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Proceedings Paper

Cyber-enabled distributed machine learning for smart manufacturing systems
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Paper Abstract

In this paper, we propose a distributed machine learning (DML) algorithm to fulfill the requirements of the smart factory (or Industry 4.0) including self-organization, a distributed control function, communication between the smart components, and real-time decision-making capability. We show the proposed DML algorithm not only enables the smart factory to adjust the components for new demands and circumstances, but also each component of the system acts smart and communicate with each other, either request or offer functions. The DML is an interactive learning mechanism among smart components and a natural way of scaling up learning algorithms. The different machines can have the best learning algorithms of their own data while the communication between different learning processes is an integration of different learning biases that compensate one another for their inefficient characteristics. As such, the size of the smart factory is scalable and the growing amount of data from additional machines has a minor effect on the communication overheat. We will elaborate on the DML model that overcomes the problems of centralized systems and increases the possibility of achieving higher accuracy, especially on a large-size domain.

Paper Details

Date Published: 18 March 2019
PDF: 6 pages
Proc. SPIE 10973, Smart Structures and NDE for Energy Systems and Industry 4.0, 109730Z (18 March 2019); doi: 10.1117/12.2514911
Show Author Affiliations
Yaser Banadaki, Southern Univ. (United States)
Safura Sharifi, Louisiana State Univ. (United States)

Published in SPIE Proceedings Vol. 10973:
Smart Structures and NDE for Energy Systems and Industry 4.0
Norbert G. Meyendorf; Kerrie Gath; Christopher Niezrecki, Editor(s)

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