Dynamics Modeling-Based Optimization of Process Parameters in Face Milling of Workpieces with Discontinuous Surfaces

[+] Author and Article Information
Guilong Li

Dongchuan Road 800, School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai, 200240, China Shanghai, Shanghai 200240 China lgl52613@sjtu.edu.cn

Shichang Du

800 Dongchuan Road Shanghai, 200240 China lovbin@sjtu.edu.cn

Delin Huang

800 Dongchuan Road Shanghai 200240 China Shanghai, 200240 China cjwanan@sjtu.edu.cn

Chen Zhao

Dong Chuan Road 800 Shanghai, Shanghai 200240 China zhao_chen@sjtu.edu.cn

Yafei Deng

Dongchuan Road 800 Shanghai, Shanghai 200240 China phoenixdyf@sjtu.edu.cn

1Corresponding author.

Manuscript received May 14, 2019; final manuscript received July 23, 2019; published online xx xx, xxxx. Assoc. Editor: Tony Schmitz.

ASME doi:10.1115/1.4044421 History: Received May 14, 2019; Accepted July 25, 2019


Face milling is widely used in machining processes, aimed at providing workpieces with high surface quality. The chatter generated in face milling could lead to tremendous damage to machine tools, poor machined surface quality and loss of processing efficiency. Most related researches have been focused on the modeling of spindle dynamics and discretization algorithms for chatter prediction. However, few published articles have taken the geometric characteristics of workpieces into consideration, especially for workpieces with discontinuous surfaces in face milling, which leads to poor accuracy of chatter prediction as well as the waste of processing efficiency. To overcome this shortage, a novel dynamic model for the face milling process is built in this paper, considering the cutting insert engagement based on the geometric characteristics of the workpieces and the tool path. The stability lobe diagrams (SLDs) applicable to workpieces with discontinuous surfaces are constructed. A process parameter optimization model is developed to maximize the chatter free processing efficiency of the face milling process. The sensitivity analysis is utilized to simplify the objective function and the genetic algorithm is employed to solve the optimization model. The proposed approach is validated by an experimental case study of an engine block, improving the chatter free material removal rate by 53.3% in comparison to the classic approach.

Copyright © 2019 by ASME
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