Minimum Zone Evolution of Circularity Error Based on an Improved Genetic Algorithm

From the analysis of existent evaluation methods for circularity errors and optimization techniques, an intelligent evaluation method is provided in this paper. The evolutional optimum model and the calculation process are introduced in detail. According to characteristics of circularity error evaluation, an improved Genetic Algorithm (GA) is proposed to evaluate the minimum zone error. Compared with conventional optimum evaluation methods such as simplex search and Powell method, it can find the global optimal solution, and the precision of calculating result is very good. Then, the objective function calculation approaches for using the improved GA to evaluate minimum zone error are formulated. Finally, the control experiment results evaluated by different method such as the least square, simplex search, Powell optimum methods and Simulated Anneal Algorithm (SAA), indicate that the proposed method does provide better accuracy on circularity error evaluation, and it has fast convergent speed as well as using computer expediently and popularizing application easily.