Abstract
High productivity of parts manufacturing in industrial practice is closely related, not only to time efficiency, but also to the production of parts with high surface quality and considerable lifespan. Face milling is widely used for the efficient creation of accurately flat surfaces, for a large variety of part sizes and materials. However, determining the process parameters, which can lead to the achievement of all required conditions can be considered as a multi-objective problem. This problem can be sufficiently solved using suitable optimization techniques. In the present work, it is attempted to determine the optimum parameters for face milling of steel parts, in order to achieve minimum cutting forces and surface roughness, as well as maximum possible material removal rate. For that reason, after regression models are derived to correlate process parameters with cutting forces and surface roughness, an optimization process is carried out with two different optimization methods, namely Genetic Algorithm and Fireworks Algorithm and after the determination of the optimum process parameters, results concerning the efficiency of optimization algorithms are discussed as well.
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
References
Masmiati N, Sarhan AAD, Hassan MAN, Hamdi M (2016) Optimization of cutting conditions for minimum residual stress, cutting forces and surface roughness in end milling of S50c medium carbon steel. Measurement 86:253–265
Pimenov DYu, Guzeev VI, Mikolajczyk T, Patra K (2017) A study of the influence of processing parameters and tool wear on elastic displacements of the technological system under face milling. Int J Adv Manuf Technol 92:4473–4486
Hadad M, Ramezani M (2016) Modeling and analysis of a novel approach in machining and structuring of flat surfaces using face milling process. Int J Mach Tools Manuf 105:32–44
Hricova J, Naprstkova N (2015) Surface roughness optimization in milling aluminium alloy by using the Taguchi’s design of experiment. Manuf Technol 15(4):541–546
Mikó B (2016) Surface quality prediction in case of steep free form surface milling. Key Eng Mater 686:119–124
Mikó B, Farkas G (2017) Comparison of flatness and surface roughness parameters when face milling and turning. Dev Mach Technol 7:18–27
Kundrák J, Felhő C (2016) 3D roughness parameters of surfaces face milled by special tools. Manuf Technol 16(3):532–538
Felhő C, Kundrák J (2014) Comparison of theoretical and real surface roughness in face milling with octagonal and circular inserts. Key Eng Mater 581:360–365
Mikó B, Tóth B, Varga B (2017) Comparison of theoretical and real surface roughness in case of ball-end milling. Solid State Phenom 261:299–304
Wang Q, Liu F, Wang X (2014) Multi-objective optimization of machining parameters considering energy consumption. Int J Adv Manuf Technol 71:1133–1142
Yan J, Li L (2013) Multi-objective optimization of milling parameters- the trade-offs between energy, production rate and cutting quality. J Clean Prod 52:462–471
Anand Y, Gupta A, Abrol A, Gupta A, Kumar V, Tyagi SK, Anand S (2016) Optimization of machining parameters for green manufacturing. Cogent Eng 3:1153292
Sharma V, Pandey PM (2016) Optimization of machining and vibration parameters for residual stresses minimization in ultrasonic assisted turning of 4340 hardened steel. Ultrasonics 70:172–182
Fu T, Zhao J, Liu W (2012) Multi-objective optimization of cutting parameters in high-speed milling based on grey relational analysis coupled with principal component analysis. Front Mech Eng 7(4):445–452
Tan Y, Yu C, Zheng S, Ding K (2015) Introduction to fireworks algorithm. Int J Swarm Intell Res 4:39–70
Karkalos NE, Markopoulos AP (2018) Determination of Johnson-Cook material model parameters by an optimization approach using the fireworks algorithm. Procedia Manuf 22:107–113
Kundrák J, Markopoulos AP, Makkai T, Karkalos NE (2018) Correlation between process parameters and cutting forces in the face milling of steel. In: Jármai K, Bolló B (eds) Vehicle and Automotive Engineering 2. VAE 2018, ser. Lecture Notes in Mechanical Engineering, Springer, Cham, pp 255–267
Acknowledgment
The authors greatly appreciate the support of the National Research, Development and Innovation Office – NKFIH (No. of Agreement: OTKA K 116876).
This study was carried out as part of the EFOP-3.6.1-16-00011 “Younger and Renewing University – Innovative Knowledge City – institutional development of the University of Miskolc aiming at intelligent specialization” project implemented in the framework of the Szechenyi 2020 program. The realization of this project is supported by the European Union, co-financed by the European Social Fund.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Kundrák, J., Markopoulos, A.P., Makkai, T., Karkalos, N.E., Nagy, A. (2019). Multi-objective Optimization Study in Face Milling of Steel. In: Durakbasa, N., Gencyilmaz, M. (eds) Proceedings of the International Symposium for Production Research 2018. ISPR 2018. Springer, Cham. https://doi.org/10.1007/978-3-319-92267-6_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-92267-6_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-92266-9
Online ISBN: 978-3-319-92267-6
eBook Packages: EngineeringEngineering (R0)