Open Access

Joachim Brinkmann, Jan C. Aurich, Henrik te Heesen

• Additive manufacturing is an essential tool in innovative production processes. The extended degrees of freedom offer much potential in usage, construction, and product design. Rising raw material and energy costs, constantly increasing environmental requirements, and the increasing demand for resource-saving products represent a paradigm shift in classic production processes. In addition to the purely energetic evaluation, developing energy models is a method to determine energy consumption and reduce it in the long term. The specific energy consumption model, also known as the SEC model, allows a quick estimation of energy consumption by multiplying the SEC with a unit like the mass of the workpiece, the manufacturing time, or the exposed area. Here, high dependence on the used machine, the considered peripheral devices, and the geometry are noticeable. Previous studies, such as those by Kellens et al. and Baumers et al., have laid the basis for understanding the energy demands of PBF-LB/M processes. Various energy models have subsequently been proposed, including those by Paul and Anand, Yi et al., Lv et al., and Hui et al. These models are often limited by their specificity to sub-processes or subsystems. This results in limitations in their applicability to other manufacturing machines or inaccuracies in energy consumption predictions. The simulation accuracy ACC is mostly in the range of 90% with the limitation of small sample sizes. Moreover, nearly, all these models rely heavily on process time information, making the accuracy of their simulations largely dependent on the quality of the underlying time model. In the following study, two manufacturing machines of the PBF-LB/M process are analyzed and compared with other studies. The aim is to analyze the power and resource consumption to use these data to build an improved energy model with a high accuracy, which can be used as an additional parameter in the adapted design methodology. Furthermore, potential savings are derived from the load curves.