Journal
JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING
Volume 22, Issue 1, Pages -Publisher
ASME
DOI: 10.1115/1.4051663
Keywords
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
Funding
- National Science Foundation [1631803, 1646013]
- US Department of Energy (DOE) [DE-AC0500OR22725]
- Writing Lab
- TecLabs
- Tecnologico de Monterrey
- CONACYT
- research group Automotive Consortium
- Directorate For Engineering
- Div Of Civil, Mechanical, & Manufact Inn [1646013] Funding Source: National Science Foundation
- Directorate For Engineering
- Div Of Industrial Innovation & Partnersh [1631803] Funding Source: National Science Foundation
Ask authors/readers for more resources
Effective and efficient modern manufacturing operations require the acceptance and incorporation of Industry 4.0. This paper proposes a specific architecture for transforming smart factory data into useful information. A case study demonstrates the effectiveness of the proposed method in automating control processes and reducing response times.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available