Johannes Glasschröder

Measurement of the Resource Consumption of a Selective Laser Melting Process

One important purchasing criterion for end customers is the resource consumption of products, which manufacturers aim to reduce through sustainable product designs and optimization of production processes. In order to quantify the resource consumption, in this study the demand of raw materials and operating materials of the selective laser melting process was quantified according to the methodology developed within the initiative Cooperative Effort on Process Emissions in Manufacturing (CO2PE!). The selective laser melting process was selected due to two reasons. First, the process enables lightweight constructions, which offers the potential to reduce the resource consumption during the product use phase. Second, few studies have been published about this process so far which also measure the demand of compressed air and shielding gas apart from the electric energy demand. It was found that the resource demand for the manufactured 0.5 cm3 cuboid part amounted to 3.6 kWh electric energy, 0.81 m3 compressed air and 0.31 m3 Argon. This corresponds to an energy demand of nearly 1000 kWh/kg, though such key performance indicators alone are not very representative for the selective laser melting process, as described below.

Improving Resource and Energy Efficiency of Packaging Machines: Contribution of an Increasing Format Flexibility

Manufacturers and users of packaging machines are facing two significant challenges: the improvement of the sustainability and the enhancement of the format flexibility. The sustainability of products is becoming a more important influencing factor for the consumer buying behavior. Therefore, the increase of the energy and resource efficiency of packaging machines is a very promising strategy to achieve this goal. At first glance no correlation between format flexibility and sustainability seems to exist. But in fact, both topics partly depend on each other. These connections are the focus of this paper. Exemplary for sustainability, the resource and energy efficiency in the context of packaging machines will be considered. As a further detailing, this paper also contains an experimental investigation of a more format flexible and sustainable processing technology for sealing. Therefore, the heat-impulse technology was investigated.

Scenario Based Method to Increase the Flexibility of Energy Use in Production Systems

The paper presents a method to increase the energy flexibility of production systems, establishing a tool to increase the energy flexibility by using the scenario technique. First of all barriers and possible modifications to influence the energy consumption have to be identified. Afterwards, measures can be designed order to address these obstacles. Impacts of the energy conduct on the material flow and production performance indicators will be investigated. These results are necessary for investment decisions for future production plants.

Cost-Efficient Energy Monitoring of Manufacturing Machines Based on Nonintrusive Load Monitoring

In order to increase the energy efficiency of manufacturing machines, knowledge about their energy consumption is necessary, preferably on machine component level and including the temporal behavior. To gain such information, nowadays dozens of current clamps have to be installed in the machine by a professional. A more cost-efficient alternative could be the use of non-intrusive load monitoring (NILM), which is already popular in households. This paper highlights several challenges when trying to transfer NILM from households to manufacturing machines. It then focuses on a previously unresolved problem of disaggregating motors with a time varying active power demand and presents a new solution, which was successfully applied to a thermoform machine.

Kostengünstige Energiemessungen: Eignung von Non-Intrusive Load Monitoring für Maschinen in der Fertigungsindustrie

Kurzfassung Um die Energieeffizienz von Maschinen zu steigern, ist eine Kenntnis des elektrischen Energieverbrauchs auf Komponentenebene notwendig. Dazu wird normalerweise an jeder Komponente ein Messsensor installiert, was jedoch hohe Hardware- und Installationskosten verursacht. Eine alternative Messmethode ist das sogenannte Non-intrusive Load Monitoring, welches in Privathaushalten bereits erfolgreich eingesetzt wird. In diesem Beitrag wird untersucht, wie geeignet die Methode für in der Fertigungsindustrie eingesetzte Maschinen ist.

Steigerung der Energieflexibilität von Fabriken

Kurzfassung Die Energieflexibilität von Fabriken muss aufgrund des Ausbaus erneuerbarer Energien und der wachesenden Nachfrage zur Anpassung des Energiebedarfs gesteigert werden. Zu diesem Zweck gilt es Flexibilitätspotenziale zu erkennen und gezielt auszubauen. Diese können anschließend in der Betriebsphase zur Steuerung des Energiebedarfs eingesetzt werden. Im vorliegenden Beitrag soll auf die Steigerung der Energieflexibilität eingegangen und ein Vorgehen vorgestellt werden.

Comparison of Prognosis Methods for the Energy Consumption of Machines and Further Development with Regard to Increasing Data Availability

An increasing number of companies establish energy management systems for continuous improvement in their energy efficiency and for this intensify monitoring their current energy consumption. These data can be used to gain further information about the production and to find potentials to increase its energy efficiency. In the procurement process of machinery and equipment or in the planning phase of production systems and building services, information about energy demand is rarely available, though it would be valuable for an early inclusion of energy efficiency in these processes. Therefore this paper discusses different forecast methods for energy consumption of machinery and evaluates in particular their universal applicability, effort and accuracy by analyzing them through the example of a packaging machine. In addition this paper proposes a further usage of energy-related data of machinery, which can be automatically acquired by monitoring systems for prognosticating their energy consumption as well as a possible distribution approach of this information. Therefore an own forecast method is presented, which shall process the energy-related data combined with information about dominant parameters of the product, the usage of the machine and the environmental conditions. For the distribution concept it was taken into account that the generated and shared information has to be abstracted in a way that no critical secrets of the company are revealed.

Feature Extraction and Classification of the Electric Current Signal of an Induction Motor for Condition Monitoring Purposes

A high availability of machines has always been important in production. One way to increase it is to avoid unscheduled production stops by detecting the onset of machine faults and to conduct preventative repairs. The detection part consists of the three steps signal acquisition, feature extraction and classification. This paper focuses on the last two steps through the example of an induction motor. Based on a publicly available motor current data set, features were extracted using the continuous wavelet transform. In the subsequent classification step eight different classification methods were compared with each other. It was found, that the accuracy of the classifiers varied significantly in a range from 20.6 % to 92.8 %. Moreover, the supportive vector machine, scoring an accuracy of 92.8 %, was the only classifier with an accuracy above 55.0 %.

Approaches for Monitoring the Energy Consumption with Machine Learning Methods

In times of rising energy costs and increasing customer awareness of sustainable production methods, many manufacturers take measures to reduce their energy consumption. However, after the realization of such activities the energy demand often tends to increase again due to e.g. leaks, clogged filters, defect valves or suboptimal parameter settings. In order to prevent this, it is necessary to quickly identify such increases by continuously monitoring the energy consumption and counteracting accordingly. Currently, the monitoring is either performed manually or by setting static threshold values. The manual control can be time consuming for large amounts of sensor data. By setting static threshold values only a fraction of the inefficiencies are disclosed. Another option is to use anomaly detection methods from the area of machine learning, which compare the actual sensor values with the expected ones. In this paper an overview about existing anomaly detection methods, which can be applied for this purpose, is presented.