ZUPERL, Uros ;ČUŠ, Franci . A Cyber-Physical System for Surface Roughness Monitoring in End-Milling. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 65, n.2, p. 67-77, february 2019. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/sl/article/smart-surface-roughness-monitoring-in-end-milling/>. Date accessed: 20 dec. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2018.5792.
Zuperl, U., & Čuš, F. (2019). A Cyber-Physical System for Surface Roughness Monitoring in End-Milling. Strojniški vestnik - Journal of Mechanical Engineering, 65(2), 67-77. doi:http://dx.doi.org/10.5545/sv-jme.2018.5792
@article{sv-jmesv-jme.2018.5792,
author = {Uros Zuperl and Franci Čuš},
title = {A Cyber-Physical System for Surface Roughness Monitoring in End-Milling},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {65},
number = {2},
year = {2019},
keywords = {machining; end milling; chip size control; surface roughness; monitoring},
abstract = {The main focus of this paper is to describe the structure of the cyber-physical machining system developed for on-line surface roughness monitoring via cutting chip size control in end-milling. The two level cyber- physical machining system is realised by connecting the computing resources in the developed cloud machining platform to the machine tool with its smart sensor system. The smart optical sensor system is developed to acquire and transfer in real time the values of the cutting chips sizes to the cloud level. The cloud based machining platform with the developed internet of things applications is employed to perform instant surface roughness monitoring and cutting chip size control based on advanced sensor signal processing, edge computing, process feature extraction, machine learning, process modelling, data analysing and cognitive corrective process control acting. These actions are performed as cloud services. A cloud application with an adaptive neural inference system is applied to model and on-line predict surface roughness based on the determined cutting chip size. Based on the inprocess predictions, a novel application for cognitive corrective control acting is employed to control the cutting chip size by modifying the machining parameters and consequently keeping surface roughness constant. The results of the machining experiment are presented to demonstrate that this proposed system where the cloud computing resources and services are linked with the machine tool is feasible and could be implemented to monitor surface roughness during milling operation.},
issn = {0039-2480}, pages = {67-77}, doi = {10.5545/sv-jme.2018.5792},
url = {https://www.sv-jme.eu/sl/article/smart-surface-roughness-monitoring-in-end-milling/}
}
Zuperl, U.,Čuš, F. 2019 February 65. A Cyber-Physical System for Surface Roughness Monitoring in End-Milling. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 65:2
%A Zuperl, Uros %A Čuš, Franci %D 2019 %T A Cyber-Physical System for Surface Roughness Monitoring in End-Milling %B 2019 %9 machining; end milling; chip size control; surface roughness; monitoring %! A Cyber-Physical System for Surface Roughness Monitoring in End-Milling %K machining; end milling; chip size control; surface roughness; monitoring %X The main focus of this paper is to describe the structure of the cyber-physical machining system developed for on-line surface roughness monitoring via cutting chip size control in end-milling. The two level cyber- physical machining system is realised by connecting the computing resources in the developed cloud machining platform to the machine tool with its smart sensor system. The smart optical sensor system is developed to acquire and transfer in real time the values of the cutting chips sizes to the cloud level. The cloud based machining platform with the developed internet of things applications is employed to perform instant surface roughness monitoring and cutting chip size control based on advanced sensor signal processing, edge computing, process feature extraction, machine learning, process modelling, data analysing and cognitive corrective process control acting. These actions are performed as cloud services. A cloud application with an adaptive neural inference system is applied to model and on-line predict surface roughness based on the determined cutting chip size. Based on the inprocess predictions, a novel application for cognitive corrective control acting is employed to control the cutting chip size by modifying the machining parameters and consequently keeping surface roughness constant. The results of the machining experiment are presented to demonstrate that this proposed system where the cloud computing resources and services are linked with the machine tool is feasible and could be implemented to monitor surface roughness during milling operation. %U https://www.sv-jme.eu/sl/article/smart-surface-roughness-monitoring-in-end-milling/ %0 Journal Article %R 10.5545/sv-jme.2018.5792 %& 67 %P 11 %J Strojniški vestnik - Journal of Mechanical Engineering %V 65 %N 2 %@ 0039-2480 %8 2019-02-17 %7 2019-02-17
Zuperl, Uros, & Franci Čuš. "A Cyber-Physical System for Surface Roughness Monitoring in End-Milling." Strojniški vestnik - Journal of Mechanical Engineering [Online], 65.2 (2019): 67-77. Web. 20 Dec. 2024
TY - JOUR AU - Zuperl, Uros AU - Čuš, Franci PY - 2019 TI - A Cyber-Physical System for Surface Roughness Monitoring in End-Milling JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2018.5792 KW - machining; end milling; chip size control; surface roughness; monitoring N2 - The main focus of this paper is to describe the structure of the cyber-physical machining system developed for on-line surface roughness monitoring via cutting chip size control in end-milling. The two level cyber- physical machining system is realised by connecting the computing resources in the developed cloud machining platform to the machine tool with its smart sensor system. The smart optical sensor system is developed to acquire and transfer in real time the values of the cutting chips sizes to the cloud level. The cloud based machining platform with the developed internet of things applications is employed to perform instant surface roughness monitoring and cutting chip size control based on advanced sensor signal processing, edge computing, process feature extraction, machine learning, process modelling, data analysing and cognitive corrective process control acting. These actions are performed as cloud services. A cloud application with an adaptive neural inference system is applied to model and on-line predict surface roughness based on the determined cutting chip size. Based on the inprocess predictions, a novel application for cognitive corrective control acting is employed to control the cutting chip size by modifying the machining parameters and consequently keeping surface roughness constant. The results of the machining experiment are presented to demonstrate that this proposed system where the cloud computing resources and services are linked with the machine tool is feasible and could be implemented to monitor surface roughness during milling operation. UR - https://www.sv-jme.eu/sl/article/smart-surface-roughness-monitoring-in-end-milling/
@article{{sv-jme}{sv-jme.2018.5792},
author = {Zuperl, U., Čuš, F.},
title = {A Cyber-Physical System for Surface Roughness Monitoring in End-Milling},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {65},
number = {2},
year = {2019},
doi = {10.5545/sv-jme.2018.5792},
url = {https://www.sv-jme.eu/sl/article/smart-surface-roughness-monitoring-in-end-milling/}
}
TY - JOUR AU - Zuperl, Uros AU - Čuš, Franci PY - 2019/02/17 TI - A Cyber-Physical System for Surface Roughness Monitoring in End-Milling JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 65, No 2 (2019): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2018.5792 KW - machining, end milling, chip size control, surface roughness, monitoring N2 - The main focus of this paper is to describe the structure of the cyber-physical machining system developed for on-line surface roughness monitoring via cutting chip size control in end-milling. The two level cyber- physical machining system is realised by connecting the computing resources in the developed cloud machining platform to the machine tool with its smart sensor system. The smart optical sensor system is developed to acquire and transfer in real time the values of the cutting chips sizes to the cloud level. The cloud based machining platform with the developed internet of things applications is employed to perform instant surface roughness monitoring and cutting chip size control based on advanced sensor signal processing, edge computing, process feature extraction, machine learning, process modelling, data analysing and cognitive corrective process control acting. These actions are performed as cloud services. A cloud application with an adaptive neural inference system is applied to model and on-line predict surface roughness based on the determined cutting chip size. Based on the inprocess predictions, a novel application for cognitive corrective control acting is employed to control the cutting chip size by modifying the machining parameters and consequently keeping surface roughness constant. The results of the machining experiment are presented to demonstrate that this proposed system where the cloud computing resources and services are linked with the machine tool is feasible and could be implemented to monitor surface roughness during milling operation. UR - https://www.sv-jme.eu/sl/article/smart-surface-roughness-monitoring-in-end-milling/
Zuperl, Uros, AND Čuš, Franci. "A Cyber-Physical System for Surface Roughness Monitoring in End-Milling" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 65 Number 2 (17 February 2019)
Strojniški vestnik - Journal of Mechanical Engineering 65(2019)2, 67-77
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
The main focus of this paper is to describe the structure of the cyber-physical machining system developed for on-line surface roughness monitoring via cutting chip size control in end-milling. The two level cyber- physical machining system is realised by connecting the computing resources in the developed cloud machining platform to the machine tool with its smart sensor system. The smart optical sensor system is developed to acquire and transfer in real time the values of the cutting chips sizes to the cloud level. The cloud based machining platform with the developed internet of things applications is employed to perform instant surface roughness monitoring and cutting chip size control based on advanced sensor signal processing, edge computing, process feature extraction, machine learning, process modelling, data analysing and cognitive corrective process control acting. These actions are performed as cloud services. A cloud application with an adaptive neural inference system is applied to model and on-line predict surface roughness based on the determined cutting chip size. Based on the inprocess predictions, a novel application for cognitive corrective control acting is employed to control the cutting chip size by modifying the machining parameters and consequently keeping surface roughness constant. The results of the machining experiment are presented to demonstrate that this proposed system where the cloud computing resources and services are linked with the machine tool is feasible and could be implemented to monitor surface roughness during milling operation.