A Digital Twin-Based Distributed Manufacturing Execution System for Industry 4.0 with AI-Powered On-The-Fly Replanning Capabilities

Industry 4.0 smart production systems comprise industrial systems and subsystems that need to be integrated in such a way that they are able to support high modularity and reconfigurability of all system components. In today’s industrial production, manufacturing execution systems (MESs) and supervisory control and data acquisition (SCADA) systems are typically in charge of orchestrating and monitoring automated production processes. This article explicates an MES architecture that is capable of autonomously composing, verifying, interpreting, and executing production plans using digital twins and symbolic planning methods. To support more efficient production, the proposed solution assumes that the manufacturing process can be started with an initial production plan that may be relatively inefficient but quickly found by an AI. While executing this initial plan, the AI searches for more efficient alternatives and forwards better solutions to the proposed MES, which is able to seamlessly switch between the currently executed plan and the new plan, even during production. Further, this on-the-fly replanning capability is also applicable when newly identified production circumstances/objectives appear, such as a malfunctioning robot, material shortage, or a last-minute change to a customizable product. Another feature of the proposed MES solution is its distributed operation with multiple instances. Each instance can interpret its part of the production plan, dedicated to a location within the entire production site. All of these MES instances are continuously synchronized, and the actual global or partial (i.e., from the instance perspective) progress of the production is handled in real-time within one common digital twin. This article presents three main contributions: (i) an execution system that is capable of switching seamlessly between an original and a subsequently introduced alternative production plan, (ii) on-the-fly AI-powered planning and replanning of industrial production integrated into a digital twin, and (iii) a distributed MES, which allows for running multiple instances that may depend on topology or specific conditions of a real production plant. All of these outcomes are demonstrated and validated on a use-case utilizing an Industry 4.0 testbed, which is equipped with an automated transport system and several industrial robots. While our solution is tested on a lab-sized production system, the technological base is prepared to be scaled up to larger systems.

[1]  Digital Transformation: Core Technologies and Emerging Topics from a Computer Science Perspective , 2023 .

[2]  P. Renna Special Issue: “The Planning and Scheduling of Manufacturing Systems” , 2022, Applied Sciences.

[3]  M. Pekarcikova,et al.  Comparing Modern Manufacturing Tools and Their Effect on Zero-Defect Manufacturing Strategies , 2022, Applied Sciences.

[4]  Mohamed Rafik N. Qureshi Evaluating Enterprise Resource Planning (ERP) Implementation for Sustainable Supply Chain Management , 2022, Sustainability.

[5]  M. Segovia,et al.  Design, Modeling and Implementation of Digital Twins , 2022, Sensors.

[6]  Petr Novák,et al.  Digitalized Automation Engineering of Industry 4.0 Production Systems and Their Tight Cooperation with Digital Twins , 2022, Processes.

[7]  H. Najjaran,et al.  Intelligent manufacturing execution systems: A systematic review , 2022, Journal of Manufacturing Systems.

[8]  Nikolaos Papakonstantinou,et al.  Roadmap to semi-automatic generation of digital twins for brownfield process plants , 2021, J. Ind. Inf. Integr..

[9]  S. Ogonowski,et al.  Security Challenges in Industry 4.0 PLC Systems , 2021, Applied Sciences.

[10]  Laith A. Hadidi,et al.  Impact of Industry 4.0 and Lean Manufacturing on the Sustainability Performance of Plastic and Petrochemical Organizations in Saudi Arabia , 2021, Sustainability.

[11]  F. Allgöwer,et al.  Model Predictive Control for Flexible Job Shop Scheduling in Industry 4.0 , 2021, Applied Sciences.

[12]  Paolo Renna,et al.  A Literature Review of Energy Efficiency and Sustainability in Manufacturing Systems , 2021, Applied Sciences.

[13]  A. Hellmich,et al.  Digital Twins for High-Tech Machining Applications—A Model-Based Analytics-Ready Approach , 2021, Journal of Manufacturing and Materials Processing.

[14]  Vicente Rodríguez-Montequín,et al.  Development of a Steel Plant Rescheduling Algorithm Based on Batch Decisions , 2021, Applied Sciences.

[15]  Yuansong Qiao,et al.  Digital Twin: Origin to Future , 2021, Applied System Innovation.

[16]  Achim Rettberg,et al.  A Methodology for Digital Twin Modeling and Deployment for Industry 4.0 , 2021, Proceedings of the IEEE.

[17]  Christian Huemer,et al.  Leveraging Iterative Plan Refinement for Reactive Smart Manufacturing Systems , 2021, IEEE Transactions on Automation Science and Engineering.

[18]  Raymond Chiong,et al.  Energy-efficient production scheduling through machine on/off control during preventive maintenance , 2021, Eng. Appl. Artif. Intell..

[19]  Vladimir Modrak,et al.  Implementing Industry 4.0 in SMEs , 2021 .

[20]  Yinghao Zhao,et al.  Robust and Efficient Trajectory Replanning Based on Guiding Path for Quadrotor Fast Autonomous Flight , 2021, Remote. Sens..

[21]  Zhong Fan,et al.  Digital Twin: Enabling Technologies, Challenges and Open Research , 2019, IEEE Access.

[22]  Bernhard Wally,et al.  The Digital Twin as a Core Component for Industry 4.0 Smart Production Planning , 2020 .

[23]  N. Wild,et al.  Investigating the Potential of Smart Manufacturing Technologies , 2020, ISM.

[24]  Alexander Fay,et al.  The development of a digital twin for machining processes for the application in aerospace industry , 2020 .

[25]  Christian Huemer,et al.  A Graphical Toolkit for IEC 62264-2 , 2020 .

[26]  G. Reinhart Handbuch Industrie 4.0 , 2020 .

[27]  Bernhard Wally,et al.  Flexible Production Systems: Automated Generation of Operations Plans Based on ISA-95 and PDDL , 2019, IEEE Robotics and Automation Letters.

[28]  Luca Fumagalli,et al.  Flexible Automation and Intelligent Manufacturing , FAIM 2017 , 27-30 June 2017 , Modena , Italy A review of the roles of Digital Twin in CPS-based production systems , 2017 .

[29]  K. Voigt,et al.  Sustainable Industrial Value Creation: Benefits and Challenges of Industry 4.0 , 2017, Digital Disruptive Innovation.

[30]  A Min Tjoa,et al.  Industrial Applications of Holonic and Multi-Agent Systems: 9th International Conference, HoloMAS 2019, Linz, Austria, August 26–29, 2019, Proceedings , 2019, HoloMAS.

[31]  Guido Ignacio Novoa-Flores,et al.  A Vehicle Routing Problem with Periodic Replanning , 2018, Proceedings.

[32]  Andrew Kusiak,et al.  Data-driven smart manufacturing , 2018, Journal of Manufacturing Systems.

[33]  Li Da Xu,et al.  Industry 4.0: state of the art and future trends , 2018, Int. J. Prod. Res..

[34]  Wilfried Sihn,et al.  Digital Twin in manufacturing: A categorical literature review and classification , 2018 .

[35]  Rolf Steinhilper,et al.  The Digital Twin: Realizing the Cyber-Physical Production System for Industry 4.0☆ , 2017 .

[36]  Birgit Vogel-Heuser,et al.  Handbuch Industrie 4.0 Bd.4, Allgemeine Grundlagen , 2017, Handbuch Industrie 4.0.

[37]  Birgit Vogel-Heuser,et al.  Evolution of software in automated production systems: Challenges and research directions , 2015, J. Syst. Softw..

[38]  Erwin Rauch,et al.  Trends towards Distributed Manufacturing Systems and modern forms for their design , 2015 .

[39]  Alexandre Sousa,et al.  Toward Automated Planning Algorithms Applied to Production and Logistics , 2013 .

[40]  Steve Evans,et al.  Industrial sustainability: challenges, perspectives, actions , 2013 .

[41]  Hakki Ozgur Unver,et al.  An ISA-95-based manufacturing intelligence system in support of lean initiatives , 2013 .

[42]  Alexander Fay,et al.  Automated generation of simulation models for control code tests , 2013 .

[43]  Thilo Sauter,et al.  Functional Analysis of Manufacturing Execution System Distribution , 2011, IEEE Transactions on Industrial Informatics.

[44]  Egon Berghout,et al.  Management of lifecycle costs and benefits: Lessons from information systems practice , 2011, Comput. Ind..

[45]  Malte Helmert,et al.  The Fast Downward Planning System , 2006, J. Artif. Intell. Res..

[46]  Vladimír Marík,et al.  Industrial adoption of agent-based technologies , 2005, IEEE Intelligent Systems.

[47]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..