Abstract: Optimisation is a must for every engineering design so as to increase the performance of every mechanical system. Trial and error methods are mostly used to check for stresses and validate the design which is not optimum. A new optimisation technique is found out by using bivariate interpolation method or surface fit method. From multiple variables, the problem is reduced to 3 variables (x, y, z). The variation of x with respect to z, keeping y constant and variation of y with respect to z, keeping x constant are found out. These sampling points are combined to get the relating surface of z= f(x, y) using Bivariate interpolation methods. The surface plot is obtained and for specific value of z, the 3D surface plot is chopped off, the resulting surface will be optimum curve which satisfies all the design considerations. This method is applicable for all types of problems. In this paper, taking an example of spring, keeping the various parameters like free length, effective no of coils,

• Publication Date: 12-Apr-2015
• DOI: 10.15224/978-1-63248-059-0-63
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Abstract: This paper proposes a methodology for robustnessbased design optimization under both aleatory (i.e., natural or physical variability) and epistemic uncertainty (i.e., imprecise probabilistic information). The proposed formulations specifically deal with epistemic uncertainty arising from multiple interval data. An efficient likelihood-based approach is used to represent the interval uncertainty, which is then used in the framework for robustness-based design optimization to achieve computational efficiency. The proposed robust design optimization methodology is illustrated using a general mathematical example problem

• Publication Date: 12-Apr-2015
• DOI: 10.15224/978-1-63248-059-0-64
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