This paper shows a framework of the different techniques for the design of the planning and governing components, implementation, Evolution and execution of an intelligent manufacturing system. Architecture of a modern manufacturing industry is presented, which makes possible to create specific manufactrons system for the specific tasks, depending on the self-operated analysis of its essential characteristics. The manufactronic industry concept helps in the integration of intelligence and to increase flexibility at the maximum level of the manufacturing system as well as at the minimum level of the particular machine. This concept is implemented & demonstrated in the automobile and aeronautical industries, but can be simply applied to nearly all manufacturing industries. Implementation of manufactronic techniques in the industries helps to forecast and to fulfill the rapidly varying customer requirements, to produce high quality products in sufficient quantities with reduction in costs. The Modern IntelligenceTechnologies are also presented in this paper.
The present work demonstrates an efficient method for reliability analysis using sequential development of the stochastic response surface. Here, orthogonal Hermite polynomials are used whose unknown coefficients are evaluated using moving least square technique. To do so, collocation points in the conventional stochastic response surface method (SRSM) are replaced by the sparse grid scheme so as to reduce the number of function evaluations. Moreover, the domain is populated sequentially by the sparse grid based on the outcome of the optimization to find out the most probable failure point. Hence, the support points are generated based on a coupled effect of the optimization for failure region and the sub-grids hierarchy. Continuous and differentiable penalty function is imposed to determine multiple failure points, if any, by repeating the optimization. Once the response surface is developed, reliability analysis is carried out using importance sampling. Five different benchmark examples are presented in this study to validate the performance of the proposed modeling. As the accuracy of the method is established, two reliability-based design examples involving nonlinear finite element (FE) analysis of plates are demonstrated. Numerical study shows the efficiency of the proposed sequential SRSM in terms of accuracy and number of time-exhaustive evaluation of the original performance function, as compared to other methods available in the literature. Based on these results, it may be concluded that the proposed method works satisfactorily for a large class of reliability-based design problems.
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