Zustandsmodellbasierte, steuerungsnahe Energieverbrauchsoptimierung von Werkzeugmaschinen

In recent years, resource efficiency and the environment protection has increased in importance for the industry. The pressure on companies based on the energy efficiency rises due to national and international policies, rising electricity prices, market competition but also directly by expectations of the society. To continue to operate efficiently, industrial companies pursue the goal to reduce their energy costs. During the operation of machine tools, especially during non-productive times, electrical energy is wasted, because, although power saving states are state of the art, these are not used efficiently. Therefore, in this work, the aim is pursued to bridge interruptions in production energy-optimally. A configurable, energy-model-based solution for the optimization of energy consumption during production interruptions is developed. First, a method is analyzed and developed that allows the use of the operating state model for calculating consumption and detection of the current operating state during the operation of machine tools. In order to define and parameterize this model with significantly reduce effort, a method for partially-automated state modeling is developed. It also allows the integration of expert knowledge. The configured operating state model can then be used in the optimization of energy consumption. To complete the work, a method for model-based energy consumption optimization is developed. The optimization method uses a graph-based search algorithms for the determination of the energy-optimal operation state sequences during non-productive times. The applicability of the developed methods is validated on the demonstration machine Exeron Digma HSC600 within a control framework

Multi-level Energy Demand Optimizer System for Machine Tool Controls

The need to increase resource and energy efficiency for a sustainable production has led to saving potential analyses and afterwards saving strategies in a multitude of disciplines: product design, supply chain management, process chain design, production process development, energy-optimal machine or component control and even machine tool and component design. Each of those strategies resulted in numerous improvements, however, they still lack reciprocal consideration. To overcome this deficit, a machine-independent energy control system to include any control- or operation-based energy optimizer will be introduced in this paper. It is based on real-time control information from the machine and software-based energy demand optimizers targeting the machining process, the machine tool components control as well as the overlaying production process. The control system itself ensures the correct cooperative operation of the three optimizer types, to enable the much needed reciprocal consideration of the optimizer's effects

ECOMATION - Energieeffiziente Steuerung von Werkzeugmaschinen

Aktuelle Maßnahmen zur Steigerung der Energieeffizienz von Werkzeugmaschinen und Fertigungseinrichtungen beschränken sich maßgeblich auf die Verbesserung oder den Austausch von Einzelkomponenten. Hiermit kann zwar der Energiebedarf bereits gesenkt werden, jedoch liegt dieser noch deutlich über dem technisch möglichen Wert. Zum einen muss die Flexibilität der Werkzeugmaschine bezüglich der Prozessfähigkeit erhalten bleiben, so dass nur eine nutzungsübergreifen Optimierung erfolgen kann. Zum anderen wird nicht das Gesamtsystem optimiert, so dass hier noch Einsparpotentiale ungenutzt bleiben. Im Rahmen der DFG Forschergruppe ECOMATION wird untersucht, ob auf Basis regelungs- und betriebstechnischer Maßnahmen der Energiebedarf von bestehenden Werkzeugmaschinen gesenkt und das Wissen in den Entwurf neuer Maschinen übernommen werden kann. Dazu werden Modelle zur Energieverbrauchsvorhersage und eine Beschreibungssprache zur rechnergestützten Nutzung dieser Modelle entwickelt

Modular Modeling of Energy Consumption for Monitoring and Control

A prerequisite to achieve energy efficiency in production through optimization is the possibility to use simulation models to predict the consequences settings, decisions and actions have on the energy consumption and thus on the lifecycle cost of production machines. But today’s production systems are dominated by agile structures and rapid changes of general conditions so that predictive energy consumption models need to be adaptable. Therefore in the ECOMATION project the modular modeling approach for energy consumption prediction presented here is being developed, which facilitates re-use of energy consumption models and thus minimizes the cost of finding the optimal operational profiles