Title: Evaluating the Cumulative Energy Demand of Additive Manufacturing using Direct Energy Deposition
Name: M. Sc. Svenja Ehmsen
Phone: +49 631 205 5448
Additive manufacturing (AM) defines a process in which parts are produced by applying material in layers. Therefore, only the material that is actually needed for the part is processed. The resulting realization of complex geometries offers great design freedom and the opportunity of topological optimization as well as lightweight design, which not only saves material in the manufacturing process but also reduces energy demand in the use phase. For this reason, AM is often claimed as an environmentally friendly and resource efficient technology. However, previous investigations have shown that the specific energy demand of AM is higher than the energy demand of established processes such as machining.
The energy demand of an AM-part depends on a set of influencing factors. Therefore, a consideration of the AM process itself, without considering preceding or subsequent processes, is not sufficient for a comprehensive energy analysis of the technology. Furthermore, previous analyses of energy demand are part-specific and can only be transferred to other components to a limited extent. Therefore, the aim of this project is to develop a generally valid simulation tool which calculate the cumulative energy demand (CED) for the AM technology direct energy deposition (DED). This technology processes metal, which is melted as it is being deposited. Due to its shorter processing time and higher forming accuracy compared to other metal-based technologies, DED is one of the most promising AM technologies that gains increasing attention in industry.
The considered process chain includes the process steps of raw material extraction, powder production, DED and post-processing and therefore encompasses a cradle-to-gate perspective.
First of all, it is necessary to define the scope of the tool and to set system boundaries. Afterwards, each single process step and its energy-related input factors as well as their correlation are identified. To calculate the CED in a generally valid way, calculation models are developed based on these correlations and dependencies. Based on these models the tool is developed. The tool is then iteratively optimized and finally validated by measuring and calculating the CED of reference parts to compare their CED with the predicted CED of the tool.
The expected result is a CED calculation tool, that determines an approximate CED for a DED process from cradle-to-gate. Here, the CED in total as well as the CED for individual process steps or input factors are calculated. Thus, the key factors that have a significant impact on the CED are identified.