Time filter

Source Type

Li G.,Hunan University | Li G.,Joint Center for Intelligent New Energy Vehicle | Jiang H.,Hunan University | Zhang X.,Hunan University of Science and Technology | And 2 more authors.
Journal of Manufacturing Processes | Year: 2017

Electromagnetic riveting (EMR) has gained increasing attention as a relatively new mechanical joining technique in automobile industry. In this paper, the mechanical properties and fatigue behavior of electromagnetic riveted lap joints are discussed systematically. The rivet deformation, microstructure and hardness distribution of the formed rivets were investigated, which were also compared with regular pressure riveting (RPR). The results of shear strength showed that there was almost no difference between EMR and RPR, and the fatigue performance of EMR was about 1–3 times higher than that of RPR at any cyclic stress level. Quasi-static fracture analysis showed that shear fracture occurred in rivet shaft and the rupture appearance of two processes was similar. For fatigue failure, there were two fatigue failure modes for both processes: rivet shaft fracture under a higher cyclic stress and manufactured head fracture under a lower cyclic stress. Under the higher cyclic stress level, there was no big difference between two processes in the fatigue appearance. However, the fatigue cracks propagation zone of EMR sample fracture was significantly wider than that of RPR under a lower cyclic stress level, indicating a higher fatigue life of EMR samples. © 2017


Ye F.,Hunan University | Ye F.,Joint Center for Intelligent New Energy Vehicle | Wang H.,Hunan University | Wang H.,Joint Center for Intelligent New Energy Vehicle | And 2 more authors.
Structural and Multidisciplinary Optimization | Year: 2017

In this work, a surrogate assisted optimization method is utilized to optimize buckling loads of variable stiffness composites made by fiber steering. To improve the efficiency of optimization procedure, an expected improvement criterion is employed. Moreover, considering uncertainties of the fiber placement, a robust surrogate, least square support vector regression (LSSVR) considering empirical and structural risks is integrated with the expected improvement (EI) criterion and applied to two applications. The first case is the fiber path design of a variable stiffness plate under the compression load. The second one is the fiber path design of a variable stiffness cylinder under the bending load. According to results of the optimization, the buckling load of the variable stiffness plate has 52.63% improvement than the constant stiffness plate and 24.3% improvement than the quasi-isotropic plate. The buckling load of the variable stiffness cylinder has 40.22% improvement than the constant stiffness cylinder and 31.25% improvement than the quasi-isotropic cylinder. Furthermore, to verify the robustness of optimal design variables for the variable stiffness cylinder, the perturbed optimum design is presented and demonstrates that the results are reliable. © 2017 Springer-Verlag Berlin Heidelberg


Wang H.,Hunan University | Wang H.,Joint Center for Intelligent New Energy Vehicle | Ye F.,Hunan University | Chen L.,Hunan University | Li E.,Central South University of forestry and Technology
Chinese Journal of Mechanical Engineering (English Edition) | Year: 2017

Surrogate assisted optimization has been widely applied in sheet metal forming design due to its efficiency. Therefore, to improve the efficiency of design and reduce the product development cycle, it is important for scholars and engineers to have some insight into the performance of each surrogate assisted optimization method and make them more flexible practically. For this purpose, the state-of-the-art surrogate assisted optimizations are investigated. Furthermore, in view of the bottleneck and development of the surrogate assisted optimization and sheet metal forming design, some important issues on the surrogate assisted optimization in support of the sheet metal forming design are analyzed and discussed, involving the description of the sheet metal forming design, off-line and online sampling strategies, space mapping algorithm, high dimensional problems, robust design, some challenges and potential feasible methods. Generally, this paper provides insightful observations into the performance and potential development of these methods in sheet metal forming design. © Chinese Mechanical Engineering Society and Springer-Verlag Berlin Heidelberg 2017


Huang G.,Hunan University | Huang G.,Joint Center for Intelligent New Energy Vehicle | Wang H.,Hunan University | Wang H.,Joint Center for Intelligent New Energy Vehicle | And 2 more authors.
Composite Structures | Year: 2016

An efficient reanalysis assisted optimization method is proposed for variable-stiffness composite design. The contour lines of functions can be used to describe fiber paths by using newly developed path functions. The parameters of path functions are used as design variables, and the initial objective function is evaluated by using Finite Element Method (FEM). The variable-stiffness composite laminate is formulated using FEM based on Mindlin shell theory. Manufacturing constraints are considered by examining the curvature and parallelism of fiber paths, therefore, the optimal solutions should be manufacturable. In order to improve the efficiency of optimization, reanalysis methods are employed instead of popular surrogate models. Compared with the Surrogate Assisted Optimization (SAO), the advantage of reanalysis is that the error can be well controlled, thus the accuracy of optimization should be improved significantly. Two numerical examples are used to verify the performance of the proposed method. The comparison with the linear variation fiber orientation angles shows that the proposed method obtains better solutions by considering the manufacturing constraints simultaneously. © 2016 Elsevier Ltd


Li E.,Central South University | Wang H.,Hunan University | Wang H.,Joint Center for Intelligent New Energy Vehicle
Engineering Computations (Swansea, Wales) | Year: 2016

Purpose - For global optimization, an important issue is a trade-off between exploration and exploitation within limited number of evaluations. Efficient global optimization (EGO) is an important algorithm considering such condition termed as expected improvement (EI). One of major bottlenecks of EGO is to keep the diversity of samples. Recently, Multi-Surrogate EGO uses more samples generated by multiple surrogates to improve the efficiency. However, the total number of samples is commonly large. The purpose of this paper is to suggest a bi-direction multi-surrogate global optimization to overcome this bottleneck. Design/methodology/approach - As the name implies, two different ways are used. The first way is to EI criterion to find better samples similar to EGO. The second way is to use the second term of EI to find accurate regions. Sequentially, the samples in these regions should be evaluated by multiple surrogates instead of exact function evaluations. To enhance the accuracy of these samples, Bayesian inference is employed to predicted the performance of each surrogate in each iteration and obtain the corresponding weight coefficients. The predicted response value of a cheap sample is evaluated by the weighted multiple surrogates combination. Therefore, both accuracy and efficiency can be guaranteed based on such frame. Findings - According to the test functions, it empirically shows that the proposed algorithm is a potentially feasible method for complicated underlying problems. Originality/value - A bi-direction sampling strategy is suggested. The first way is to use EI criterion to generate samples similar to the EGO. In this way, new samples should be evaluated by real functions or simulations called expensive samples. Another way is to search accurate region according to the second term of EI. To guarantee the reliability of samples, a sample selection scenario based on Bayesian theorem is suggested to select the cheap samples. The authors hope this strategy help them to construct more accurate model without increasing computational cost. © Emerald Group Publishing Limited.


Li E.,Central South University | Wang H.,Hunan University | Wang H.,Joint Center for Intelligent New Energy Vehicle | Ye F.,Hunan University | Ye F.,Joint Center for Intelligent New Energy Vehicle
Applied Soft Computing Journal | Year: 2016

Curse of dimensionality is a key issue in engineering optimization. When the dimension increases, distribution of samples becomes sparse due to expanded design space. To obtain accurate and reliable results, the amount of samples often grows exponentially with the dimensions. To improve the efficiency of the surrogate with limited samples, a Two-level Multi-surrogate Assisted Optimization (TMAO) is suggested. The framework of the TMAO is to decompose a complicated problem into separable and non-separable components. In the first-level, High Dimensional Model Representation (HDMR) is utilized to determine the correlations among input variables. Then, a high dimensional problem can be decomposed into separable and non-separable components. Thus, the dimension of the original problem might be reduced significantly. Moreover, considering noises and outliers, Support Vector Regression (SVR)-HDMR is utilized to obtain more reliable surrogate. Expected Improvement (EI) criterion is suggested to generate new samples to save computational cost. In the second-level, to handle the non-separable component, a multi-surrogate assisted sampling strategy is suggested. Compared with other methods, the distinctive characteristic of the suggested sampling strategy is to use different surrogates to search potential uncertain regions. Considering the diversity of surrogates, more feature samples might be generated close to the local optimum. Even though it is still difficult to find a global solution, it could help us to find a feasible solution in practice. To verify the performance of the suggested method, several high dimensional mathematical functions are tested by the suggested method. The results demonstrate that all test functions can be successfully solved. © 2016 Elsevier B.V. All rights reserved.


Lei F.,Hunan University | Du B.,Hunan University | Liu X.,Changsha University | Xie X.,Joint Center for Intelligent New Energy Vehicle | Chai T.,Hunan University
Energy | Year: 2016

In this paper, implicit constrained multi-physics model of a motor wheel for an electric vehicle is built and then optimized. A novel optimization approach is proposed to solve the compliance problem between implicit constraints and stochastic global optimization. Firstly, multi-physics model of motor wheel is built from the theories of structural mechanics, electromagnetism and thermal physics. Then, implicit constraints are applied from the vehicle performances and magnetic characteristics. Implicit constrained optimization is carried out by a series of unconstrained optimization and verifications. In practice, sequentially updated subspaces are designed to completely substitute the original design space in local areas. In each subspace, a solution is obtained and is then verified by the implicit constraints. Optimal solutions which satisfy the implicit constraints are accepted as final candidates. The final global optimal solution is optimized from those candidates. Discussions are carried out to discover the differences between optimal solutions with unconstrained problem and different implicit constrained problems. Results show that the implicit constraints have significant influences on the optimal solution and the proposed approach is effective in finding the optimals. © 2016 Elsevier Ltd


Wang G.,Hunan University | Wang G.,Joint Center for Intelligent New Energy Vehicle | Cui X.Y.,Hunan University | Cui X.Y.,Joint Center for Intelligent New Energy Vehicle | And 2 more authors.
International Journal for Numerical Methods in Engineering | Year: 2016

This paper proposed a rotation-free thin shell formulation with nodal integration for elastic-static, free vibration, and explicit dynamic analyses of structures using three-node triangular cells and linear interpolation functions. The formulation is based on the classic Kirchhoff plate theory, in which only three translational displacements are treated as the filed variables. Based on each node, the integration domains are further formed, where the generalized gradient smoothing technique and Green divergence theorem that can relax the continuity requirement for trial function are used to construct the curvature filed. With the aid of strain smoothing operation and tensor transformation rule, the smoothed strains in the integration domain can be finally expressed by constants. The principle of virtual work is then used to establish the discretized system equations. The translational boundary conditions are imposed same as the practice of standard finite element method, while the rotational boundary conditions are constrained in the process of constructing the smoothed curvature filed. To test the performance of the present formulation, several numerical examples, including both benchmark problems and practical engineering cases, are studied. The results demonstrate that the present method possesses better accuracy and higher efficiency for both static and dynamic problems. Copyright © 2016 John Wiley & Sons, Ltd.


Li E.,Central South University | Wang H.,Hunan University | Wang H.,Joint Center for Intelligent New Energy Vehicle
Advances in Engineering Software | Year: 2016

Differential Evolution (DE) is one of the most powerful stochastic real parameter optimizers. An alternative adaptive DE algorithm called Expected Improvement (EI)-High Dimensional Model Representation (HDMR)-DE is suggested. The EI criterion and the Kriging-HDMR are used to adjust scale factor F and crossover constant Cr, respectively. Considering the expensive computational cost of evaluation, the Kriging is integrated to evaluate the objective function when an accuracy criterion is met. To compare the performance, the suggested method, it has been compared with four popular adaptive DE algorithms over 25 standard numerical benchmarks derived from the IEEE Congress on Evolutionary Computation 2005 competition. To verify the feasibility of the suggested algorithm, a real-world application, time-dependent variable Blank Hold Force (BHF) optimization problem is also carried out by the EI-HDMR-DE. The results show that the EI-HDMR-DE improves the performance of adaptive DE and has potential capability to solve some complicated real-world applications. © 2016 Elsevier Ltd. All rights reserved.


Huang G.,Hunan University | Huang G.,Joint Center for Intelligent New Energy Vehicle | Wang H.,Hunan University | Wang H.,Joint Center for Intelligent New Energy Vehicle | And 2 more authors.
Structural and Multidisciplinary Optimization | Year: 2016

An exact reanalysis method named Indirect Factorization Updating (IFU) is proposed for the structure with local modifications, including boundary modifications. The IFU method is developed from the Independent Coefficients (IC) and Sherman-Morrison-Woodbury (SMW) formula. Due to the local modifications, the modified equations are divided into two parts: balanced equations and unbalanced equations. Using the theory of the IC, extra constraints are enforced on the unbalanced Degree of Freedoms (DOFs), so that the fundamental solution system of the balanced equations can be obtained by using the SMW formula. Then, a unique solution is derived from the general solution of the balanced equations to satisfy the unbalanced equations. In order to use the SMW formula directly, the change of the stiffness matrix is converted to a low-rank form by using the Cholesky factorization of the initial stiffness matrix. The Cholesky factorization is indirectly updated according to the stiffness matrix of the balanced equations but not directly according to the modified equations. Three examples are presented to verify the performance of the suggested IFU method. The results show that the IFU can efficiently obtain the exact solution of the structures with boundary modifications. © 2016 Springer-Verlag Berlin Heidelberg

Loading Joint Center for Intelligent New Energy Vehicle collaborators
Loading Joint Center for Intelligent New Energy Vehicle collaborators