Model-Based Planning of Resource Efficient Process Chains Using System Entity Structures

  • R. Larek
  • E. Brinksmeier
  • T. Pawletta
  • O. Hagendorf
Conference paper

Abstract

Whereas in the past the sustainable use of resources and the reduction of waste have mainly been looked at from an ecological point of view, resource efficiency recently becomes more and more an issue of cost saving as well. Before resource consumption in manufacturing processes can be reduced systematically it is necessary to quantify the amount of resources needed, to identify major consumers and to determine the available degrees of freedom for a reduction. Simulation can be an adequate tool; however, most of the available simulation methods are not suitable for this task. Therefore in close cooperation of production engineering and computer science a new method is being developed to quantify and numerically optimize the resource consumption of manufacturing process chains. In particular, the resources electrical energy, raw material, cooling lubricants and tool wear are taken into consideration. The concept will be demonstrated exemplary in this paper for the consumption of electrical energy.

A new, system theoretical approach, the System Entity Structure framework (SES) is utilized to define alternative manufacturing sequences and parameter sets independently of a specific manufacturing task in a meta-model. Furthermore, a model base is used as a library to store specific models for each relevant manufacturing process. The concept of the so-called basis models is based on the discrete-event simulation. It has been adapted to model machining procedures and to generate workpiece-specific resource consumption profiles and footprints.

A search and optimization algorithm is able to examine valid combinations of manufacturing processes and parameter sets in the meta-model for user-specific manufacturing tasks. The optimization result is a parameter and structure optimal model of the process chain which is transferable to the real planning task.

Keywords

Tool Wear Resource Consumption Decision Node Variable Branch Atomic Entity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The interdisciplinary research in this project is funded by the German Research Foundation DFG.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • R. Larek
    • E. Brinksmeier
      • 1
    • T. Pawletta
      • 1
    • O. Hagendorf
      • 2
    1. 1.Foundation Institute of Materials ScienceTU BremenBremenGermany
    2. 2.Research Group Computational Engineering and Automation, HS WismarWismarGermany

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