Abstract
Industrial production systems for smart factories or the so-called Industry 4.0 will demand high interoperability and connectivity between production modules, so that modules could be monitored in real-time. Production modules should make decisions on their own without human intervention; and they must be modular and adaptive to changing circumstances and customers’ requirements. The autonomous operation of production modules in smart factories imposes asynchronous delays due to several reasons, such as object recognition time, grasping time or welding delays that change due to a newly reoriented or positioned component. Consequently, production modules need to be speeded up to compensate for the delays in the previous production stages. In this paper, we present a novel Reconfigurable Distributed Controller (RDC) for Intelligent Robotic Welding and Assembly Systems that autonomously compensate the production delays. The proposed RDC compensates for three types of major production delays that affect the total production time. (I) The first delay can occur at individual level. In this case, the module can fully compensate, since no other modules are affected and the total production time for this product can be met. (II) The second type of delay occurs at inter-module level, where delays are so long that more than one production module will need to be reconfigured. (III) Finally, the third type of delay occurs in the worst-case scenario when the total production time cannot be met by modifying individual module’s production time. A total cell reconfiguration is needed, which implies to speed up the next production cycle to deliver the following product before its deadline. By doing so, the mean production time is maintained. In this paper, issues and experiments that show the feasibility of the RDC are presented. Results of using a distributed reconfigurable manufacturing cell composed of three industrial robots, conveyor belts, and a positioning table demonstrated the effectiveness of our approach to compensate the major delays in real working environments.
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Quigley M, Conley K, Gerkey B et al (2009) ROS: an open-source robot operating system. ICRA Workshop Open Source Softw 3(3):5
Sanfilippo F, Hatledal LI, Zhang H et al (2015) Controlling Kuka industrial robots: flexible communication interface JOpenShowVar. IEEE Robot Autom Mag 22(4):96–109
Shrouf F, Ordieres J, Miragliotta G (2014) Smart factories in Industry 4.0: a review of the concept and of energy management approached in production based on the internet of things paradigm. In: 2014 IEEE international conference on industrial engineering and engineering management, vol 1, Bandar Sunway. IEEE, pp 697–701
Wu D, Greer MJ, Rosen DW et al (2013) Cloud manufacturing: strategic vision and state-of-the-art. J Manuf Syst 32(4):564–579
Tang H, Huang Q, Zhang M et al (2014) Dynamic resource scheduling system with cloud. Mech Eng Autom 6:4–6
Yuan M, Deng K, Chaovalitwongse W et al (2017) Multi-objective optimal scheduling of reconfigurable assembly line for cloud manufacturing. Optim Methods Softw 32(3):581–593
Georgios A, Georgios F, Bouzakis KD (2015) Collaborative design in the era of cloud computing. Adv Eng Softw 81:66–72
Pei J, Liu X, Pardalos PM et al (2016) Solving a supply chain scheduling problem with non-identical job sizes and release times by applying a novel effective heuristic algorithm. Int J Syst Sci 47(4):765–776
Pei J, Pardalos P, Liu X et al (2015) Serial batching scheduling of deteriorating jobs in a two-stage. Eur J Oper Res 244(1):13–25
Yu Z, Guo F, Ouyang J et al (2016) Object-oriented petri nets and π-calculus-based modeling and analysis of reconfigurable manufacturing systems. Adv Mech Eng 8(11):1
Navarro-Gonzalez J, Lopez-Juarez I, Rios-Cabrera R et al (2015) On-line knowledge acquisition and enhancement in robotic assembly tasks. Robot Comput Integr Manuf 33:78–89
Aviles-Vinas JF, Rios-Cabrera R, Lopez-Juarez I (2016) On-line learning of welding bead geometry in industrial robots. Int J Adv Manuf Technol 83(1):217–231
Benitez Perez H, Lopez Juarez I, Garza Alanis PC et al (2016) Reconfiguration distributed objects in an intelligent manufacturing cell. IEEE Latin America Trans 14(1):136–146
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Maldonado-Ramirez, A., Lopez-Juarez, I., Rios-Cabrera, R. (2019). Reconfigurable Distributed Controller for Welding and Assembly Robotic Systems: Issues and Experiments. In: Chen, S., Zhang, Y., Feng, Z. (eds) Transactions on Intelligent Welding Manufacturing. Transactions on Intelligent Welding Manufacturing. Springer, Singapore. https://doi.org/10.1007/978-981-10-8740-0_2
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DOI: https://doi.org/10.1007/978-981-10-8740-0_2
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