Jianguang Fang

Optimization of Spot-Welded Joints Combined Artificial Bee Colony Algorithm with Sequential Kriging Optimization

Generally, spot-welded joints are the weakest parts of structures leading to fatigue failure under fluctuating loads. Therefore, it is important to optimize the spot weld to improve the fatigue life. However, a classical optimization of the spot weld often directly couples finite element analysis (FEA) with optimization algorithm, which may fall into a local optimum or be expensive computationally. In this study, a metamodel-based optimization procedure is proposed to find the optimum locations of spotwelded joints for maximum fatigue life. Based on the initial training points, Kriging model is implemented to approximate the objective function regarding the design variables (i.e., locations of spot welds). To further overcome the defect of traditional Kriging model and improve the accuracy of optimumresults, the sequential Kriging optimization (SKO) is utilized, where theKrigingmodel is updated iteratively by adding new training points to the training dataset till the global optimum is obtained. The optimization is run using artificial bee colony (ABC) algorithm and the results show that our proposed method is able to improve the performance of the spot-welded joint. More importantly, more competent optimum can be found and the optimization can be executed more efficiently, compared to the conventional methods.

Multiobjective sequential optimization for a vehicle door using hybrid materials tailor-welded structure

To achieve lightweight vehicle door, this paper presents a novel design with a hybrid material tailor-welded structure (HMTWS). A multiobjective optimization procedure is adopted to generate a set of solutions, in which the door stiffness and mass are taken as objective functions, and the material types and plate thicknesses are regarded as the discrete and continuous design variables, respectively. To improve the optimization efficiency, Kriging algorithm is used for generating surrogate model through a sequential sampling strategy. The non-dominated sorting genetic algorithm II (NSGA-II) is employed to perform the multiobjective optimization. It is found that for the same computational cost, the sequential sampling strategy can yield more accurate optimization results than the conventional one-step sampling strategy. Most importantly, HMTWS is found more competent than the traditional thin-walled configurations made of steel or other lighter mono-materials for maximizing the usage of materials and stiffness of the vehicular door structures.

Development of a novel identification platform for automotive dampers

Traditional approaches with manual regulation of damping parameters could often be too difficult to yield correct parameters due to high nonlinearity and cross effects between different parameters involved. To tackle the problem, this paper proposes a new approach to the identification of the damping parameters for a shock absorber. In this approach, the parameter identification is modelled as an optimisation problem, in which the discrepancy between simulation and test curves is formulated as the objective function and the damping parameters to be identified are regarded as design variables. The kriging model is updated iteratively and an optimum is sought by the particle swarm optimisation (PSO) algorithm until convergence. The effectiveness and robustness of the proposed platform is validated by correlating the simulation results obtained from the identified damping parameters to the corresponding experimental results in the case of a full vehicle.

Structural Optimization of Body-in-white Based on Bi-level Programming

An optimization with a high-dimensional design space and multi-form variables processed within an all-in-one optimization is sometimes unsolvable.Therefore,it is decomposed into a multi-level optimization with a low dimension and variables of single form.An optimization decomposed into a bi-level problem in which the upper-level and the lower-level interdepend,interact and codetermine the responses,is a bi-level programming problem.However,due to the optima of the lower-level are searched within the decision environment of the upper-level,a bi-level programming is intrinsically hard.So iterations between the upper-level and the lower-level are performed.To enhance the body-in-white performance and achieve lightweight,both section shape and shell thickness optimization should be executed.The complex section shape parameterized by mesh morphing technology is accompanied with problems of shell penetration and poor element quality,which result in the failure of reanalysis within all-in-one optimization and in turn terminate the iteration.Therefore,the section shape optimization is applied based on response surface model.While to achieve lightweight,the design variable size of the size optimization should be increased.With the improvement of the design space dimension,sample points multiply accordingly and the fitting accuracy declines radically.So the size optimization is applied in Nastran Sol200.Considering the traits of section shape and shell thickness optimization,a bi-level programming is introduced into the structural optimization of body-in-white,so that the unsolvable optimization is settled,meanwhile the static stiffness and modal frequency are enhanced greatly and lightweight is achieved.

Sensitivity-Based Parameter Calibration and Model Validation Under Model Error

In calibrating model parameters, it is important to include the model discrepancy term in order to capture missing physics in simulation, which can result from numerical, measurement, and modeling errors. Ignoring the discrepancy may lead to biased calibration parameters and predictions, even with an increasing number of observations. In this paper, a simple yet efficient calibration method is proposed based on sensitivity information when the simulation model has a model error and/or numerical error but only a small number of observations are available. The sensitivity-based calibration method captures the trend of observation data by matching the slope of simulation predictions and observations at different designs and then utilizing a constant value to compensate for the model discrepancy. The sensitivity-based calibration is compared with the conventional least squares calibration method and Bayesian calibration method in terms of parameter estimation and model prediction accuracies. A cantilever beam example, as well as a honeycomb tube crush example, is used to illustrate the calibration process of these three methods. It turned out that the sensitivity-based method has a similar performance with the Bayesian calibration method and performs much better than the conventional method in parameter estimation and prediction accuracy. [DOI: 10.1115/1.4038298]

Multi-objective robust design optimization of fatigue life for a welded box girder

ABSTRACT To reduce the scatter of fatigue life for welded structures, a robust optimization method is presented in this study based on a dual surrogate modelling and multi-objective particle swam optimization algorithm. Considering the perturbations of material parameters and environment variables, the mean and standard deviation of fatigue life are fitted using dual surrogate modelling and selected as the objective function to be minimized. As an example, a welded box girder is presented to reduce the standard deviation of fatigue life. A set of non-dominated solutions is produced through a multi-objective particle swam optimization algorithm. A cognitive approach is used to select the optimum solution from the Pareto sets. As a comparative study, traditional single objective optimizations are also presented in this study. The results reduced the standard deviation of the fatigue life by about 16.5%, which indicated that the procedure improved the robustness of the fatigue life.

Structural Analysis and Optimization for a Frame-Type Cab of a Heavy-Duty Truck

The structural analysis of a cab can examine its performances, and then provide a direction for structural optimization. This paper takes a frame-type cab as the research subject, its finite element model and corresponding test scheme are established, and then its torsion stiffness and modal characteristics are analyzed through both simulation and test. Appropriate variables for optimization are screened according to sensitivity analysis. Finally structural optimization for the cab is conducted, so that its torsion stiffness is improved effectively, and simultaneously the structural lightweight design is accomplished on the premise of maintaining the lower modal frequencies on the original level.