Multi-level simulation concept for multidisciplinary analysis and optimization of production systems

In the context of digitization and industry 4.0, the production-related disciplines developed powerful simulation models with different scopes and varying levels of detail. As these simulation systems are usually not built in a compatible way, the models cannot be combined easily. Co-simulation techniques provide a promising basis for combining these models into one superordinate model and utilizing it for planning new factories, adapting existing ones or for production planning. However, today’s co-simulation systems do not benefit from the inherent flexibility of the represented production systems. Simulation-based optimization is carried out inside each discipline’s simulation system, which means that interdisciplinary, global optima are often impossible to reach. Additionally, the aspect of human interaction with such complex co-simulation systems is often disregarded. Addressing these two issues, this paper presents a concept for combining different simulation models to interdisciplinary multi-level simulations of production systems. In this concept, the inherent flexibilities are capitalized to enhance the flexibility and performance of production systems. The concept includes three hierarchical levels of production systems and allows human interaction with the simulation system. These three levels are the Process Simulation level, the Factory Simulation level, and the Human Interaction level, but the concept is easily extendable to support additional levels. Within the multi-level structure, each simulation system carries out a multi-objective optimization. Pareto-optimal solutions are forwarded to simulations on higher hierarchical levels in order to combine them and meet flexibly adaptable objectives of the entire production system. The concept is tested by means of a simplified production system, to optimize it in terms of throughput time and electric energy consumption. Results show that the presented interdisciplinary combination of heterogeneous simulation models in multi-level simulations has the potential to optimize the productivity and efficiency of production systems.

[1]  Fritz Klocke,et al.  FE-simulation of the Cutting Process under Consideration of Cutting Fluid ☆ , 2017 .

[2]  Petra Wiederkehr,et al.  Point-based tool representations for modeling complex tool shapes and runout for the simulation of process forces and chatter vibrations , 2018 .

[3]  Denis Kurle,et al.  Multi-level simulation in manufacturing companies: The water-energy nexus case , 2016 .

[4]  Jürgen Roßmann,et al.  Simulation in the woods: From remote sensing based data acquisition and processing to various simulation applications , 2011, Proceedings of the 2011 Winter Simulation Conference (WSC).

[5]  Petra Wiederkehr,et al.  Stochastic modeling of grain wear in geometric physically-based grinding simulations , 2018 .

[6]  Iva Kovacic,et al.  Co-simulation environment for optimizing energy efficiency in production systems , 2014 .

[7]  Petra Wiederkehr,et al.  Conference on Intelligent Computation in Manufacturing Engineering-CIRP ICME ' 17 Simulation-assisted investigation of the electric power consumption of milling processes and machine tools , 2018 .

[8]  Christian Brecher,et al.  Interaction of manufacturing process and machine tool , 2009 .

[9]  Lan Yan,et al.  Optimizing the geometric parameters of cutting edge for finishing machining of Fe-Cr-Ni stainless steel , 2017 .

[10]  Marco Taisch,et al.  Modelling, simulation and evaluation of energy consumptions for a manufacturing production line , 2013, IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society.

[11]  Berend Denkena,et al.  Prediction of Temperature Induced Shape Deviations in dry Milling , 2015 .

[12]  Chee Khiang Pang,et al.  Energy saving and monitoring technologies in manufacturing systems with industrial case studies , 2012, 2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA).

[13]  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 .

[14]  Judith S. Dahmann,et al.  Creating Computer Simulation Systems: An Introduction to the High Level Architecture , 1999 .

[15]  Barbara Linke,et al.  Sustainability of abrasive processes , 2013 .

[16]  Y. Feng,et al.  Active energy saving strategy for sensible manufacturing systems operation based on real time production status , 2016, 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM).

[17]  Xifan Yao,et al.  Multi-level Modeling for Hybrid Manufacturing Systems Using Arena and MATLAB , 2008, 2008 International Workshop on Modelling, Simulation and Optimization.

[18]  Christian Brecher,et al.  Modelling and simulation of process: machine interaction in grinding , 2009, Prod. Eng..

[19]  John E. Renaud,et al.  Multiobjective Collaborative Optimization , 1997 .

[20]  Malte Schönemann,et al.  Multiscale Simulation Approach for Battery Production Systems , 2017 .

[21]  J. Oakland.  Leadership and policy deployment: The backbone of TQM , 2011 .

[22]  Christoph Herrmann,et al.  Multi-level Modeling and Simulation of Manufacturing Systems for Lightweight Automotive Components , 2016 .

[23]  T. Siebrecht,et al.  Grinding process simulation of free-formed WC-Co hard material coated surfaces on machining centers using poisson-disk sampled dexel representations , 2014 .

[24]  Wolfgang Kastner,et al.  Energy efficient production - a holistic modeling approach , 2011, 2011 World Congress on Sustainable Technologies (WCST).

[25]  Richard Greenough,et al.  A NEW MODELLING APPROACH WHICH COMBINES ENERGY FLOWS IN MANUFACTURING WITH THOSE IN A FACTORY BUILDING , 2011 .

[26]  Stephan Biller,et al.  Energy efficiency analysis for a casting production system , 2011, Proceedings of the 2011 Winter Simulation Conference (WSC).

[27]  Gunther Reinhart,et al.  A Petri-net Based Approach for Evaluating Energy Flexibility of Production Machines , 2014 .

[28]  Charles R. McLean,et al.  An Architecture and Interfaces for Distributed Manufacturing Simulation , 2004, Simul..

[29]  Jürgen Roßmann,et al.  Finite Element Analysis as a Key Functionality for eRobotics to Predict the Interdependencies between Robot Control and Structural Deformation , 2018 .

[30]  Marc Priggemeyer,et al.  Experimentable Digital Twins—Streamlining Simulation-Based Systems Engineering for Industry 4.0 , 2018, IEEE Transactions on Industrial Informatics.

[31]  Petra Wiederkehr,et al.  Virtual Machining: Capabilities and Challenges of Process Simulations in the Aerospace Industry , 2016 .

[32]  Thomas Lindsay Jackson,et al.  Hoshin Kanri for the Lean Enterprise: Developing Competitive Capabilities and Managing Profit , 2006 .

[33]  A. J. de Wit,et al.  Overview of Methods for Multi-Level and/or Multi-Disciplinary Optimization , 2010 .

[34]  Berend Denkena,et al.  Virtual process systems for part machining operations , 2014 .

[35]  Jürgen Roßmann,et al.  A Flexible Framework to Model and Evaluate Factory Control Systems in Virtual Testbeds , 2018 .

[36]  Yiming Yan,et al.  Collaborative multi-objective optimization for distributed design of complex products , 2018, GECCO.

[37]  Rainer Drath,et al.  AutomationML - the glue for seamless automation engineering , 2008, 2008 IEEE International Conference on Emerging Technologies and Factory Automation.

[38]  Mélanie Despeisse,et al.  Sustainable manufacturing tactics and cross-functional factory modelling , 2013 .

[39]  Tullio Tolio,et al.  Integrated process and system modelling for the design of material recycling systems , 2013 .

[40]  T. Schmitz,et al.  Machining Dynamics: Frequency Response to Improved Productivity , 2008 .

[41]  Daniel Losch,et al.  3D simulation-based user interfaces for a highly-reconfigurable industrial assembly cell , 2016, 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA).