Cyber-Physical System Implementation for Manufacturing With Analytics in the Cloud Layer

被引:5
|
作者
Parto, Mahmoud [1 ]
Urbina Coronado, Pedro Daniel [2 ]
Saldana, Christopher [1 ]
Kurfess, Thomas [3 ]
机构
[1] Georgia Inst Technol, George W Woodruff Sch Mech Engn, 771 Ferst Dr NW,Love Bldg Room 101, Atlanta, GA 30332 USA
[2] Tecnol Monterrey, Sch Engn & Sci, Ave Eugenio Garza Sada 2501, Monterrey 64849, Mexico
[3] Oak Ridge Natl Lab, 1 Bethel Valley Rd, Oak Ridge, TN 37830 USA
基金
美国国家科学基金会;
关键词
internet of things; industry; 4; 0; fog computing; edge computing; data analytics; smart factory; computer aided manufacturing; cyber-physical system design and operation; cybermanufacturing; industrial internet of things; manufacturing automation; ARCHITECTURE; FOG; VIRTUALIZATION; FUTURE;
D O I
10.1115/1.4051663
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Effective and efficient modern manufacturing operations require the acceptance and incorporation of the fourth industrial revolution, also known as Industry 4.0. Traditional shop floors are evolving their production into smart factories. To continue this trend, a specific architecture for the cyber-physical system is required, as well as a systematic approach to automate the application of algorithms and transform the acquired data into useful information. This work makes use of an approach that distinguishes three layers that are part of the existing Industry 4.0 paradigm: edge, fog, and cloud. Each of the layers performs computational operations, transforming the data produced in the smart factory into useful information. Trained or untrained methods for data analytics can be incorporated into the architecture. A case study is presented in which a real-time statistical control process algorithm based on control charts was implemented. The algorithm automatically detects changes in the material being processed in a computerized numerical control (CNC) machine. The algorithm implemented in the proposed architecture yielded short response times. The performance was effective since it automatically adapted to the machining of aluminum and then detected when the material was switched to steel. The data were backed up in a database that would allow traceability to the line of g-code that performed the machining.
引用
收藏
页数:11
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