Simulation and Prediction of Process-Related Power Consumption of Machine Tools

Abstract:

Today a criterion for the quality of modern machine tools is their efficient use of energy and resources. Competitiveness can only be maintained if the necessity to significantly reduce the energy consumption of machine tools, plants and facilities is put into practice. Another important aspect is the need to preserve our environment. In order to be able to energetically optimize machine tools, it is essential to know how much energy they consume at all. Hence, this paper deals with modelling the energy consumption to be expected from machining operations. Using these estimations and predictions provides a starting point for carrying out energetic optimizations. The method developed here is presented by example of turning operations. The model comprises the machine components directly associated with the cutting process as well as mediate components such as auxiliary units and peripherals. Experimental tests serve to verify the accuracy of the model. Then the energy consumed by individual components is provisionally categorized as well as their proportion of the total amount of energy consumption. Following this, it is possible to analyze the potentials for saving energy. In future it is intended to minimize and condense the complexity of the model so that it will be possible to simultaneously conduct the simulation on the NC control during the cutting operation. In this way it might become possible to influence the energy state of machine tools in realtime during machining, using expert knowledge about the adaptive control of components and drives, the adjustment of control parameters on the fly or the change of parameters such as cutting speed, feed rate and depth of cut. The investigations presented here were funded by the German Research Foundation within the Research Unit 1088 ECOMATION.

• Periodical: Advanced Materials Research (Volume 907)
• Pages: 405-416
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.907.405
• Online since: April 2014
• Authors: Uwe Heisel*, Steffen Braun

References

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DOI: 10.1007/978-3-642-19692-8_60

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