According to ISO 1101, ‘‘A geometrical tolerance applied to a feature defines the tolerance zone within which that feature shall be contained’’. The main goal of the minimum zone tolerance (MZT) method is to achieve the best estimation of the roundness error, but it is computationally intensive. This paper describes the application of a genetic algorithm (GA) to minimize the computation time in the evaluation of CMM roundness errors of a large cloud of sampled points. Computational experiments have shown that by selecting the optimal GA parameters, namely a combination of the five genetic parameters related to population size, crossover, mutation, stop condition, and search space, the computation time can be reduced by up to one order of magnitude, allowing real-time operation. Optimization has been tested using seven CMM samples, obtained from different machining features. The performance of the optimized algorithm has been validated using four benchmark samples from the literature and with certified samples.

Fast genetic algorithm for roundness evaluation by the minimum zone tolerance (MZT) method

ROSSI, ANDREA;LANZETTA, MICHELE
2011-01-01

Abstract

According to ISO 1101, ‘‘A geometrical tolerance applied to a feature defines the tolerance zone within which that feature shall be contained’’. The main goal of the minimum zone tolerance (MZT) method is to achieve the best estimation of the roundness error, but it is computationally intensive. This paper describes the application of a genetic algorithm (GA) to minimize the computation time in the evaluation of CMM roundness errors of a large cloud of sampled points. Computational experiments have shown that by selecting the optimal GA parameters, namely a combination of the five genetic parameters related to population size, crossover, mutation, stop condition, and search space, the computation time can be reduced by up to one order of magnitude, allowing real-time operation. Optimization has been tested using seven CMM samples, obtained from different machining features. The performance of the optimized algorithm has been validated using four benchmark samples from the literature and with certified samples.
Rossi, Andrea; Antonetti, M; Barloscio, M; Lanzetta, Michele
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/144419
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