Tool wear monitoring using generalized regression neural network. (May 2019)

• Record Type: Journal Article
• Authors: Kang, Ling; Wang, Sibao; Wang, Shilong; Ma, Chi; Yi, Lili; Zou, Haixu
• Is Part Of: Advances in mechanical engineering. Volume 11:Number 5(2019)
• Publication Date: 2019-05
• Subjects: Tool wear monitoring -- generalized regression neural network -- improved fruit fly optimization algorithm -- particle swarm optimization; Mechanical engineering -- Periodicals; 621.05
• DOI: 10.1177/1687814019849172
• Languages: English
• ISSNs: 1687-8132

Abstract

To improve the modeling accuracy and efficiency of the tool wear monitoring system, a generalized regression neural network is adopted to build the tool wear prediction model because its excellent performance on learning speed and fast convergence to the optimal results whether the sample data are small or large. The low predictive accuracy and efficiency are caused by traditionally manual adjustment of the spread parameters in generalized regression neural network and then the improved fruit fly optimization algorithm is proposed to optimize the spread parameters of regression neural network automatically. Combining the improved fruit fly optimization and generalized regression neural network, the tool wear prediction method is proposed in the paper. Various experiments are carried out to validate the proposed method and the comparison results show a good agreement. In addition, the proposed method is compared to the tool wear prediction method in the literature, and the comparison results also show that the proposed method can achieve better performance.