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Self-piercing riveting (SPR) are one of most important joining approaches in lightweight vehicle design for Body-in-white (BIW) manufacturing. Numerical simulation of the riveting process could significantly boost design efficiency by reducing trial-and-error experiments. The traditional Finite Element Method (FEM) with element erosion is hard to capture the large plastic deformation and complex failure behaviors in the SPR process. The smoothed Particle Galerkin Method (SPG) is a genuine meshless method based on Galerkin's weak form, which uses a novel bond-based failure mechanism to keep the conservation of mass and momentum during the material failure process. This study utilizes a combined FEM and SPG approach to join Aluminum sheet 5754 using a full three-dimensional (3D) model in LS-DYNA/explicit.

The use of lightweight materials is one of the important means to reduce the quality of the vehicle, which involves the connection of dissimilar materials, such as the combination of lightweight materials and traditional steel materials. The riveting quality of self-piercing riveting (SPR) technology will directly affect the safety and durability of automobiles. Therefore, in the initial joint development process, the quality of self-piercing riveting should be inspected and classified to meet safety standards. Based on this, this paper divides the self-piercing riveting quality into riveting appearance quality and riveting section quality. Aiming at the appearance quality of riveting, the generation of cracks on the lower surface of riveting will seriously affect the riveting strength. The existing method of identifying cracks on the lower surface of riveting based on artificial vision has strong subjectivity, low efficiency and cannot be applied on a large scale.

Intelligent driving is an important research direction in the field of artificial intelligence. The fourth industrial revolution represented by the Internet of things provides more prospects for the development of intelligent vehicles. Trajectory planning and tracking control is one of the key technologies of intelligent driving vehicle. This paper takes intelligent driving vehicle as the starting point and establishes a research method of intelligent vehicle trajectory planning based on particle swarm optimization, based on the vehicle kinematics and dynamics model, a model predictive control algorithm is built for trajectory tracking control, the simulation scene is built by Prescan, the vehicle dynamics parameters are set in Carsim, and then the joint simulation is carried out with Simulink.

Self-piercing rivets (SPR) are efficient and economical joining methods used in the manufacturing of lightweight automotive bodies. The finite element method (FEM) is a potentially effective way to assess the joining process of SPRs. However, uncertain parameters could lead to significant mismatches between the FEM predictions and physical tests. Thus, a sensitivity study on critical model parameters is important to guide the high-fidelity modeling of the SPR insertion process. In this paper, an axisymmetric FEM model is constructed to simulate the insertion process of the SPR using LS-DYNA/explicit. Then, several surrogate models are evaluated and trained using machine learning methods to represent the relations between selected inputs (e.g., material properties, interfacial frictions, and clamping force) and outputs (cross-section dimensions).

Significant progress in modeling techniques has greatly enhanced the application of computer simulations in vehicle safety. However, the fine-meshed impact models are usually complex and take lots of computational resources and time to conduct design optimization. Hence, to develop effective methods to simplify the impact models without losing necessary accuracy is of significant meaning in vehicle crashworthiness analysis. Surface deformation is frequently regarded as a critical factor to be measured for validating the accuracy of CAE models. This paper proposes an integrated validation framework to evaluate the inconsistencies between the deformed surfaces of the original model and simplified model. The geometric features and curvature information of the deformed surfaces are firstly obtained from crash simulation. Then, the magnitude and shape discrepancy information are integrated into the validation framework as the surface comparison objects.

Computer modeling and simulation have significantly facilitated the efficiency of product design and development in modern engineering, especially in the automotive industry. For the design and optimization of car models, optimization algorithms usually work better if the initial searching points are within or close to a feasible domain. Therefore, finding a feasible design domain in advance is beneficial. A data mining technique, Iterative Dichotomizer 3 (ID3), is exploited in this paper to identify sets of reduced feasible design domains from the original design space. Within the reduced feasible domains, optimal designs can be efficiently obtained while releasing computational burden in iterations. A mathematical example is used to illustrate the proposed method. Then an industrial application about automotive structural optimization is employed to demonstrate the proposed methodology. The results show the proposed method’s potential in practical engineering.

In automobile industry, computational models built to predict the performances of the prototype vehicles are on the rise. To assess the validity or predictive capability of the model for its intended usage, validation activities are conducted to compare computational model outputs with test measurements. Validation becomes difficult when dealing with dynamic systems which often involve multiple functional responses, and the complex characteristics need to be appropriately considered. Many promising data analysis tools and metrics were previously developed to handle data correlation and evaluate the errors in magnitude, phase shift, and shape. However, these methods show their limitations when dealing with nonlinear multivariate dynamic systems. In this paper, kernel function based projection is employed to transform the nonlinear data into linear space, followed by the regular principal component analysis (PCA) based data processing.

Vehicle weight reduction has become one of the viable solutions to ever-growing energy and environmental crisis. In vehicle design, response surface model (RSM) is commonly used as a surrogate of the high fidelity Finite Element (FE) model to reduce the computational time and improve the efficiency of design process. However, RSM introduces additional sources of uncertainty, such as model bias, which largely affects the reliability and robustness of the prediction results. The bias of RSM need to be addressed before the model is ready for extrapolation and design optimization. For the purpose of constructing and correcting the bias in RSMs, scheduling Design of Experiments (DOEs) must be conducted properly. This paper develops a method to arrange DOEs in order to build RSMs with high quality, considering the influence of input uncertainty.

Computer programs and models are playing an increasing role in simulating vehicle crashworthiness, dynamic, and fuel efficiency. To maximize the effectiveness of these models, the validity and predictive capabilities of these models need to be assessed quantitatively. For a successful implementation of Computer Aided Engineering (CAE) models as an integrated part of the current vehicle development process, it is necessary to develop objective validation metric that has the desirable metric properties to quantify the discrepancy between multiple tests and simulation results. However, most of the outputs of dynamic systems are multiple functional responses, such as time history series. This calls for the development of an objective metric that can evaluate the differences of the multiple time histories as well as the key features under uncertainty.

Simulation based design optimization has become the common practice in automotive product development. Increasing computer models are developed to simulate various dynamic systems. Before applying these models for product development, model validation needs to be conducted to assess their validity. In model validation, for the purpose of obtaining results successfully, it is vital to select or develop appropriate metrics for specific applications. For dynamic systems, one of the key obstacles of model validation is that most of the responses are functional, such as time history curves. This calls for the development of a metric that can evaluate the differences in terms of phase shift, magnitude and shape, which requires information from both time and frequency domain. And by representing time histories in frequency domain, more intuitive information can be obtained, such as magnitude-frequency and phase-frequency characteristics.

In vehicle design, response surface model (RSM) is commonly used as a surrogate of the high fidelity Finite Element (FE) model to reduce the computational time and improve the efficiency of design process. However, RSM introduces additional sources of uncertainty, such as model bias, which largely affect the reliability and robustness of the prediction results. The bias of RSM need to be addressed before the model is ready for extrapolation and design optimization. This paper further investigates the Bayesian inference based model extrapolation method which is previously proposed by the authors, and provides a systematic and integrated stochastic bias corrected model extrapolation and robustness design process under uncertainty. A real world vehicle design example is used to demonstrate the validity of the proposed method.

Model validation is a process to assess the validity and predictive capabilities of a computer model by comparing simulation results with test data for its intended use of the model. One of the key difficulties for model validation is to evaluate the quality of a computer model at different test configurations in design space, and interpolate or extrapolate the evaluation results to untested new design configurations. In this paper, an integrated model interpolation and extrapolation framework based on Bayesian inference and Response Surface Models (RSM) is proposed to validate the designs both within and outside of the original design space. Bayesian inference is first applied to quantify the distributions' hyper-parameters of the bias between test and CAE data in the validation domain. Then, the hyper-parameters are extrapolated from the design configurations to untested new design. They are then followed by the prediction interval of responses at the new design points.

Computer Aided Engineering (CAE) has become a vital tool for product development in automotive industry. Increasing computer models are developed to simulate vehicle crashworthiness, dynamic, and fuel efficiency. Before applying these models for product development, model validation needs to be conducted to assess the validity of the models. However, one of the key difficulties for model validation of dynamic systems is that most of the responses are functional responses, such as time history curves. This calls for the development of an objective metric which can evaluate the differences of both the time history and the key features, such as phase shift, magnitude, and slope between test and CAE curves. One of the promising metrics is Error Assessment of Response Time Histories (EARTH), which was recently developed. Three independent error measures that associated with physically meaningful characteristics (phase, magnitude, and slope) were proposed.