Authors:
Soujanya Mantravadi
;
Chen Li
and
Charles Møller
Affiliation:
Department of Materials & Production, Aalborg University, Fibigestræde 16, Aalborg and Denmark
Keyword(s):
AI Applications, Industry 4.0, Intelligent Manufacturing, Manufacturing Operations Management (MOM), Multi-Agent Systems, Enterprise Information Systems, Architectural Solution, Automated Reasoning, Uncertainty, Work-in-Progress (WIP).
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Distributed and Mobile Software Systems
;
Enterprise Information Systems
;
Group Decision Support Systems
;
Industrial Applications of Artificial Intelligence
;
Intelligent Agents
;
Internet Technology
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Multi-Agent Systems
;
Scheduling and Planning
;
Software Engineering
;
Symbolic Systems
;
Web Information Systems and Technologies
Abstract:
Smart factory of the future is expected to support interoperability on the shop floor, where information systems are pivotal in enabling interconnectivity between its physical assets. In this era of digital transformation, manufacturing execution system (MES) is emerging as a critical software tool to support production planning and control while accessing the shop floor data. However, application of MES as an enterprise information system still lacks the decision support capabilities on the shop floor. As an attempt to design intelligent MES, this paper demonstrates one of the artificial intelligence (AI) applications in the manufacturing domain by presenting a decision support mechanism for MES aimed at production coordination. Machine learning (ML) was used to develop an anomaly detection algorithm for multi-agent based MES to facilitate autonomous production execution and process optimization (in this paper switching the machine off after anomaly detection on the production line)
. Thus, MES executes the ‘turning off’ of the machine without human intervention. The contribution of the paper includes a concept of next-generation MES that has embedded AI, i.e., a MES system architecture combined with machine learning (ML) technique for multi-agent MES. Future research directions are also put forward in this position paper.
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