Investigation of electrical power consumption of an additive process chain and empirical modelling as feature selection for machine learning algorithms

Abstract

The focus on the fourth industrial revolution and advancements in 3D printing has reignited the need for energy efficient manufacturing. In particular, Selective Laser Melting (SLM), an additive manufacturing process, has garnered wide attention owing to its adaptability in producing lightweight components for metal industries. Reasonable material demand along with environmental and methodical capabilities of SLM machines has opened up an intriguing possibility to examine its power consumption as well as to determine its suitability for energy efficient manufacturing. In addition, the energy demand of SLM machines along with its occupancy time in a factory floor poses challenges to energy supply grid and subsequent effects on energy flexibility. Hence, it is necessary to determine energy demand of SLM process chain. This paper provides an empirical power consumption analysis of an additive process chain and interprets the power utilized by various process steps of an SLM machine.