@Article{cmes.2020.010565,
AUTHOR = {Haitao Yue, Chenguang Guo, Qiang Li, Lijuan Zhao, Guangbo Hao},
TITLE = {Milling Parameters Optimization of Al-Li Alloy Thin-Wall Workpieces Using Response Surface Methodology and Particle Swarm Optimization},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {124},
YEAR = {2020},
NUMBER = {3},
PAGES = {937--952},
URL = {http://www.techscience.com/CMES/v124n3/39917},
ISSN = {1526-1506},
ABSTRACT = {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.},
DOI = {10.32604/cmes.2020.010565}
}