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Milling Parameters Optimization of Al-Li Alloy Thin-Wall Workpieces Using Response Surface Methodology and Particle Swarm Optimization

Haitao Yue1, Chenguang Guo1,*, Qiang Li1, Lijuan Zhao1, Guangbo Hao2
1 School of Mechanical Engineering, Liaoning Technical University, Fuxin, 12300, China
2 School of Engineering, University College Cork, Cork, T12 K8AF, Ireland
* Corresponding Author: Chenguang Guo. Email:

Computer Modeling in Engineering & Sciences 2020, 124(3), 937-952.

Received 18 March 2020; Accepted 16 June 2020; Issue published 21 August 2020


To improve the milling surface quality of the Al-Li alloy thin-wall workpieces and reduce the cutting energy consumption. Experimental research on the milling processing of AA2195 Al-Li alloy thin-wall workpieces based on Response Surface Methodology was carried out. The single factor and interaction of milling parameters on surface roughness and specific cutting energy were analyzed, and the multi-objective optimization model was constructed. The Multiobjective Particle Swarm Optimization algorithm introducing the Chaos Local Search algorithm and the adaptive inertial weight was applied to determine the optimal combination of milling parameters. It was observed that surface roughness was mainly influenced by feed per tooth, and specific cutting energy was negatively correlated with feed per tooth, radial cutting depth and axial cutting depth, while cutting speed has a non-significant influence on specific cutting energy. The optimal combination of milling parameters with different priorities was obtained. The experimental results showed that the maximum relative error of measured and predicted values was 8.05%, and the model had high reliability, which ensured the low surface roughness and cutting energy consumption. It was of great guiding significance for the success of Al-Li alloy thin-wall milling with a high precision and energy efficiency.


Al-Li alloy thin-wall workpieces; response surface methodology; surface roughness; specific cutting energy; multi-objective particle swarm optimization algorithm

Cite This Article

Yue, H., Guo, C., Li, Q., Zhao, L., Hao, G. (2020). Milling Parameters Optimization of Al-Li Alloy Thin-Wall Workpieces Using Response Surface Methodology and Particle Swarm Optimization. CMES-Computer Modeling in Engineering & Sciences, 124(3), 937–952.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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