A CPS-Agent self-adaptive quality control platform for industry 4.0

With the emergence of new requirements in manufacturing, mainly the product customization trend, implementing a solution for product quality control becomes an increasingly complicated task. In this modern industrial context, those solutions need to take into account the constant updates in the product design and combine its resources in the most effective manner in order to meet the new product specifications. By reason of the lack of available technical solutions, this task is usually carried out by human operators which is time consuming, tiring and not always a reliable solution. In the present work, a self-* automatic quality control platform based on cyber-physical systems and multi-agents paradigm is presented. The solution is validated within a real industrial usecase.

[1]  Lida Xu,et al.  The internet of things: a survey , 2014, Information Systems Frontiers.

[2]  Ebrahim Mamdani,et al.  Applications of fuzzy algorithms for control of a simple dynamic plant , 1974 .

[3]  Sanja Petrovic,et al.  SURVEY OF DYNAMIC SCHEDULING IN MANUFACTURING SYSTEMS , 2006 .

[4]  Babu Joseph,et al.  Predictive control of quality in a batch manufacturing process using artificial neural network models , 1993 .

[5]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[6]  Dragos Axinte.  Approach into the use of probabilistic neural networks for automated classification of tool malfunctions in broaching , 2006 .

[7]  Jay Lee,et al.  Recent advances and trends in predictive manufacturing systems in big data environment , 2013 .

[8]  N. Jazdi,et al.  Cyber physical systems in the context of Industry 4.0 , 2014, 2014 IEEE International Conference on Automation, Quality and Testing, Robotics.

[9]  H. Tullberg,et al.  The Foundation of the Mobile and Wireless Communications System for 2020 and Beyond: Challenges, Enablers and Technology Solutions , 2013, 2013 IEEE 77th Vehicular Technology Conference (VTC Spring).

[10]  Barbara Messing,et al.  An Introduction to MultiAgent Systems , 2002, Künstliche Intell..

[11]  R. Olfati-Saber,et al.  Distributed Kalman Filter with Embedded Consensus Filters , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[12]  V. Vyatkin,et al.  Multiagent Smart Grid Automation Architecture Based on IEC 61850/61499 Intelligent Logical Nodes , 2012, IEEE Transactions on Industrial Electronics.

[13]  José Barbosa,et al.  Bio-inspired multi-agent systems for reconfigurable manufacturing systems , 2012, Eng. Appl. Artif. Intell..

[14]  Daqiang Zhang,et al.  Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination , 2016, Comput. Networks.

[15]  Jay Lee,et al.  A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems , 2015 .

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

[17]  Russell G. Congalton,et al.  Assessing the accuracy of remotely sensed data : principles and practices , 1998 .

[18]  Soundarr T. Kumara,et al.  Flank Wear Estimation in Turning Through Wavelet Representation of Acoustic Emission Signals , 2000 .

[19]  Deborah F. Cook,et al.  A predictive neural network modelling system for manufacturing process parameters , 1992 .

[20]  Boris Otto,et al.  Design Principles for Industrie 4.0 Scenarios , 2016, 2016 49th Hawaii International Conference on System Sciences (HICSS).