Identification of Heterogeneous Constitutive Parameters in a Welded Specimen: Uniform Stress and Virtual Fields Methods for Material Property Estimation
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Local strain data obtained throughout the entire weld region encompassing both the weld nugget and heat affected zones (HAZs) are processed using two methodologies, uniform stress and virtual fields, to estimate specific heterogeneous material properties throughout the weld zone. Results indicate that (a) the heterogeneous stress–strain behavior obtained by using a relatively simple virtual fields model offers a theoretically sound approach for modeling stress–strain behavior in heterogeneous materials, (b) the local stress–strain results obtained using both a uniform stress assumption and a simplified uniaxial virtual fields model are in good agreement for strains ɛ xx < 0.025, (c) the weld nugget region has a higher hardening coefficient, higher initial yield stress and a higher hardening exponent, consistent with the fact that the steel weld is overmatched and (d) for ɛ xx > 0.025, strain localization occurs in the HAZ region of the specimen, resulting in necking and structural effects that complicate the extraction of local stress strain behavior using either of the relatively simple models.
KeywordsMaterial parameter identification Heterogeneous weld zone Virtual fields method Computer vision Digital image correlation
The authors gratefully acknowledge the support of (a) the Army Research Office and Dr. Bruce Lamattina through W911NF-06-1-0216, (b) Dr. Stephen Smith and NASA Langley under Grant NASA-NRA-NNX07AB46A and (c) General Motors and Dr. Pablo Zavattieri through both contractual support ND0144200 and an unrestricted gift 002259249. The technical assistance provided by Prof. Xiaomin Deng in the Department of Mechanical Engineering at the University of South Carolina and Dr. Hubert Schreier and Correlated Solutions Incorporated for supporting this effort through modifications to their software, VIC-2D, for our use is deeply appreciated.
- 1.Lockwood WD, Reynolds AP (2003) Simulation of the global response of a friction stir weld using local constitutive behavior. Mater Sci Eng A V3391–2:35–42.Google Scholar
- 2.Peters WH, Ranson WF (1981) Digital imaging techniques in experimental stress analysis. Opt Eng 213:427–432.Google Scholar
- 5.Anderson J, Peters WH, Sutton MA, Ranson WF, Chu TC (1984) Application of digital correlation methods to rigid body mechanics. Opt Eng 226:238–743.Google Scholar
- 7.Sutton MA, McNeill SR, Jang J, Babai M (1988) The effects of subpixel image restoration on digital correlation error estimates. Opt Eng 273:173–175.Google Scholar
- 10.Sutton MA, Bruck HA, Chae TL, Turner JL (1990) Development of a computer vision methodology for the analysis of surface deformations in magnified images. ASTM STP-1094; MICON-90. Advances in video technology for micro-structural evaluation of materials 109–134.Google Scholar
- 11.VIC2D, Correlated Solutions Incorporated, 120 Kaminer Way, Parkway Suite A, Columbia, SC 29210 www.correlatedsolutions.com.
- 15.Lockwood WD, Tomaz B, Reynolds AP (2002) Mechanical response of friction stir welded AA2024: experiment and modeling. Mater Sci Eng A323:348–353.Google Scholar
- 16.Reynolds AP, Duvall F (1999) Welding J 7810:355s–360s.Google Scholar
- 17.Adeeb SM, Horsley D, Yan J-H, Sutton MA, Reynolds AP. Local stress–strain response of an axial x100 girth weld under tensile loading using digital image correlation. Proceedings of the 2006 International Pipeline Conference, September 25–29, Calgary, Alberta, Canada, IPC2006-10330.Google Scholar
- 24.Avril S, Pierron F, Yan J, Sutton MA (2007) Identification of viscoplastic parameters using DIC and the virtual fields method. Proceedings of the SEM Conference and Exposition, Springfield, USA.Google Scholar
- 28.Hart JD, Powell GH, Zulfigar N (1996) A material model for pipeline steels. International Pipeline Conference ASME 2:613–627.Google Scholar