A novel process damping identification model and cutting stability prediction

• Published in: International journal of advanced manufacturing technology Vol. 126; no. 9-10; pp. 4573 - 4579
• Main Authors: Chen, Dongju, Zhang, Xuan, Li, Shupei, Pan, Ri, Sun, Kun, Fan, Jinwei
• Format: Journal Article
• Language: English
• Published: London Springer London 01-06-2023; Springer Nature B.V
• Subjects: Advanced manufacturing technologies; CAE) and Design; Coefficients; Computer-Aided Engineering (CAD; Cutting speed; Damping; Energy methods; Engineering; Industrial and Production Engineering; Interference; Low speed; Manufacturing; Mathematical analysis; Mathematical models; Mechanical Engineering; Media Management; Original Article; Prediction models; Stability analysis; Stability lobes; Tool wear; Workpieces; Complete analytical method; Process damping; Stability lobe diagram; Cutting stability
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-023-11428-8

This paper presents an analytical model for identifying process damping caused by interference between tool and workpiece during the cutting process. In contrast to the traditional discrete method, the model identifies the interference area between tool and workpiece using a complete analytical method. The obtained interference area can achieve the accuracy of the result obtained by using a shorter discrete step, but the time required for the model is shorter than that of the discrete method and is unaffected by the discrete step. The influence of tool wear band on the process damping coefficient is also considered. The process damping coefficient is identified based on the interference area obtained by the complete analytical model, and the damping coefficient of the non-linear process is linearized by the energy method. Finally, the cutting stability lobe diagram is drawn considering the influence of process damping, and the cutting stability region is predicted. Compared with the lobe diagram without process damping, it is found that process damping can significantly improve the cutting stability in low-speed cutting, and a series of cutting tests are carried out to verify the correctness of the prediction model.