Cloud Based Platform for Optimal Machining Parameter Selection Based on Function Blocks and Real Time MonitoringSource: Journal of Manufacturing Science and Engineering:;2015:;volume( 137 ):;issue: 004::page 40909Author:Tapoglou, Nikolaos
,
Mehnen, Jأ¶rn
,
Vlachou, Aikaterini
,
Doukas, Michael
,
Milas, Nikolaos
,
Mourtzis, Dimitris
DOI: 10.1115/1.4029806Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: The way machining operations have been running has changed over the years. Nowadays, machine utilization and availability monitoring are becoming increasingly important for the smooth operation of modern workshops. Moreover, the nature of jobs undertaken by manufacturing small and medium enterprises (SMEs) has shifted from a mass production to small batch. To address the challenges caused by modern fast changing environments, a new cloudbased approach for monitoring the use of manufacturing equipment, dispatching jobs to the selected computer numerical control (CNC) machines, and creating the optimum machining code is presented. In this approach the manufacturing equipment is monitored using a sensor network and though an information fusion technique it derives and broadcasts the data of available tools and machines through the internet to a cloudbased platform. On the manufacturing equipment event driven function blocks with embedded optimization algorithms are responsible for selecting the optimal cutting parameters and generating the moves required for machining the parts while considering the latest information regarding the available machines and cutting tools. A case study based on scenario from a shop floor that undertakes machining jobs is used to demonstrate the developed methods and tools.
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| contributor author | Tapoglou, Nikolaos | |
| contributor author | Mehnen, Jأ¶rn | |
| contributor author | Vlachou, Aikaterini | |
| contributor author | Doukas, Michael | |
| contributor author | Milas, Nikolaos | |
| contributor author | Mourtzis, Dimitris | |
| date accessioned | 2017-05-09T01:20:25Z | |
| date available | 2017-05-09T01:20:25Z | |
| date issued | 2015 | |
| identifier issn | 1087-1357 | |
| identifier other | manu_137_04_040909.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/158704 | |
| description abstract | The way machining operations have been running has changed over the years. Nowadays, machine utilization and availability monitoring are becoming increasingly important for the smooth operation of modern workshops. Moreover, the nature of jobs undertaken by manufacturing small and medium enterprises (SMEs) has shifted from a mass production to small batch. To address the challenges caused by modern fast changing environments, a new cloudbased approach for monitoring the use of manufacturing equipment, dispatching jobs to the selected computer numerical control (CNC) machines, and creating the optimum machining code is presented. In this approach the manufacturing equipment is monitored using a sensor network and though an information fusion technique it derives and broadcasts the data of available tools and machines through the internet to a cloudbased platform. On the manufacturing equipment event driven function blocks with embedded optimization algorithms are responsible for selecting the optimal cutting parameters and generating the moves required for machining the parts while considering the latest information regarding the available machines and cutting tools. A case study based on scenario from a shop floor that undertakes machining jobs is used to demonstrate the developed methods and tools. | |
| publisher | The American Society of Mechanical Engineers (ASME) | |
| title | Cloud Based Platform for Optimal Machining Parameter Selection Based on Function Blocks and Real Time Monitoring | |
| type | Journal Paper | |
| journal volume | 137 | |
| journal issue | 4 | |
| journal title | Journal of Manufacturing Science and Engineering | |
| identifier doi | 10.1115/1.4029806 | |
| journal fristpage | 40909 | |
| journal lastpage | 40909 | |
| identifier eissn | 1528-8935 | |
| tree | Journal of Manufacturing Science and Engineering:;2015:;volume( 137 ):;issue: 004 | |
| contenttype | Fulltext |