Tolerance Analysis with Multiple Surrogate Models
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Design and analysis of modern engineering systems are complicated, mostly relying on computational analyses codes and hence often computationally expensive. Meanwhile, improved computer-aided design and numerical simulation methods are used extensively in the design process. Even continuing growth of computing power and speed, computationally cheap and reliable models or simulation techniques are not available, at least as ready-to-use computer analysis codes. The necessity of tolerance analysis takes important place at the same time with improved CAD and computer based process planning, because of high quality production requirements, and reduces manufacturing costs. Simulation based design and optimization becomes the only option to meet the specifications, and improves the system reliability. In the last few decades, the use of surrogate models has achieved significant advantages, because they provide fast computation and design investigation as they replace computationally expensive to run computer analyses with cheap to run approximations. Surrogate models also simplify the integration of analyses codes to optimization and reliability assessment studies. In this paper, response surface and Kriging surrogate models are used within a Monte Carlo simulation framework for tolerance analysis. As an illustrative example problem, tolerance analyses of a clutch assembly with nonlinear objection function are used. The effects of the surrogate model parameters (e.g., the use of different regression polynomials) and the number of training points on tolerance analysis are explored.