Affiliations: [a] College of Mechanical Engineering, Jilin Teachers Institute of Engineering and Technology, Changchun, Jilin, China
| [b] Department of Materials Science and Engineering, Yingkou Institute of Technology, Yingkou, Liaoning, China
| [c] College of Mechanical Engineering, Jilin Teachers Institute of Engineering and Technology, Changchun, Jilin, China
Correspondence:
[*]
Corresponding author. Pengyuan Qi, Department of Materials Science and Engineering, Yingkou
Institute of Technology, Yingkou 115014, Liaoning, China. E-mail: qipengyuan@126.com.
Abstract: In order to effectively avoid the violent vibration in the process of mechanical processing and to achieve high efficiency and high precision machining of mechanical parts, the improved algorithm of adaptive neuro-fuzzy inference system is used to study the optimization of parameters in the process of side milling of mechanical parts, and an adaptive network structure is formed. It has the learning ability of artificial neural network and the expression ability of “if-then” of fuzzy reasoning system, which is a new prediction and control method. The results validate the applicability of the stability. The machined surface topography is measured and the effect of flutter on the surface topography is analyzed. The three-dimensional stability of milling provides a theoretical basis for the rational selection of milling parameters of mechanical parts, the realization of stable milling and the improvement of processing efficiency. Thus, the relationship between the radial depth of cut, the axial depth of cut and the spindle speed is established, and the contour of material removal rate is obtained. The corresponding spindle speed and radial shear depth are obtained when the material removal rate is maximum. The reasonable selection of machining parameters is carried out in the region near the maximum spindle speed with stability.
