Crashworthiness design of a steel–aluminum hybrid rail using multi-response objective-oriented sequential optimization

© 2017 Elsevier Ltd Hybrid structures with different materials have aroused increasing interest for their lightweight potential and excellent performances. This study explored the optimization design of steel–aluminum hybrid structures for the highly nonlinear impact scenario. A metamodel based multi-response objective-oriented sequential optimization was adopted, where Kriging models were updated with sequential training points. It was indicated that the sequential sampling strategy was able to obtain a much higher local accuracy in the neighborhood of the optimum and thus to yield a better optimum, although it did lead to a worse global accuracy over the entire design space. Furthermore, it was observed that the steel–aluminum hybrid structure was capable of decreasing the peak force and simultaneously enhancing the energy absorption, compared to the conventional mono-material structure

Metamodel-Based Global Optimization of Vehicle Structures for Crashworthiness Supported by Clustering Methods

This work introduces a metamodel-based global optimization method for crashworthiness with the ability to synthesize continuum structures with an optimal distribution of material phases or gauges. The proposed optimization method makes use of fully nonlinear, dynamic crash simulations and consists of three main elements: (1) the generation of a conceptual design from the structures crash response, (2) the optimal clustering of the conceptual design to define the location of the material phases or gauges, (3) the metamodel-based global optimization, which aims to find the optimal settings for each cluster. The conceptual design can be generated from extracting finite element analysis information or by using structural optimization. The conceptual design is then clustered using clustering analysis to reduce the dimension of the design space. The global optimization problem aims to find the optimal material distribution on the reduced design space using metamodels. The metamodels are built using sampling and cross-validation, and sequentially updated using an expected improvement function until convergence. The proposed methodology is demonstrated using examples from multi-objective crashworthiness design examples

Thin-Walled Compliant Mechanism Component Design Assisted by Machine Learning and Multiple Surrogates

This work introduces a new design algorithm to optimize progressively folding thin-walled structures and in order to improve automotive crashworthiness. The proposed design algorithm is composed of three stages: conceptual thickness distribution, design parameterization, and multi-objective design optimization. The conceptual thickness distribution stage generates an innovative design using a novel one-iteration compliant mechanism approach that triggers progressive folding even on irregular structures under oblique impact. The design parameterization stage optimally segments the conceptual design into a reduced number of clusters using a machine learning K-means algorithm. Finally, the multi-objective design optimization stage finds non-dominated designs of maximum specific energy absorption and minimum peak crushing force. The proposed optimization problem is addressed by a multi-objective genetic algorithm on sequentially updated surrogate models, which are optimally selected from a set of 24 surrogates. The effectiveness of the design algorithm is demonstrated on an S-rail thin-walled structure. The best compromised Pareto design increases specific energy absorption and decreases peak crushing force in the order of 8% and 12%, respectively

Lightweight energy absorbing structures for crashworthy design

PhD ThesisThe application of lightweight composite materials into the rail industry requires a stepwise approach to ensure rail vehicle designs can make optimal use of the inherent properties of each material. Traditionally, materials such as steel and aluminium have been used in railway rolling stock to achieve the energy absorption and structural resistance demanded by European rail standards. Adopting composite materials in primary structural roles requires an innovative design approach which makes the best use of the available space within the rolling stock design such that impact energies and loads are accommodated in a managed and predictable manner. This thesis describes the innovative design of a rail driver’s cab to meet crashworthiness and structural requirements using lightweight, cost-effective composite materials. This takes the application of composite materials in the rail industry beyond the current state-of-the-art and delivers design solutions which are readily applicable across rolling stock categories. An overview of crashworthiness with respect to the rail industry is presented, suitable composite materials for incorporation into rolling stock designs are identified and a methodology to reconfigure and enhance the space available within rail vehicles to meet energy absorption requirements is provided. To realise the application of composite materials, this body of work describes the pioneering application of aluminium honeycomb to deliver unique solutions for rail vehicle energy absorbers, as well as detailing the use of lightweight composite materials to react the structural loads into the cab and carbody. To prove the capability of the design it is supported by finite element analysis and the construction of a full-scale prototype cab which culminated in the successful filing of two patents to protect the intellectual property of the resulting design.The European Commission whose Framework 6 funded project “De-Light” (Contract Number 031483) forms the basis of this work

Optimal Design of Nonlinear Multimaterial Structures for Crashworthiness Using Cluster Analysis

This study presents an efficient multimaterial design optimization algorithm that is suitable for nonlinear structures. The proposed algorithm consists of three steps: conceptual design generation, clustering, and metamodel-based global optimization. The conceptual design is generated using a structural optimization algorithm for linear models or a heuristic design algorithm for nonlinear models. Then, the conceptual design is clustered into a predefined number of clusters (materials) using a machine learning algorithm. Finally, the global optimization problem aims to find the optimal material parameters of the clustered design using metamodels. The metamodels are built using sampling and cross-validation and sequentially updated using an expected improvement function until convergence. The proposed methodology is demonstrated using examples from multiple physics and compared with traditional multimaterial topology optimization (MTOP) method. The proposed approach is applied to a nonlinear, multi-objective design problems for crashworthiness

Structural Optimization of Thin-Walled Tubular Structures for Progressive Collapse Using Hybrid Cellular Automaton with a Prescribed Response Field

The design optimization of thin-walled tubular structures is of relevance in the automotive industry due to their low cost, ease of manufacturing and installation, and high-energy absorption efficiency. This study presents a methodology to design thin-walled tubular structures for crashworthiness applications. During an impact, thin-walled tubular structures may exhibit progressive collapse/buckling, global collapse/buckling, or mixed collapse/buckling. From a crashworthiness standpoint, the most desirable collapse mode is progressive collapse due to its high-energy absorption efficiency, stable deformation, and low peak crush force (PCF). In the automotive industry, thin-walled components have complex structural geometries. These complexities and the several loading conditions present in a crash reduce the possibility of progressive collapse. The Hybrid Cellular Automata (HCA) method has shown to be an efficient continuum-based approach in crashworthiness design. All the current implementations of the HCA method use a scalar set point to design structures with a uniform distribution of a field variable, e.g., stress, strain, internal energy density (IED), mutual potential energy. For example, using IED and mutual potential energy as the field variable result in high stiffness and progressive collapsing structures, respectively. This paper presents a modified version of the HCA method to design thin-walled structures that collapse progressively. In this methodology, the set point has two components, a prescribed response field, which promotes progressive collapse, and a variable offset value, which satisfies the mass constraint. The numerical examples show that this modified HCA method is capable of finding material distributions that exhibit progressive collapse, resulting in significant improvement in specific energy absorption (SEA) with relatively little change in the PCF

Crash Analysis and Energy Absorption Characteristics of S-shaped Longitudinal Members

This paper presents finite element simulations of the crash behavior and the energy absorption characteristics of thin S-shaped longitudinal members with variable cross-sections made of different materials to investigate the design of optimized energy-absorbing members. Numerical studies are carried out by simulation via the explicit finite element code LS-DYNA [1] to determine the desired variables for the design of energy-absorbing members. The specific energy absorption (SEA), the weight of the members and the peak force responses during the frontal impact are the main measurements of the S-shaped members' performance. Several types of inner stiffening members are also investigated to determine the influence of the additional stiffness on the crash behavior

Multi-objective design optimisation of a 3D-rail stamping process using a robust multi-objective optimisation platform (RMOP)

The paper investigates the multi-objective design optimisation of a stamping process to control the final shape and the final quality using advanced high strength steels. The design problem of the stamping process is formulated to minimise the difference between the desired shape and the final geometry obtained by numerical simulation accounting elastic springback. In addition, the final product quality is maximised by improving safety zones without wrinkling, thinning, or failure. Numerical results show that the proposed methodology improves the final product quality while reduces its springback.Peer ReviewedPostprint (published version

Crashworthiness optimization of vehicle structures considering the effects of lightweight material substitution and dummy models

This study uses numerical design optimization with advanced metamodeling techniques to investigate the effects of material substitution and dummy models on crashworthiness characteristics of automotive structures. A full-scale Dodge Neon LS-DYNA finite element model is used in all structural analysis and optimization calculations. Optimization is performed using vehicle-based responses for multiple crash scenarios and occupant-based responses for one crash scenario. An AZ31 magnesium alloy is substituted for the baseline steel in twenty-two vehicle parts. Five base metamodels and an Optimized Ensemble metamodel are used to develop global surrogate models of crash-induced responses. Magnesium alloy is found to maintain or improve vehicle crashworthiness with an approximate 50% reduction in selected part mass using vehicle-based responses while dummy-based designs show less percentage decrease in weight. Vehicle-based responses selected to approximate dummy injury metrics do not show the same relative change compared to dummy-based responses