Abstract :
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.
Keywords :
evolutionary computation; least squares approximations; mechanical variables measurement; optimisation; search problems; Powell method; ant colony optimization algorithm; evolutional optimum model; existent evaluation methods; intelligent evaluation method; least square; minimum zone evaluation; simplex search; sphericity error; Algorithm design and analysis; Ant colony optimization; Application software; Automation; Chebyshev approximation; Computer errors; Error correction; Instruments; Least squares methods; Mechanical variables measurement; Metrology; ant colony optimization; evaluation; minimum zone method; sphericity;
