Diameter error prediction of workpiece in turning process based on fuzzy system

To satisfy machining accuracy requirements for specific workpieces, fuzzy system was adopted to predicate the diameter error of workpiece in turning process based on the characteristics of diameter error. Turning experiment was designed to obtain the original training data and testing data. After analyzing the advantages and disadvantages of gradient descent algorithm and traditional genetic algorithm, gradient descent algorithm was imbedded into traditional genetic algorithm to form the hybrid genetic algorithm. Using training data, Mamdani fuzzy system was trained by gradient descent algorithm, traditional genetic algorithm and hybrid genetic algorithm respectively. Results showed that hybrid genetic algorithm had better convergence than gradient descent algorithm and traditional genetic algorithm. The fuzzy system which was trained by three algorithms was tested by testing data respectively, the prediction effect of hybrid genetic algorithm was the best of three algorithms. The prediction results showed that under certain structure of workpiece and working conditions, Mamdani fuzzy system which was trained by hybrid genetic algorithm was feasible in predicating diameter error of workpiece in turning process.