ŽUPERL, Uroš ;ČUŠ, Franci . A Determination of the Characteristic Technological and Economic Parameters during Metal Cutting. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 50, n.5, p. 252-266, july 2017. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/article/a-determination-of-the-characteristic-technological-and-economic-parameters-during-metal-cutting/>. Date accessed: 20 dec. 2024. doi:http://dx.doi.org/.
Župerl, U., & Čuš, F. (2004). A Determination of the Characteristic Technological and Economic Parameters during Metal Cutting. Strojniški vestnik - Journal of Mechanical Engineering, 50(5), 252-266. doi:http://dx.doi.org/
@article{.,
author = {Uroš Župerl and Franci Čuš},
title = {A Determination of the Characteristic Technological and Economic Parameters during Metal Cutting},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {50},
number = {5},
year = {2004},
keywords = {machining; cutting parameters; turining; nondeterministic optimization; },
abstract = {A new non-deterministic optimization approach to the complex optimization of cutting parameters during machining is proposed. It uses artificial neural networks to solve the cutting-conditions optimization problem. The developed approach is based on the maximum production rate criterion and incorporates four technological constraints. By selecting the optimum cutting conditions it is possible to reach a favourable ratio between low machining costs and high productivity, taking into account the given limitation of the cutting process. First, the problem of determining the optimum machining parameters is formulated as a multiple-objective optimization problem. Then, neural networks are proposed to represent manufacturers preference structures. The experimental results show that the proposed algorithm for solving the non-linearconstrained optimization problems is efficient and can be integrated into intelligent manufacturing systems. To demonstrate the performance of the proposed approach, an illustrative example is discussed in detail.},
issn = {0039-2480}, pages = {252-266}, doi = {},
url = {https://www.sv-jme.eu/article/a-determination-of-the-characteristic-technological-and-economic-parameters-during-metal-cutting/}
}
Župerl, U.,Čuš, F. 2004 July 50. A Determination of the Characteristic Technological and Economic Parameters during Metal Cutting. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 50:5
%A Župerl, Uroš %A Čuš, Franci %D 2004 %T A Determination of the Characteristic Technological and Economic Parameters during Metal Cutting %B 2004 %9 machining; cutting parameters; turining; nondeterministic optimization; %! A Determination of the Characteristic Technological and Economic Parameters during Metal Cutting %K machining; cutting parameters; turining; nondeterministic optimization; %X A new non-deterministic optimization approach to the complex optimization of cutting parameters during machining is proposed. It uses artificial neural networks to solve the cutting-conditions optimization problem. The developed approach is based on the maximum production rate criterion and incorporates four technological constraints. By selecting the optimum cutting conditions it is possible to reach a favourable ratio between low machining costs and high productivity, taking into account the given limitation of the cutting process. First, the problem of determining the optimum machining parameters is formulated as a multiple-objective optimization problem. Then, neural networks are proposed to represent manufacturers preference structures. The experimental results show that the proposed algorithm for solving the non-linearconstrained optimization problems is efficient and can be integrated into intelligent manufacturing systems. To demonstrate the performance of the proposed approach, an illustrative example is discussed in detail. %U https://www.sv-jme.eu/article/a-determination-of-the-characteristic-technological-and-economic-parameters-during-metal-cutting/ %0 Journal Article %R %& 252 %P 15 %J Strojniški vestnik - Journal of Mechanical Engineering %V 50 %N 5 %@ 0039-2480 %8 2017-07-07 %7 2017-07-07
Župerl, Uroš, & Franci Čuš. "A Determination of the Characteristic Technological and Economic Parameters during Metal Cutting." Strojniški vestnik - Journal of Mechanical Engineering [Online], 50.5 (2004): 252-266. Web. 20 Dec. 2024
TY - JOUR AU - Župerl, Uroš AU - Čuš, Franci PY - 2004 TI - A Determination of the Characteristic Technological and Economic Parameters during Metal Cutting JF - Strojniški vestnik - Journal of Mechanical Engineering DO - KW - machining; cutting parameters; turining; nondeterministic optimization; N2 - A new non-deterministic optimization approach to the complex optimization of cutting parameters during machining is proposed. It uses artificial neural networks to solve the cutting-conditions optimization problem. The developed approach is based on the maximum production rate criterion and incorporates four technological constraints. By selecting the optimum cutting conditions it is possible to reach a favourable ratio between low machining costs and high productivity, taking into account the given limitation of the cutting process. First, the problem of determining the optimum machining parameters is formulated as a multiple-objective optimization problem. Then, neural networks are proposed to represent manufacturers preference structures. The experimental results show that the proposed algorithm for solving the non-linearconstrained optimization problems is efficient and can be integrated into intelligent manufacturing systems. To demonstrate the performance of the proposed approach, an illustrative example is discussed in detail. UR - https://www.sv-jme.eu/article/a-determination-of-the-characteristic-technological-and-economic-parameters-during-metal-cutting/
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author = {Župerl, U., Čuš, F.},
title = {A Determination of the Characteristic Technological and Economic Parameters during Metal Cutting},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {50},
number = {5},
year = {2004},
doi = {},
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TY - JOUR AU - Župerl, Uroš AU - Čuš, Franci PY - 2017/07/07 TI - A Determination of the Characteristic Technological and Economic Parameters during Metal Cutting JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 50, No 5 (2004): Strojniški vestnik - Journal of Mechanical Engineering DO - KW - machining, cutting parameters, turining, nondeterministic optimization, N2 - A new non-deterministic optimization approach to the complex optimization of cutting parameters during machining is proposed. It uses artificial neural networks to solve the cutting-conditions optimization problem. The developed approach is based on the maximum production rate criterion and incorporates four technological constraints. By selecting the optimum cutting conditions it is possible to reach a favourable ratio between low machining costs and high productivity, taking into account the given limitation of the cutting process. First, the problem of determining the optimum machining parameters is formulated as a multiple-objective optimization problem. Then, neural networks are proposed to represent manufacturers preference structures. The experimental results show that the proposed algorithm for solving the non-linearconstrained optimization problems is efficient and can be integrated into intelligent manufacturing systems. To demonstrate the performance of the proposed approach, an illustrative example is discussed in detail. UR - https://www.sv-jme.eu/article/a-determination-of-the-characteristic-technological-and-economic-parameters-during-metal-cutting/
Župerl, Uroš, AND Čuš, Franci. "A Determination of the Characteristic Technological and Economic Parameters during Metal Cutting" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 50 Number 5 (07 July 2017)
Strojniški vestnik - Journal of Mechanical Engineering 50(2004)5, 252-266
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
A new non-deterministic optimization approach to the complex optimization of cutting parameters during machining is proposed. It uses artificial neural networks to solve the cutting-conditions optimization problem. The developed approach is based on the maximum production rate criterion and incorporates four technological constraints. By selecting the optimum cutting conditions it is possible to reach a favourable ratio between low machining costs and high productivity, taking into account the given limitation of the cutting process. First, the problem of determining the optimum machining parameters is formulated as a multiple-objective optimization problem. Then, neural networks are proposed to represent manufacturers preference structures. The experimental results show that the proposed algorithm for solving the non-linearconstrained optimization problems is efficient and can be integrated into intelligent manufacturing systems. To demonstrate the performance of the proposed approach, an illustrative example is discussed in detail.