• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.2
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• pp.220-227
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• 2002

This paper describes an integrated approach process to carry out pre-test, model correlation and updating analysis on the sub-frame of a vehicle. In this study, it was found that the modal test could be more efficient when the exciting point was selected on the area with high driving point residue. Such area could be located with the aid of finite element modal analysis. The model correlation was appraised in conjunction with the modal parameters between modal test and finite elements analysis. Also, the finite element model updating was obtained the good resultant using the iteration method based on sensitivity analysis results that carried out the variation of natural frequencies and MAC for the material properties. Finally, optimization of vehicle subframe was carried out the analysis of core location and physical properties by tow steps.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.18 no.4
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• pp.337-347
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• 2009

Generally, finite element models used in structural analysis have some uncertainties of the geometric dimensions, applied loads and boundary conditions, as well as in material properties due to the manufacturability of aluminum intensive body. Therefore, it is very important to refine or update a finite element model by correlating it with dynamic and static tests. The structural optimization problems of automotive body are considered for mechanical structures with initial stiffness due to preloading and in operation condition or manufacturing. As the mean compliance and deflection under preloading are chosen as the objective function and constraints, their sensitivities must be derived. The optimization problem is iteratively solved by a sequential convex approximation method in the commercial software. The design variables are corrected by the strain energy scale factor in the element levels. This paper presents an updated method based on the sensitivities of structural responses and the residual error vectors between experimental and simulation models.

• Structural Monitoring and Maintenance
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• v.9 no.1
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• pp.59-80
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• 2022

In the present study, the objective is to detect the structural damages using the responses obtained from the sensors at the optimal location under uncertainty conditions. Reducing the error rate in damage detection process due to responses' noise is an important goal in this study. In the proposed algorithm for optimal sensor placement, the noise of responses recorded from the sensors is initially reduced using the principal component analysis. Afterward, the optimal sensor placement is obtained by the damage detection equation based sensitivity analysis. The sensors are placed on degrees of freedom corresponding to the minimum error rate in structural damage detection through this procedure. The efficiency of the proposed method is studied on a truss bridge, a space dome, a double-layer grid as well as a three-story experimental frame structure and the results are compared. Moreover, the performance of the suggested method is compared with three other algorithms of Average Driving Point Residue (ADPR), Effective Independence (EI) method, and a mass weighting version of EI. In the examples, young's modulus, density, and cross-sectional areas of the elements are considered as uncertainty parameters. Ultimately, the results have demonstrated that the presented algorithm under uncertainty conditions represents a high accuracy to obtain the optimal sensor placement in the structures.