A novel method to forecast energy consumption of selective laser melting processes

Abstract

As a promising additive manufacturing (AM) technology, the applications of selective laser melting (SLM) are expanding. Yet, due to the complex structure of SLM machines and low processing rates, the SLM process is highly energy-intensive. Energy forecasting is crucial for accurate evaluation and reduction of SLM energy consumption. However, due to the diversity of SLM machines and their various operating states, the energy consumption of SLM processes is difficult to predict. This article presents a novel method to forecast the energy consumption of SLM processes. The proposed approach is based on the power modelling of machine subsystems and the temporal modelling of sub-processes. Through identifying the working statuses of subsystems of SLM machines in each sub-process, forecast accuracy can be greatly improved. Two cases of aluminium components fabricated by an SLM process using an SLM 280HL facility are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method outperforms specific, stage-based and subsystem-based energy benchmark models in energy consumption forecasting.

Keywords:

• 3D printing
• additive manufacturing
• energy consumption
• selective laser melting
• energy saving

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Natural Science Foundation of Zhejiang Province [grant number LY19E050019]; National Natural Science Foundation of China [grant number 51505423, 51675441]; the International Clean Energy Talent Program (iCET) of China Scholarship Council [grant number Liujinfa[2017]5047, Liujinfa[2018]5023] and the Fundamental Research Funds for the Central Universities, CHD [grant number 300102250303, 300102259201].