Abstract The first step to lower energy impact of products is to quantify the current impact. The mayor energy consumer in machine tools is not the spindle, it‘s the auxiliary units, such as pneumatic, hydraulic system, actuators, chip disposal and others. The quantification is achieved by precise measurements from signals by the PLC with high sampling rates. The analysis of this data is done by Engineering Apps (eApps). The analysis of the energy per component reveals the potential for energy reduction. The reduction can be achieved by switching to a more efficiency component. In this research examples of component substitution measurements have been performed for more efficient electrical motors, cutting fluid pumps, compressors vs. controlled compressors and start-stop hydraulic power packs. The stand-by energy analysis is similar to the component energy analysis and reveals the potential for setting some components to sleep mode during stand-by. Examples of analysed sleep mode components are chip disposal, cutting fluid pump, pneumatic and hydraulic system. As a result the energy usage can be displayed and visualized in various ways such as energy per part, energy per time or energy per shift. The energy usage visualization can be split into usage per component. These results can also be displayed online on mobile devices.
[1]
Engelbert Westkämper,et al.
Life cycle management and assessment: Approaches and visions towards sustainable manufacturing
,
2001
.
[2]
Athulan Vijayaraghavan,et al.
Automated energy monitoring of machine tools
,
2010
.
[3]
H. Kaebernick.
Green manufacturing, fundamentals and applications
,
2014
.
[4]
D. Alting,et al.
The Life Cycle Concept as a Basis for Sustainable Industrial Production
,
1993
.
[5]
S. R. Schmid.
Energy consumption in manufacturing
,
2012
.
[6]
Andrea Matta,et al.
Energy Efficient Control Strategy for Machine Tools with Stochastic Arrivals and Time Dependent Warm-up
,
2014
.
[7]
Andrew Jarvis,et al.
Strategies for Minimum Energy Operation for Precision Machining
,
2009
.
[8]
Makoto Fujishima,et al.
A study on energy efficiency improvement for machine tools
,
2011
.