Investigation of intelligent features for CFRP structure in automotive safety systems
New stringent European Union regulations, on both emission and fuel consumption are driving the automobile manufacturers in continuous search of suitable lightweight materials. Carbon Fiber Reinforced Plastic (CFRP) is one of the most prominent lightweight materials in use. In addition to the benefit of reduction in weight, the inherent properties of CFRP materials such as acoustic emission, electrical properties of fibers etc. change with the applied load during elastic deformation and failure. This change in properties is the base of the presented idea to assign intelligence to current automotive safety structures made from CFRP material. This paper gives a short review of the work done by various researchers in the field of health monitoring of CFRP structures using electrical resistance. Further, experiments were performed for initial investigations to show the promising usage of the above explained intelligence features. Tube-like and plate-like specimens were used, because of their similarity with automotive structures. It can be concluded, that the mechanisms like elastic bending and micro buckling have very negligible influence on resistance change compared to other mechanisms. The results from the experiments show significant resistance change within 5 to 50 milliseconds after impact. This demonstrates the potential of this concept to be used for crash severity detection in automobiles.
Prediction of the Elastic Properties of a Plain Woven Carbon Fiber Reinforced Composite with Internal Geometric Variability
A statistical analysis of the yarn parameters of a plain woven carbon fiber reinforced polymer composite was conducted using X-ray micro-computed tomography data. An algorithm based on the correlated Gaussian random sequence was proposed to construct statistically equivalent yarns, which were introduced into a numerical multiscale model. A representative volume element was created to evaluate the macroscopic elastic properties of the composite. The predicted elastic constants showed a good agreement with experimental data obtained from tensile, compressive, and shear tests. This showed the importance of considering internal geometric variability for obtaining accurate simulation results. Finally, the performance of an electric vehicle back door made of the composite material was calculated by finite element analysis. The weight of the back door system was reduced by 47.45%, and performance results showed an excellent prospect of using lightweight composites.
A natural language discourse model to explain linear programming models and solutions
Abstract This paper presents a natural language (i.e., English) discourse model to explain a linear programming model and, possibly, its computed solution (which need not be optimal). Drawing from earlier syntactic translations, a semantic model is presented, based on economic input-output relations commonly found in large-scale linear programs. These are then shown how to form rule-driven explanations, which comprises a fundamental component of an intelligent mathematical programming system.