No abstract

Safety and weight reduction continue to be the main drivers of structural developments. Better control of frontal collapse and avoidance of bending are the most important aspects of the design of front longitudinal members. Such members usually involve a curved section to provide clearance from mechanical systems, so it is difficult to prevent the onset of bending collapse, under end load, prior to the desired controlled longitudinal collapse of the box sections. While vertical ribs are formed into the walls of the box members to induce longitudinal buckling, it is found that inclining these at an angle is successful in cancelling the bending moment induced by the front end load. In this paper various configurations of incorporating formed ribs into the walls of the S-frame are considered and their effects on energy absorption and force response of the S-frame are studied. It is shown that, by using a proper arrangement of ribs in the walls of the S-frame, better crashworthiness characteristics may be achieved.

Stress evaluation plays a pivotal role in the design of material systems, often accomplished through the finite element method (FEM) for intricate structures. However, the substantial costs and time requirements associated with multi-scale FEM analyses have prompted a growing interest in adopting more efficient, machine-learning-driven strategies. This study investigates the utilization of advanced machine learning techniques for predicting local stress fields in composite materials, presenting it as a superior alternative to traditional FEM approaches. The primary objective of this research is to develop a predictive model for stress field maps in composite components featuring diverse configurations of fibers distributed within the matrix. To achieve this, we employ a Convolutional Neural Network (CNN) with a specialized U-Net architecture, enabling the correlation of spatial fiber organization with the resultant von Mises stress field. The CNN model was extensively trained using four distinct data sets, encompassing uniform fibrous structures, non-uniform fibrous structures, irregularly shaped fibrous structures, and a comprehensive combination of these data sets. The trained U-Net models demonstrate exceptional proficiency in predicting von Mises stress fields, yielding impressive structural similarity index scores (SSIM) of 0.977 and mean squared errors (MSE) of 0.0009 on a dedicated test set. This research harnesses 2D cross-sectional imagery to establish a surrogate model for finite element analysis, offering an accurate and efficient approach for predicting stress fields in composite material design, irrespective of geometric complexity or boundary conditions.

Crashworthiness analysis for a passenger car consists of car-body structural analysis and occupant behavior analysis. With raising the energy absorption of each component an improved car-body structure against crashing may be achieved. In this paper various design of a simplified front side rail is studied. Various possible model of S-frame is presented and characteristics of each model are compared and discussed. It is shown that a hybrid S-frame made of steel and aluminum shows better characteristics from the point of view of passenger safety.