Title :
An improved genetic algorithm for sphericity error evaluation
Author :
Xiulan, Wen ; Aiguo, Song
Abstract :
Sphere is widely used in many engineering applications and its geometric error has a serious effect on the performance of mechanical parts. In this paper, a heuristic approach, that is an improved genetic algorithm (GA), is proposed to implement sphericity error evaluation. Then, the concepts of the minimum zone sphere (MZS), the minimum circumscribed sphere (MCS) and the maximum inscribed sphere (MIS) are clearly defined and their objective function calculation methods are formulated. Finally, the experiment results evaluated by different methods confirmed the proposed GA effectiveness. Compared with conventional evaluation methods, it has the advantages of simple algorithms, strong robustness and high precision. Also, it is a unified approach for other form errors evaluation.
Keywords :
electric machines; genetic algorithms; genetic algorithm; heuristic approach; maximum inscribed sphere; minimum circumscribed sphere; minimum zone sphere; objective function calculation methods; sphericity error evaluation; Educational institutions; Error correction; Genetic algorithms; Instruments; Iterative methods; Least squares methods; Mechanical systems; Potential energy; Robustness; Search methods;
Conference_Titel :
Neural Networks and Signal Processing, 2003. Proceedings of the 2003 International Conference on
Conference_Location :
Nanjing
Print_ISBN :
0-7803-7702-8
DOI :
10.1109/ICNNSP.2003.1279332
