Abstract
According to the orthogonal test results, the surface roughness prediction model based on BP artificial neural network algorithm combined with genetic algorithm and considering material removal rate, a multi-objective optimization mathematical model for high-speed milling process parameters optimization was established, and the optimal combination of parameters satisfying the requirements was found within the given parameters range. The method is validated by comparing the surface roughness and processing efficiency with the optimization parameters determined by range analysis method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Wang S, Ai X, Zhao J (2006) Modeling and prediction of surface roughness in high speed milling. Manuf Technol Mach Tools 8:65–68 (in Chinese)
Yang J, Guo H, Li H (2011) Experimental study on surface roughness of aeronautical aluminum 7050 high speed milling. Mech Manuf Res 40(3):34–35 (in Chinese)
Tian X, Yu A (2008) Establishment of milling surface roughness prediction model based on regression analysis. Manuf Technol Mach Tools 11:101–104 (in Chinese)
Li S, Sun L, Guo L (2000) Roughness prediction of surface machining in NC milling. Mech Des Manuf Eng 29(4):35–37 (in Chinese)
Liu M, Yang M, Huo Y (2005) Key technologies for surface roughness prediction in five-axis NC milling of complex surfaces. Mach Manuf 43(495):11, 12–14 (in Chinese)
Wang W, Tang S, Zhang Y (2010) Prediction of surface roughness in high speed milling based on neural network. Mech Des Manuf 3:216–217 (in Chinese)
Tian M, Li Z, Yan B (2004) Research on roughness prediction of high speed milling of hard-to-machine materials based on RBF neural network. Tool Technol 42(3):35–37 (in Chinese)
Xiao J (2010) Establishment of surface roughness model for aluminium alloy milling based on processing residual height. Modul Mach Tools Autom Process Technol 3:76–78 (in Chinese)
Baskar N, Asokan P, Saravanan R, Prabhaharan G (2005) Selection of optimum tool geometry and cutting conditions using a surface roughness prediction model for end milling. Int J Adv Manuf Technol 26:1202–1210
Feng D (2005) Countermeasure of reducing surface roughness in nc milling of aluminum alloy parts. Mech Eng 12:78–79 (in Chinese)
Wang S, Zhu X (2009) Orthogonal optimization of milling parameters for TC4 titanium alloy. Mech Electr 12:39 (in Chinese)
Guo X, Chen X (2011) Experimental study on surface roughness of aluminium alloy 6061 based on high speed milling. Mach Tools Hydraul Press 39(6):38–3913(2011) (in Chinese)
Hu Z, Meng G, Xia H (2011) Study on surface roughness of GH169 high speed milling based on orthogonal test method. Manuf Technol Mach Tools 1:44–46 (in Chinese)
Fang Y, Li F (2006) Prediction model of surface roughness for high speed machining based on artificial neural network. Tool Technol 40(11):78–80 (in Chinese)
Lei Y, Zhang S, Li X (2005) MATLAB genetic algorithm toolbox and its application. Bureau of Electronic Commerce University Press, Xi’an (in Chinese)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, Y., Sun, Y., Lin, H., Zhang, B. (2020). Prediction Model of Milling Surface Roughness Based on Genetic Algorithms. In: Xu, Z., Choo, KK., Dehghantanha, A., Parizi, R., Hammoudeh, M. (eds) Cyber Security Intelligence and Analytics. CSIA 2019. Advances in Intelligent Systems and Computing, vol 928. Springer, Cham. https://doi.org/10.1007/978-3-030-15235-2_179
Download citation
DOI: https://doi.org/10.1007/978-3-030-15235-2_179
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-15234-5
Online ISBN: 978-3-030-15235-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)