Using autonomous intelligence to build a smart shop floor

The vision of smart shop floor is based on the notion of Industry 4.0 that denotes technologies and concepts related to Cyber-Physical Production Systems (CPPS). In smart shop floors, CPPS monitors physical processes, creates a virtual copy of the physical world, and makes decentralized decisions. CPPS allows virtual world to store data, process data, communicate, and cooperate with each other in real time. This paper presents architecture of smart shop floor based on physical, logical, and communication layers that embed intelligent approaches within manufacturing processes. Every physical entity in the smart shop floor is regarded as an autonomous intelligent logical unit that performs operations guided by distributed control functions. Moreover, computing power and optimization approaches are embedded into each logical unit to make decisions to agilely respond to frequent occurrence of unexpected disturbances at the shop floor. A test platform has been set up to demonstrate how physical entities can be cooperative and autonomous logical units that can automatize the shop floor operations. The results verify the feasibility and efficiency of the proposed method.

[1]  Paulo Leitão,et al.  ADACOR: A holonic architecture for agile and adaptive manufacturing control , 2006, Comput. Ind..

[2]  Lei Wang,et al.  An improved adaptive genetic algorithm based on hormone modulation mechanism for job-shop scheduling problem , 2011, Expert Syst. Appl..

[3]  Ronald L. Hartung,et al.  An Infrastructure for Individualised and Intelligent Decision-making and Negotiation in Cyber-physical Systems , 2014, KES.

[4]  Eric W.T. Ngai,et al.  Implementing an RFID-based manufacturing process management system: Lessons learned and success factors , 2012 .

[5]  Sankha Deb,et al.  Scheduling optimization of flexible manufacturing system using cuckoo search-based approach , 2013 .

[6]  Lei Wang,et al.  A neuroendocrine-inspired approach for adaptive manufacturing system control , 2011 .

[7]  Reiner Anderl,et al.  Response Behavior Model for Process Deviations in Cyber-Physical Production Systems , 2015, WCE-2015 2015.

[8]  Lin Lin,et al.  Multiobjective evolutionary algorithm for manufacturing scheduling problems: state-of-the-art survey , 2014, J. Intell. Manuf..

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

[10]  Paul Valckenaers,et al.  Benchmarking the performance of manufacturing control systems: design principles for a web-based simulated testbed , 2003, J. Intell. Manuf..

[11]  Adriana Giret,et al.  A hormone regulation–based approach for distributed and on-line scheduling of machines and automated guided vehicles , 2018 .

[12]  Ahmad T. Al-Hammouri,et al.  A comprehensive co-simulation platform for cyber-physical systems , 2012, Comput. Commun..

[13]  Patrick Pujo,et al.  Pull control for job shop: holonic manufacturing system approach using multicriteria decision-making , 2012, J. Intell. Manuf..

[14]  Adriana Giret,et al.  Dynamic shop floor re-scheduling approach inspired by a neuroendocrine regulation mechanism , 2015 .

[15]  Damien Trentesaux,et al.  ORCA-FMS: a dynamic architecture for the optimized and reactive control of flexible manufacturing scheduling , 2014, Comput. Ind..

[16]  Marco Saggiomo,et al.  Applying Multi-objective Optimization Algorithms to a Weaving Machine as Cyber-Physical Production System , 2017 .

[17]  José Barbosa,et al.  Deployment of industrial agents in heterogeneous automation environments , 2015, 2015 IEEE 13th International Conference on Industrial Informatics (INDIN).

[18]  Gunther Reinhart,et al.  Cyber-Physical-Robotics – Modelling of modular robot cells for automated planning and execution of assembly tasks , 2016 .

[19]  Wang Shaolin,et al.  An Integrated Scheme for Cyber-physical Building Energy Management System , 2011 .

[20]  Siddharth Sridhar,et al.  Cyber–Physical System Security for the Electric Power Grid , 2012, Proceedings of the IEEE.

[21]  Adil Baykasoglu,et al.  A multi-agent based approach to dynamic scheduling of machines and automated guided vehicles in manufacturing systems , 2012, Appl. Soft Comput..

[22]  Remzi Seker,et al.  Big Data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook , 2016, Comput. Ind..

[23]  Robert Pellerin,et al.  Simultaneous control of maintenance and production rates of a manufacturing system with defective products , 2012, J. Intell. Manuf..

[24]  J. Jerald,et al.  Scheduling of machines and automated guided vehicles in FMS using differential evolution , 2010 .

[25]  Detlef Zühlke,et al.  Interdisciplinary Engineering Methodology for changeable Cyber-Physical Production Systems , 2016 .

[26]  Simon Bergweiler.  Intelligent Manufacturing based on Self-Monitoring Cyber-Physical Systems , 2015 .

[27]  Manoj Kumar Tiwari,et al.  Bidding-based multi-agent system for integrated process planning and scheduling: a data-mining and hybrid tabu-SA algorithm-oriented approach , 2007 .

[28]  Ivo Pereira,et al.  Redundant and Decentralised Directory Facilitator for Resilient Plug and Produce Cyber Physical Production Systems , 2016, SOHOMA.

[29]  Paulo Leitão,et al.  An agile and adaptive holonic architecture for manufacturing control , 2004 .