CASOA: An Architecture for Agent-Based Manufacturing System in the Context of Industry 4.0

The fourth industrial revolution involves the advanced topics, such as industrial Internet of Things, cyber-physical system and smart manufacturing that address increasing demands for mass customized manufacturing. The agent-based manufacturing is a highly distributed control paradigm that can cope with these challenges well. This paper gives an overview of agent-based architectures for manufacturing systems. Besides, a cloud-assisted self-organized architecture is presented by comprising smart agents and cloud to communicate and negotiate through networks. Ontological representations of knowledge base are constructed to provide the information basis for decision-making of agents, which enables dynamic reconfiguration among agents in a collaborative way to achieve agility and flexibility. Furthermore, the agents’ interaction behavior is modeled to structure the agents hierarchically to reduce the complexity, because the interactions among agents in distributed system are difficult to understand and predict. The experimental results show that the presented architecture can be easily deployed to build smart manufacturing system and can improve the adaptiveness and robustness of the manufacturing system when dealing with mixed multi-product tasks.

[1]  Helmut Seitz,et al.  The Impact of Public Infrastructure Capital on Regional Manufacturing Production Cost , 1995 .

[2]  Hao Tang,et al.  A big data enabled load-balancing control for smart manufacturing of Industry 4.0 , 2017, Cluster Computing.

[3]  Botond Kádár,et al.  Capacity Planning and Resource Allocation in Assembly Systems Consisting of Dedicated and Reconfigurable Lines , 2014 .

[4]  José Barbosa,et al.  Dynamic self-organization in holonic multi-agent manufacturing systems: The ADACOR evolution , 2015, Comput. Ind..

[5]  Sidi Mohamed Benslimane,et al.  Ontology based Web Application Reverse-Engineering Approach , 2007 .

[6]  Jiafu Wan,et al.  Industrial Big Data for Fault Diagnosis: Taxonomy, Review, and Applications , 2017, IEEE Access.

[7]  Jaime Lloret,et al.  Context-Aware Cloud Robotics for Material Handling in Cognitive Industrial Internet of Things , 2018, IEEE Internet of Things Journal.

[8]  Alexandre Tadeu Simon,et al.  Integrating value stream mapping and discrete events simulation as decision making tools in operation management , 2015 .

[9]  Longhi Sauro,et al.  A scalable production efficiency tool for the robotic cloud in the fractal factory , 2016 .

[10]  Luís Ferreira Pires,et al.  Towards a Commitment-Based Reference Ontology for Services , 2013, 2013 17th IEEE International Enterprise Distributed Object Computing Conference.

[11]  Paolo Renna,et al.  Capacity reconfiguration management in reconfigurable manufacturing systems , 2010 .

[12]  Botond Kádár,et al.  Capacity management for assembly systems with dedicated and reconfigurable resources , 2014 .

[13]  Yuan Fan,et al.  Centralized event-triggered control of multi-agent systems with dynamic triggering mechanisms , 2015, The 27th Chinese Control and Decision Conference (2015 CCDC).

[14]  Onur Kuzgunkaya,et al.  Impact of reconfiguration characteristics for capacity investment strategies in manufacturing systems , 2012 .

[15]  Luc Bongaerts,et al.  Reference architecture for holonic manufacturing systems: PROSA , 1998 .

[16]  Athanasios V. Vasilakos,et al.  Software-Defined Industrial Internet of Things in the Context of Industry 4.0 , 2016, IEEE Sensors Journal.

[17]  Paul Valckenaers,et al.  Holonic Manufacturing Execution Systems , 2005 .

[18]  Jiafu Wan,et al.  Cloud-based smart manufacturing for personalized candy packing application , 2016, The Journal of Supercomputing.

[19]  Lina Nemuraite,et al.  TRANSFORMING ONTOLOGY REPRESENTATION FROM OWL TO RELATIONAL DATABASE , 2006 .

[20]  Angappa Gunasekaran,et al.  Logical reconfiguration of reconfigurable manufacturing systems with stream of variations modelling: a stochastic two-stage programming and shortest path model , 2014 .

[21]  Athanasios V. Vasilakos,et al.  A Manufacturing Big Data Solution for Active Preventive Maintenance , 2017, IEEE Transactions on Industrial Informatics.