Composites have become preferred material for weight reduction in automobile. Hybrid composite materials are used in many engineering applications for their versatile properties like lightweight, strength to weight ratio, low cost, ease of structure development and high strength. Automobile industry utilizes composites and hybrid composites in many of the interior and exterior applications. Considering ease of manufacturing methods under the wider choice of fabrication techniques, hybrid composites are developed wherever it can contribute for the enhancement of functional requirement simply by replacing the existing materials. Hybrid composites made from blending of natural and synthetic fibers in a polymer matrix show synergistic effect on properties which cannot be achieved from normal composites. In this paper, hybrid composites and especially its types, diversity of properties and possible material combinations are reviewed in detail. The development of a various type of hybrid composites in automotive applications such as in automotive piston application, brake friction material for medium duty automotive application, development of eco-friendly automotive anti-roll bar and low-velocity impact response of hybrid carbon/glass fiber-reinforced polymer composites are also reviewed in detail.
Reliability-based design often uses the Monte-Carlo method as a sampling procedure for predicting failure. The combination of designing for very small failure probabilities (10 8 10 6 ) and using computationally expensive finite element models, makes Monte-Carlo simulations very expensive. This paper uses an improved sampling procedure for calculating the probability of failure, called separable Monte-Carlo method. The separable MonteCarlo method can improve the accuracy of the traditional crude Monte-Carlo when response and capacity are independent. In previous research, accuracy of separable Monte-Carlo for a simple limit state was estimated via expectation calculus for a simple form of the limit state. In this paper, error estimates for a general limit state are developed through bootstrapping, and it is demonstrated that the estimates are reasonably accurate. Separable Monte-Carlo allows us to choose different sample sizes of the response and capacity in the limit state, and the paper demonstrates that bootstrapping may be used to estimate the contribution of the response and capacity to the total error. When the accuracy of the probability of failure is not good enough, the paper proposes reformulation of the limit state as another way to reduce uncertainty associated with the expensive random variable (usually the response).
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