ŽUPERL, Uroš ;ČUŠ, Franci ;GECEVSKA, Valentina . Optimization of the Characteristic Parameters in Milling Using the PSO Evolution Technique. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 53, n.6, p. 354-368, august 2017. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/article/optimization-of-the-characteristic-parameters-in-milling-using-the-pso-evolution-technique/>. Date accessed: 20 dec. 2024. doi:http://dx.doi.org/.
Župerl, U., Čuš, F., & Gecevska, V. (2007). Optimization of the Characteristic Parameters in Milling Using the PSO Evolution Technique. Strojniški vestnik - Journal of Mechanical Engineering, 53(6), 354-368. doi:http://dx.doi.org/
@article{.,
author = {Uroš Župerl and Franci Čuš and Valentina Gecevska},
title = {Optimization of the Characteristic Parameters in Milling Using the PSO Evolution Technique},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {53},
number = {6},
year = {2007},
keywords = {cutting; end-milling; cutting parameters; neural networks; },
abstract = {The selection of machining parameters is an important step in process planning; therefore, a new evolutionary computation technique has been developed to optimize the machining process. In this paper, Particle Swarm Optimization (PSO) is used to efficiently optimize the machining parameters simultaneously in milling processes where multiple conflicting objectives are present. First, an artificial neural network (ANN) predictive model is used to predict the cutting forces during machining and then the PSO algorithm is used to obtain the optimum cutting speeds and feed rates. The goal of the optimization is to determine the objective function maximum (the predicted cutting-force surface) by considering the cutting constraints. During optimization the particles 'fly' intelligently in the solution space and search for optimal cutting conditions according to the strategies of the PSO algorithm.The results showed that an integrated system of neural networks and swarm intelligence is an effective method for solving multi-objective optimization problems. The high accuracy of the results within a wide range of machining parameters indicates that the system can be practically applied in industry. The simulation results show that compared with genetic algorithms (GAs) and simulated annealing (SA) the proposed algorithm can improve the quality of the solution while speeding up the convergence process. The new computational technique has several advantages and benefits and is suitable for use when combined with ANN-based models where no explicit relation between the inputs and the outputs is available. This research opens the door for a new class of optimization techniques that are based on evolution computation in the area of machining.},
issn = {0039-2480}, pages = {354-368}, doi = {},
url = {https://www.sv-jme.eu/article/optimization-of-the-characteristic-parameters-in-milling-using-the-pso-evolution-technique/}
}
Župerl, U.,Čuš, F.,Gecevska, V. 2007 August 53. Optimization of the Characteristic Parameters in Milling Using the PSO Evolution Technique. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 53:6
%A Župerl, Uroš %A Čuš, Franci %A Gecevska, Valentina %D 2007 %T Optimization of the Characteristic Parameters in Milling Using the PSO Evolution Technique %B 2007 %9 cutting; end-milling; cutting parameters; neural networks; %! Optimization of the Characteristic Parameters in Milling Using the PSO Evolution Technique %K cutting; end-milling; cutting parameters; neural networks; %X The selection of machining parameters is an important step in process planning; therefore, a new evolutionary computation technique has been developed to optimize the machining process. In this paper, Particle Swarm Optimization (PSO) is used to efficiently optimize the machining parameters simultaneously in milling processes where multiple conflicting objectives are present. First, an artificial neural network (ANN) predictive model is used to predict the cutting forces during machining and then the PSO algorithm is used to obtain the optimum cutting speeds and feed rates. The goal of the optimization is to determine the objective function maximum (the predicted cutting-force surface) by considering the cutting constraints. During optimization the particles 'fly' intelligently in the solution space and search for optimal cutting conditions according to the strategies of the PSO algorithm.The results showed that an integrated system of neural networks and swarm intelligence is an effective method for solving multi-objective optimization problems. The high accuracy of the results within a wide range of machining parameters indicates that the system can be practically applied in industry. The simulation results show that compared with genetic algorithms (GAs) and simulated annealing (SA) the proposed algorithm can improve the quality of the solution while speeding up the convergence process. The new computational technique has several advantages and benefits and is suitable for use when combined with ANN-based models where no explicit relation between the inputs and the outputs is available. This research opens the door for a new class of optimization techniques that are based on evolution computation in the area of machining. %U https://www.sv-jme.eu/article/optimization-of-the-characteristic-parameters-in-milling-using-the-pso-evolution-technique/ %0 Journal Article %R %& 354 %P 15 %J Strojniški vestnik - Journal of Mechanical Engineering %V 53 %N 6 %@ 0039-2480 %8 2017-08-18 %7 2017-08-18
Župerl, Uroš, Franci Čuš, & Valentina Gecevska. "Optimization of the Characteristic Parameters in Milling Using the PSO Evolution Technique." Strojniški vestnik - Journal of Mechanical Engineering [Online], 53.6 (2007): 354-368. Web. 20 Dec. 2024
TY - JOUR AU - Župerl, Uroš AU - Čuš, Franci AU - Gecevska, Valentina PY - 2007 TI - Optimization of the Characteristic Parameters in Milling Using the PSO Evolution Technique JF - Strojniški vestnik - Journal of Mechanical Engineering DO - KW - cutting; end-milling; cutting parameters; neural networks; N2 - The selection of machining parameters is an important step in process planning; therefore, a new evolutionary computation technique has been developed to optimize the machining process. In this paper, Particle Swarm Optimization (PSO) is used to efficiently optimize the machining parameters simultaneously in milling processes where multiple conflicting objectives are present. First, an artificial neural network (ANN) predictive model is used to predict the cutting forces during machining and then the PSO algorithm is used to obtain the optimum cutting speeds and feed rates. The goal of the optimization is to determine the objective function maximum (the predicted cutting-force surface) by considering the cutting constraints. During optimization the particles 'fly' intelligently in the solution space and search for optimal cutting conditions according to the strategies of the PSO algorithm.The results showed that an integrated system of neural networks and swarm intelligence is an effective method for solving multi-objective optimization problems. The high accuracy of the results within a wide range of machining parameters indicates that the system can be practically applied in industry. The simulation results show that compared with genetic algorithms (GAs) and simulated annealing (SA) the proposed algorithm can improve the quality of the solution while speeding up the convergence process. The new computational technique has several advantages and benefits and is suitable for use when combined with ANN-based models where no explicit relation between the inputs and the outputs is available. This research opens the door for a new class of optimization techniques that are based on evolution computation in the area of machining. UR - https://www.sv-jme.eu/article/optimization-of-the-characteristic-parameters-in-milling-using-the-pso-evolution-technique/
@article{{}{.},
author = {Župerl, U., Čuš, F., Gecevska, V.},
title = {Optimization of the Characteristic Parameters in Milling Using the PSO Evolution Technique},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {53},
number = {6},
year = {2007},
doi = {},
url = {https://www.sv-jme.eu/article/optimization-of-the-characteristic-parameters-in-milling-using-the-pso-evolution-technique/}
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TY - JOUR AU - Župerl, Uroš AU - Čuš, Franci AU - Gecevska, Valentina PY - 2017/08/18 TI - Optimization of the Characteristic Parameters in Milling Using the PSO Evolution Technique JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 53, No 6 (2007): Strojniški vestnik - Journal of Mechanical Engineering DO - KW - cutting, end-milling, cutting parameters, neural networks, N2 - The selection of machining parameters is an important step in process planning; therefore, a new evolutionary computation technique has been developed to optimize the machining process. In this paper, Particle Swarm Optimization (PSO) is used to efficiently optimize the machining parameters simultaneously in milling processes where multiple conflicting objectives are present. First, an artificial neural network (ANN) predictive model is used to predict the cutting forces during machining and then the PSO algorithm is used to obtain the optimum cutting speeds and feed rates. The goal of the optimization is to determine the objective function maximum (the predicted cutting-force surface) by considering the cutting constraints. During optimization the particles 'fly' intelligently in the solution space and search for optimal cutting conditions according to the strategies of the PSO algorithm.The results showed that an integrated system of neural networks and swarm intelligence is an effective method for solving multi-objective optimization problems. The high accuracy of the results within a wide range of machining parameters indicates that the system can be practically applied in industry. The simulation results show that compared with genetic algorithms (GAs) and simulated annealing (SA) the proposed algorithm can improve the quality of the solution while speeding up the convergence process. The new computational technique has several advantages and benefits and is suitable for use when combined with ANN-based models where no explicit relation between the inputs and the outputs is available. This research opens the door for a new class of optimization techniques that are based on evolution computation in the area of machining. UR - https://www.sv-jme.eu/article/optimization-of-the-characteristic-parameters-in-milling-using-the-pso-evolution-technique/
Župerl, Uroš, Čuš, Franci, AND Gecevska, Valentina. "Optimization of the Characteristic Parameters in Milling Using the PSO Evolution Technique" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 53 Number 6 (18 August 2017)
Strojniški vestnik - Journal of Mechanical Engineering 53(2007)6, 354-368
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
The selection of machining parameters is an important step in process planning; therefore, a new evolutionary computation technique has been developed to optimize the machining process. In this paper, Particle Swarm Optimization (PSO) is used to efficiently optimize the machining parameters simultaneously in milling processes where multiple conflicting objectives are present. First, an artificial neural network (ANN) predictive model is used to predict the cutting forces during machining and then the PSO algorithm is used to obtain the optimum cutting speeds and feed rates. The goal of the optimization is to determine the objective function maximum (the predicted cutting-force surface) by considering the cutting constraints. During optimization the particles 'fly' intelligently in the solution space and search for optimal cutting conditions according to the strategies of the PSO algorithm.The results showed that an integrated system of neural networks and swarm intelligence is an effective method for solving multi-objective optimization problems. The high accuracy of the results within a wide range of machining parameters indicates that the system can be practically applied in industry. The simulation results show that compared with genetic algorithms (GAs) and simulated annealing (SA) the proposed algorithm can improve the quality of the solution while speeding up the convergence process. The new computational technique has several advantages and benefits and is suitable for use when combined with ANN-based models where no explicit relation between the inputs and the outputs is available. This research opens the door for a new class of optimization techniques that are based on evolution computation in the area of machining.