Minimum Zone Evaluation of Sphericity Error Based on Ant Colony Algorithm

In this paper, based on the analysis of existent evaluation methods for sphericity errors, an intelligent evaluation method is provided. The evolutional optimum model and the calculation process are introduced in detail. According to characteristics of sphericity error evaluation, ant colony optimization (ACO) algorithm 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 high. Then, the objective function calculation approaches for using the ACO algorithm 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 GA, indicate that the proposed method can provide better accuracy on sphericity error evaluation, and it has fast convergent speed as well as using computer expediently and popularizing application easily.