Physics based data driven method for the crashworthiness design of origami composite tubes

A novel method based on a physics informed data driven model is developed to design an origami composite crash tube. The structure consists of two axially stacked basic components, called modules. Each module presents lower and upper square sections with an octagonal section in the middle. The parameters of the octagonal cross-section and the height of each module are optimized to maximize the energy absorption of the tube when subjected to an axial impact. In contrast to standard surrogate modelling techniques, whose accuracy only depends on the amount of available data, a Physics-informed Neural Network (PINN) scheme is adopted to correlate the crushing response of the single modules to that of the whole origami tube, constraining the data driven method to physically consistent predictions. The PINN is first trained on the results obtained with an experimentally validated Finite Element model and then used to optimize the structure. Results show that the PINN can accurately predict the crushing response of the origami tube, while consistently reducing the computational effort required to explore the whole design domain. Also, the comparison with a standard Feed Forward Neural Network (FFNN) shows that the PINN scheme leads to more accurate results

Optimal Design of Cellular Material Systems for Crashworthiness

This work proposes a new method to design crashworthiness structures that made of functionally graded cellular (porous) material. The proposed method consists of three stages: The first stage is to generate a conceptual design using a topology optimization algorithm so that a variable density is distributed within the structure minimizing its compliance. The second stage is to cluster the variable density using a machine-learning algorithm to reduce the dimension of the design space. The third stage is to maximize structural crashworthiness indicators (e.g., internal energy absorption) and minimize mass using a metamodel-based multi-objective genetic algorithm. The final structure is synthesized by optimally selecting cellular material phases from a predefined material library. In this work, the Hashin-Shtrikman bounds are derived for the two-phase cellular material, and the structure performances are compared to the optimized structures derived by our proposed framework. In comparison to traditional structures that made of a single cellular phase, the results demonstrate the improved performance when multiple cellular phases are used

Crashworthiness design of density-graded cellular metals

AbstractCrashworthiness of cellular metals with a linear density gradient was analyzed by using cell-based finite element models and shock models. Mechanisms of energy absorption and deformation of graded cellular metals were explored by shock wave propagation analysis. Results show that a positive density gradient is a good choice for protecting the impacting object because it can meet the crashworthiness requirements of high energy absorption, stable impact resistance and low peak stress

Energy-absorbing origami structure for crashworthiness design

This paper presents experimental and numerical investigations on the origami-patterned tube which is acknowledged as a promising energy-absorption device. Its buckling mode leads to high performances in terms of specific energy absorption (SEA) and crush force efficiency (CFE). The polygonal tube is prefolded by following an origami pattern, which is designed to act as geometric imperfection and mode inducer. First, a series of quasi-static crushing tests are performed on origami tubes with different materials and geometrical features. Specimens in SUS316L and AlSi10Mg are produced through Additive Manufacturing (AM). It allows to conveniently produce few samples with a complex shape. Finite Element Analysis (FEA) and Direct Image Correlation (DIC) are employed for a better insight into the complex crushing behaviour. The Aluminum tube shows a brittle behaviour while SUS316L tubes have extremely promising performance until local crack happens. Limits stemming from the employment of AM are explored and a new geometry is designed to avoid cracking. Second, a numerical design exploration study is carried out to assess the sensitivity of origami pattern features over the energy-absorption performance. ANSYS Autodyn is utilized as FE solver and DesignXplorer for correlation and optimization. The benefits of new patterns are investigated through geometrical optimization, and an improved geometry is proposed. The pattern stiffness is tuned to account for the external boundary conditions, resulting in a more uniform crushing behaviour. A similar force trend is maintained with a SEA increment of 51.7% due to a drastic weight reduction in areas with lower influence on post-buckling stiffnes

A Data Mining Methodology for Vehicle Crashworthiness Design

This study develops a systematic design methodology based on data mining theory for decision-making in the development of crashworthy vehicles. The new data mining methodology allows the exploration of a large crash simulation dataset to discover the underlying relationships among vehicle crash responses and design variables at multiple levels and to derive design rules based on the whole-vehicle safety requirements to make decisions about component-level and subcomponent-level design. The method can resolve a major issue with existing design approaches related to vehicle crashworthiness: that is, limited abilities to explore information from large datasets, which may hamper decision-making in the design processes. At the component level, two structural design approaches were implemented for detailed component design with the data mining method: namely, a dimension-based approach and a node-based approach to handle structures with regular and irregular shapes, respectively. These two approaches were used to design a thin-walled vehicular structure, the S-shaped beam, against crash loading. A large number of design alternatives were created, and their responses under loading were evaluated by finite element simulations. The design variables and computed responses formed a large design dataset. This dataset was then mined to build a decision tree. Based on the decision tree, the interrelationships among the design parameters were revealed, and design rules were generated to produce a set of good designs. After the data mining, the critical design parameters were identified and the design space was reduced, which can simplify the design process. To partially replace the expensive finite element simulations, a surrogate model was used to model the relationships between design variables and response. Four machine learning algorithms, which can be used for surrogate model development, were compared. Based on the results, Gaussian process regression was determined to be the most suitable technique in the present scenario, and an optimization process was developed to tune the algorithm’s hyperparameters, which govern the model structure and training process. To account for engineering uncertainty in the data mining method, a new decision tree for uncertain data was proposed based on the joint probability in uncertain spaces, and it was implemented to again design the S-beam structure. The findings show that the new decision tree can produce effective decision-making rules for engineering design under uncertainty. To evaluate the new approaches developed in this work, a comprehensive case study was conducted by designing a vehicle system against the frontal crash. A publicly available vehicle model was simplified and validated. Using the newly developed approaches, new component designs in this vehicle were generated and integrated back into the vehicle model so their crash behavior could be simulated. Based on the simulation results, one can conclude that the designs with the new method can outperform the original design in terms of measures of mass, intrusion and peak acceleration. Therefore, the performance of the new design methodology has been confirmed. The current study demonstrates that the new data mining method can be used in vehicle crashworthiness design, and it has the potential to be applied to other complex engineering systems with a large amount of design data

On the crashworthiness performance of thin-walled energy absorbers: Recent advances and future developments

Over the past several decades, a noticeable amount of research efforts has been directed to minimising injuries and death to people inside a structure that is subjected to an impact loading. Thin-walled (TW) tubular components have been widely employed in energy absorbing structures to alleviate the detrimental effects of an impact loading during a collision event and thus enhance the crashworthiness performance of a structure. Comprehensive knowledge of the material properties and the structural behaviour of various TW components under various loading conditions is essential for designing an effective energy absorbing system. In this paper, based on a broad survey of the literature, a comprehensive overview of the recent developments in the area of crashworthiness performance of TW tubes is given with a special focus on the topics that emerged in the last ten years such as crashworthiness optimisation design and energy absorbing responses of unconventional TW components including multi-cells tubes, functionally graded thickness tubes and functionally graded foam filled tubes. Due to the huge number of studies that analysed and assessed the energy absorption behaviour of various TW components, this paper presents only a review of the crashworthiness behaviour of the components that can be used in vehicles structures including hollow and foam-filled TW tubes under lateral, axial, oblique and bending loading