ŽUPERL, Uroš ;ČUŠ, Franci . A Model for Analysing and Optimazing Fixtures. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 48, n.2, p. 73-86, july 2017. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/article/a-model-for-analysing-and-optimazing-fixtures/>. Date accessed: 08 oct. 2024. doi:http://dx.doi.org/.
Župerl, U., & Čuš, F. (2002). A Model for Analysing and Optimazing Fixtures. Strojniški vestnik - Journal of Mechanical Engineering, 48(2), 73-86. doi:http://dx.doi.org/
@article{., author = {Uroš Župerl and Franci Čuš}, title = {A Model for Analysing and Optimazing Fixtures}, journal = {Strojniški vestnik - Journal of Mechanical Engineering}, volume = {48}, number = {2}, year = {2002}, keywords = {fixture analysis; milling; optimization; neural networks; }, abstract = {This paper is about a neuralanalytical model for the analysis and rationalization of fixtures that are suitable for clamping thin-wall products likely to undergo deformation due to the clamping and cutting forces that occur during machining. A program called FIXAN was used for the evaluation of the fixturing scheme and for the calculation of the optimum magnitude and positioning of the clamping forces required to enable the workpiece to be safely clamped during machining. The model is suitable for the analysis of fixtures intended for the fixing of prismatic and rotational products. The model takes into consideration the friction occurring between the workpiece and the fixture components. Because of the use of an artificial neural network (ANN) the time needed for the calculation is very short, therefore, the procedure can be carried out in real time. The described procedure ensures a reduction of the fixture-planning time and the prevention of defects and deformations during the machining process.}, issn = {0039-2480}, pages = {73-86}, doi = {}, url = {https://www.sv-jme.eu/article/a-model-for-analysing-and-optimazing-fixtures/} }
Župerl, U.,Čuš, F. 2002 July 48. A Model for Analysing and Optimazing Fixtures. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 48:2
%A Župerl, Uroš %A Čuš, Franci %D 2002 %T A Model for Analysing and Optimazing Fixtures %B 2002 %9 fixture analysis; milling; optimization; neural networks; %! A Model for Analysing and Optimazing Fixtures %K fixture analysis; milling; optimization; neural networks; %X This paper is about a neuralanalytical model for the analysis and rationalization of fixtures that are suitable for clamping thin-wall products likely to undergo deformation due to the clamping and cutting forces that occur during machining. A program called FIXAN was used for the evaluation of the fixturing scheme and for the calculation of the optimum magnitude and positioning of the clamping forces required to enable the workpiece to be safely clamped during machining. The model is suitable for the analysis of fixtures intended for the fixing of prismatic and rotational products. The model takes into consideration the friction occurring between the workpiece and the fixture components. Because of the use of an artificial neural network (ANN) the time needed for the calculation is very short, therefore, the procedure can be carried out in real time. The described procedure ensures a reduction of the fixture-planning time and the prevention of defects and deformations during the machining process. %U https://www.sv-jme.eu/article/a-model-for-analysing-and-optimazing-fixtures/ %0 Journal Article %R %& 73 %P 14 %J Strojniški vestnik - Journal of Mechanical Engineering %V 48 %N 2 %@ 0039-2480 %8 2017-07-07 %7 2017-07-07
Župerl, Uroš, & Franci Čuš. "A Model for Analysing and Optimazing Fixtures." Strojniški vestnik - Journal of Mechanical Engineering [Online], 48.2 (2002): 73-86. Web. 08 Oct. 2024
TY - JOUR AU - Župerl, Uroš AU - Čuš, Franci PY - 2002 TI - A Model for Analysing and Optimazing Fixtures JF - Strojniški vestnik - Journal of Mechanical Engineering DO - KW - fixture analysis; milling; optimization; neural networks; N2 - This paper is about a neuralanalytical model for the analysis and rationalization of fixtures that are suitable for clamping thin-wall products likely to undergo deformation due to the clamping and cutting forces that occur during machining. A program called FIXAN was used for the evaluation of the fixturing scheme and for the calculation of the optimum magnitude and positioning of the clamping forces required to enable the workpiece to be safely clamped during machining. The model is suitable for the analysis of fixtures intended for the fixing of prismatic and rotational products. The model takes into consideration the friction occurring between the workpiece and the fixture components. Because of the use of an artificial neural network (ANN) the time needed for the calculation is very short, therefore, the procedure can be carried out in real time. The described procedure ensures a reduction of the fixture-planning time and the prevention of defects and deformations during the machining process. UR - https://www.sv-jme.eu/article/a-model-for-analysing-and-optimazing-fixtures/
@article{{}{.}, author = {Župerl, U., Čuš, F.}, title = {A Model for Analysing and Optimazing Fixtures}, journal = {Strojniški vestnik - Journal of Mechanical Engineering}, volume = {48}, number = {2}, year = {2002}, doi = {}, url = {https://www.sv-jme.eu/article/a-model-for-analysing-and-optimazing-fixtures/} }
TY - JOUR AU - Župerl, Uroš AU - Čuš, Franci PY - 2017/07/07 TI - A Model for Analysing and Optimazing Fixtures JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 48, No 2 (2002): Strojniški vestnik - Journal of Mechanical Engineering DO - KW - fixture analysis, milling, optimization, neural networks, N2 - This paper is about a neuralanalytical model for the analysis and rationalization of fixtures that are suitable for clamping thin-wall products likely to undergo deformation due to the clamping and cutting forces that occur during machining. A program called FIXAN was used for the evaluation of the fixturing scheme and for the calculation of the optimum magnitude and positioning of the clamping forces required to enable the workpiece to be safely clamped during machining. The model is suitable for the analysis of fixtures intended for the fixing of prismatic and rotational products. The model takes into consideration the friction occurring between the workpiece and the fixture components. Because of the use of an artificial neural network (ANN) the time needed for the calculation is very short, therefore, the procedure can be carried out in real time. The described procedure ensures a reduction of the fixture-planning time and the prevention of defects and deformations during the machining process. UR - https://www.sv-jme.eu/article/a-model-for-analysing-and-optimazing-fixtures/
Župerl, Uroš, AND Čuš, Franci. "A Model for Analysing and Optimazing Fixtures" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 48 Number 2 (07 July 2017)
Strojniški vestnik - Journal of Mechanical Engineering 48(2002)2, 73-86
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
This paper is about a neuralanalytical model for the analysis and rationalization of fixtures that are suitable for clamping thin-wall products likely to undergo deformation due to the clamping and cutting forces that occur during machining. A program called FIXAN was used for the evaluation of the fixturing scheme and for the calculation of the optimum magnitude and positioning of the clamping forces required to enable the workpiece to be safely clamped during machining. The model is suitable for the analysis of fixtures intended for the fixing of prismatic and rotational products. The model takes into consideration the friction occurring between the workpiece and the fixture components. Because of the use of an artificial neural network (ANN) the time needed for the calculation is very short, therefore, the procedure can be carried out in real time. The described procedure ensures a reduction of the fixture-planning time and the prevention of defects and deformations during the machining process.