ÇOLAK, Oğuz . Optimization of Machining Performance in High-Pressure Assisted Turning of Ti6Al4V Alloy. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 60, n.10, p. 675-681, june 2018. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/article/optimization-of-machining-performance-in-high-pressure-assisted-turning-of-ti6al4v-alloy/>. Date accessed: 25 apr. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2013.1079.
Çolak, O. (2014). Optimization of Machining Performance in High-Pressure Assisted Turning of Ti6Al4V Alloy. Strojniški vestnik - Journal of Mechanical Engineering, 60(10), 675-681. doi:http://dx.doi.org/10.5545/sv-jme.2013.1079
@article{sv-jmesv-jme.2013.1079,
author = {Oğuz Çolak},
title = {Optimization of Machining Performance in High-Pressure Assisted Turning of Ti6Al4V Alloy},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {60},
number = {10},
year = {2014},
keywords = {High pressure cooling, Optimization, Tool life},
abstract = {In this study, a genetic algorithm has been employed to determine optimum cutting parameters in the turning of Ti6Al4V alloy under conventional and high pressure cooling conditions. Three machining performance measures, i.e. surface roughness, material removal rate and cutting power, are considered as optimization criteria. First, with multi-regression analysis of experimental responses, empirical equations are defined and, by using these equations, objective functions are constructed for each pressure level, based on a hybrid model. Objective functions are maximized by means of a genetic algorithm and optimum machining parameters are determined. Moreover, tool wear tests are carried out at a cutting condition that is close to the optimum machining parameters. Optimization results show that optimum cutting parameters and their responses, particularly in P = 6 and 150 bar cooling conditions, are quite similar, but tool life is significantly different. Maximum tool life is achieved in the highest pressure level (P = 300 bar).},
issn = {0039-2480}, pages = {675-681}, doi = {10.5545/sv-jme.2013.1079},
url = {https://www.sv-jme.eu/article/optimization-of-machining-performance-in-high-pressure-assisted-turning-of-ti6al4v-alloy/}
}
Çolak, O. 2014 June 60. Optimization of Machining Performance in High-Pressure Assisted Turning of Ti6Al4V Alloy. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 60:10
%A Çolak, Oğuz %D 2014 %T Optimization of Machining Performance in High-Pressure Assisted Turning of Ti6Al4V Alloy %B 2014 %9 High pressure cooling, Optimization, Tool life %! Optimization of Machining Performance in High-Pressure Assisted Turning of Ti6Al4V Alloy %K High pressure cooling, Optimization, Tool life %X In this study, a genetic algorithm has been employed to determine optimum cutting parameters in the turning of Ti6Al4V alloy under conventional and high pressure cooling conditions. Three machining performance measures, i.e. surface roughness, material removal rate and cutting power, are considered as optimization criteria. First, with multi-regression analysis of experimental responses, empirical equations are defined and, by using these equations, objective functions are constructed for each pressure level, based on a hybrid model. Objective functions are maximized by means of a genetic algorithm and optimum machining parameters are determined. Moreover, tool wear tests are carried out at a cutting condition that is close to the optimum machining parameters. Optimization results show that optimum cutting parameters and their responses, particularly in P = 6 and 150 bar cooling conditions, are quite similar, but tool life is significantly different. Maximum tool life is achieved in the highest pressure level (P = 300 bar). %U https://www.sv-jme.eu/article/optimization-of-machining-performance-in-high-pressure-assisted-turning-of-ti6al4v-alloy/ %0 Journal Article %R 10.5545/sv-jme.2013.1079 %& 675 %P 7 %J Strojniški vestnik - Journal of Mechanical Engineering %V 60 %N 10 %@ 0039-2480 %8 2018-06-28 %7 2018-06-28
Çolak, Oğuz. "Optimization of Machining Performance in High-Pressure Assisted Turning of Ti6Al4V Alloy." Strojniški vestnik - Journal of Mechanical Engineering [Online], 60.10 (2014): 675-681. Web. 25 Apr. 2024
TY - JOUR AU - Çolak, Oğuz PY - 2014 TI - Optimization of Machining Performance in High-Pressure Assisted Turning of Ti6Al4V Alloy JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2013.1079 KW - High pressure cooling, Optimization, Tool life N2 - In this study, a genetic algorithm has been employed to determine optimum cutting parameters in the turning of Ti6Al4V alloy under conventional and high pressure cooling conditions. Three machining performance measures, i.e. surface roughness, material removal rate and cutting power, are considered as optimization criteria. First, with multi-regression analysis of experimental responses, empirical equations are defined and, by using these equations, objective functions are constructed for each pressure level, based on a hybrid model. Objective functions are maximized by means of a genetic algorithm and optimum machining parameters are determined. Moreover, tool wear tests are carried out at a cutting condition that is close to the optimum machining parameters. Optimization results show that optimum cutting parameters and their responses, particularly in P = 6 and 150 bar cooling conditions, are quite similar, but tool life is significantly different. Maximum tool life is achieved in the highest pressure level (P = 300 bar). UR - https://www.sv-jme.eu/article/optimization-of-machining-performance-in-high-pressure-assisted-turning-of-ti6al4v-alloy/
@article{{sv-jme}{sv-jme.2013.1079},
author = {Çolak, O.},
title = {Optimization of Machining Performance in High-Pressure Assisted Turning of Ti6Al4V Alloy},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {60},
number = {10},
year = {2014},
doi = {10.5545/sv-jme.2013.1079},
url = {https://www.sv-jme.eu/article/optimization-of-machining-performance-in-high-pressure-assisted-turning-of-ti6al4v-alloy/}
}
TY - JOUR AU - Çolak, Oğuz PY - 2018/06/28 TI - Optimization of Machining Performance in High-Pressure Assisted Turning of Ti6Al4V Alloy JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 60, No 10 (2014): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2013.1079 KW - High pressure cooling, Optimization, Tool life N2 - In this study, a genetic algorithm has been employed to determine optimum cutting parameters in the turning of Ti6Al4V alloy under conventional and high pressure cooling conditions. Three machining performance measures, i.e. surface roughness, material removal rate and cutting power, are considered as optimization criteria. First, with multi-regression analysis of experimental responses, empirical equations are defined and, by using these equations, objective functions are constructed for each pressure level, based on a hybrid model. Objective functions are maximized by means of a genetic algorithm and optimum machining parameters are determined. Moreover, tool wear tests are carried out at a cutting condition that is close to the optimum machining parameters. Optimization results show that optimum cutting parameters and their responses, particularly in P = 6 and 150 bar cooling conditions, are quite similar, but tool life is significantly different. Maximum tool life is achieved in the highest pressure level (P = 300 bar). UR - https://www.sv-jme.eu/article/optimization-of-machining-performance-in-high-pressure-assisted-turning-of-ti6al4v-alloy/
Çolak, Oğuz"Optimization of Machining Performance in High-Pressure Assisted Turning of Ti6Al4V Alloy" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 60 Number 10 (28 June 2018)
Strojniški vestnik - Journal of Mechanical Engineering 60(2014)10, 675-681
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
In this study, a genetic algorithm has been employed to determine optimum cutting parameters in the turning of Ti6Al4V alloy under conventional and high pressure cooling conditions. Three machining performance measures, i.e. surface roughness, material removal rate and cutting power, are considered as optimization criteria. First, with multi-regression analysis of experimental responses, empirical equations are defined and, by using these equations, objective functions are constructed for each pressure level, based on a hybrid model. Objective functions are maximized by means of a genetic algorithm and optimum machining parameters are determined. Moreover, tool wear tests are carried out at a cutting condition that is close to the optimum machining parameters. Optimization results show that optimum cutting parameters and their responses, particularly in P = 6 and 150 bar cooling conditions, are quite similar, but tool life is significantly different. Maximum tool life is achieved in the highest pressure level (P = 300 bar).