Professor
Department of Product and
Production Development,
Chalmers University of Technology,
Göteborg SE-412 96, Sweden
e-mail: lali@chalmers.se

Rikard Söderberg

Mem. ASME
Professor
Department of Product and
Production Development,
Chalmers University of Technology,
Göteborg SE-412 96, Sweden
e-mail: rikard.soderberg@chalmers.se

1Corresponding author.

Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received September 18, 2014; final manuscript received November 7, 2015; published online January 4, 2016. Editor: Bahram Ravani.

J. Comput. Inf. Sci. Eng 16(1), 011002 (Jan 04, 2016) (7 pages) Paper No: JCISE-14-1284; doi: 10.1115/1.4032077 History: Received September 18, 2014; Revised November 07, 2015

Virtual tools and methods are becoming increasingly important in order to predict the geometric outcome in early phases of the product realization process. Method of influence coefficients (MIC) in combination with Monte Carlo simulation (MCS) is a well-known technique that can be used in nonrigid variation simulation. In these simulations, contact modeling is important to ensure a correct result. Contact modeling simulates how mating surfaces are hindered from penetrating each other, giving rise to contact forces that contribute to the deformation of the parts when assembled and the final shape of the subassembly after springback. These contact forces have to be taken into consideration in each MCS-iteration. To secure reasonable response times, the calculation of the contact forces needs to be fast. In this paper, we formulate a quadratic programming (QP) problem to solve the contact problem. The case studies presented show that node-based contact modeling can be efficiently solved through QP.

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References

Figures

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Fig. 2

Variation simulation based on MIC

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Fig. 3

Contact modeling in combination with MIC

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Fig. 4

Contact modeling definitions

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Fig. 5

Simulated sheet metal assembly

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Fig. 6

Plot showing the initial part deviation

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Fig. 7

Defined weld points and the locators scheme for springback calculation

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Fig. 8

Rattle and squeak model, center-panel

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