0
Research Papers

Variation Simulation of Stresses Using the Method of Influence Coefficients

[+] Author and Article Information
Samuel Lorin

e-mail: samuel.lorin@chalmers.se

Lars Lindkvist

e-mail: lali@chalmers.se

Rikard Söderberg

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

1Corresponding author.

Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINNERING. Manuscript received August 22, 2013; final manuscript received September 24, 2013; published online November 14, 2013. Editor: Bahram Ravani.

J. Comput. Inf. Sci. Eng 14(1), 011001 (Nov 14, 2013) (7 pages) Paper No: JCISE-13-1162; doi: 10.1115/1.4025632 History: Received August 22, 2013; Revised September 24, 2013

In every manufacturing situation there are geometric deviations leading to variation in properties of the manufactured products. Variation affects the manufacturability, functions and aesthetics of the products. Therefore, a number of methods and tools have been developed during the last 20 yr in order to assure the geometric quality and to minimize the effect of variability. These methods and tools have mainly been developed for rigid- or sheet metal components. Plastics or composites have been an increasingly popular material due to their flexible mechanical properties and their relative ease in manufacturing. However, their mechanical properties are introducing challenges that have not often been addressed in the process of geometry assurance. One challenge is to assure that the stresses introduced, as a consequence of non-nominal assembly, are kept well below critical limits during the conditions of use. In this paper, we are proposing the use of the method of influence coefficients (MIC) to simulate the distribution of von Mises stresses in assembled components. This method will be compared to the more flexible but computationally much heavier direct Monte Carlo (DMC) method, which is not suitable for variation simulation due to the large number of runs required for statistical inference. Two industrial case studies are presented to elicit the need of the proposed method.

FIGURES IN THIS ARTICLE
<>
Copyright © 2014 by ASME
Topics: Simulation , Stress
Your Session has timed out. Please sign back in to continue.

References

Söderberg, R., Lindkvist, L., and Dahlström, S., 2006, “Computer-Aided Robustness Analysis for Compliant Assemblies,” J. Eng. Des., 17(5), pp. 411–428. [CrossRef]
Chase, K. W., and Parkinson, A. R., 1991, “A Survey of Research in the Application of Tolerance Analysis to the Design of Mechanical Assemblies,” Res. Eng. Des., 3(1), pp. 23–37. [CrossRef]
Hong, Y., and Chang, T., 2002, “A Comprehensive Review of Tolerancing Research,” Int. J. Prod. Res., 40(11), pp. 2425–2459. [CrossRef]
Shah, J. J., Ameta, G., Shen, Z., and Davidson, J. K., 2007, “Navigating the Tolerance Analysis Maze,” Comput.-Aided Des Appl., 4(5), pp. 705–718.
Nigam, S. D., and Turner, J. U., 1995, “Review of Statistical Approaches to Tolerance Analysis,” Computer-Aided Des., 27(1), pp. 6–15. [CrossRef]
Maropoulos, P., and Ceglarek, D., 2010, “Design Verification and Validation in Product Lifecycle,” CIRP Ann., 59(2), pp. 740–759. [CrossRef]
McCrum, N. G., Buckley, C. P., and Bucknall, C. B., 2007, Principles of Polymer Engineering, Oxford Science Publication, Oxford, UK.
Chen, C., and Sauer, J., 1990, “Yield and Fracture Mechanisms in ABS,” J. Appl. Polym. Sci., 40(3–4), pp. 503–521. [CrossRef]
Vlahinos, A., and Kelker, S., 2001, “Body-in-White Weight Reduction Via Probabilistic Modeling of Manufacturing Variations,” SAE Technical Paper No. 2001-01-3044, 110(6), pp. 2304–2311 [CrossRef].
Vlahinos, A., and Kelkar, S., 2002, “Designing for Six-Sigma Quality With Robust Optimization Using CAE,” 2002 International Body Engineering Conference.
Söderberg, R., Lindkvist, L., and Carlson, J., 2006, “Virtual Geometry Assurance for Effective Product Realization,” First Nordic Conference on Product Lifecycle Management-NordPLM’06.
Liu, S. C., and Hu, S. J., 1997, “Variation Simulation for Deformable Sheet Metal Assemblies Using Finite Element Methods,” ASME J. Manuf. Sci. Eng., 119, pp. 368–374. [CrossRef]
Camelio, J. A., Hu, J. S., and Ceglarek, D., 2003, “Modeling Variation Propagation of Multi-Station Assembly System With Compliant Parts,” ASME J. Mech. Des., 125(4), pp. 673–682. [CrossRef]
Camelio, J. A., Hu, J. S., and Ceglarek, D., 2002, “Impact of Fixture Design on Sheet Metal Assembly Variation,” J. Manuf. Syst., 23(3), pp. 182–192. [CrossRef]
Söderberg, R., and Lindkvist, L., 1999, “Computer Aided Assembly Robustness Evaluation,” J. Eng. Des., 10(2), pp. 165–181. [CrossRef]
Lindkvist, L., and Söderberg, R., 2003, “Computer-Aided Tolerance Chain and Stability Analysis,” J. Eng. Des., 14(1), pp. 17–39. [CrossRef]
Lorin, S., Söderberg, R., Lindkvist, L., and Sandboge, R., 2013, “Combining Variation Simulation With Thermal Expansion for Geometry Assurance,” Journal of Computing and Information Science in Engineering, 13(3), p. 031007 [CrossRef].

Figures

Grahic Jump Location
Fig. 1

Relations between displacements, strains, and stresses

Grahic Jump Location
Fig. 2

The positioning system for the component used for comparing DMC and MIC. The red arrows denote the 3–2–1 positioning system and the two blue arrows denote the 2 support points

Grahic Jump Location
Fig. 3

The von Mises stress in the 4 points implied by the arrows is recorded for DMC and MIC

Grahic Jump Location
Fig. 4

Comparison between DMC and MIC for von Mises stress in four measures (MPa)

Grahic Jump Location
Fig. 5

A plastic appliqué (in blue) and the corresponding positioning system

Grahic Jump Location
Fig. 6

Color-coding of the mean values of the induced von Mises Stresses (MPa)

Grahic Jump Location
Fig. 7

Color-coding of the 6 standard deviations of von Mises Stresses (MPa)

Grahic Jump Location
Fig. 8

The position of 3 measures used to record the distribution of von Mises Stresses

Grahic Jump Location
Fig. 9

The distributions of measure 1–3 from case study 1

Grahic Jump Location
Fig. 10

In case 2, the appliqué is hindered to expand with temperature along the y-direction by locking the positioned appliqué in the y-direction in the positions indicated by the circles

Grahic Jump Location
Fig. 11

The distributions of measure 1–3 from case study 2 in −30  °C

Grahic Jump Location
Fig. 12

The distributions of measure 1–3 from case study 2 in 60  °C

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In