Electrical energy estimation of 3D printing jobs for industrial internet of things (IIoT) applications.

Purpose: This paper aims to develop an architecture for 3D printers in an Industrial Internet of Things (IIoT) controlled automated manufacturing environment. An algorithm is proposed to estimate the electrical energy consumption of 3D printing jobs, which is used, 3D Printing, Sustainable Manufacturing, Industry 4.0, Electrical Energy Estimation, IIoT to schedule printing jobs on optimal electrical tariff rates. Design/methodology/approach: An IIoT-enabled architecture with connected pools of 3D printers and an Electrical Energy Estimation System (EEES) are used to estimate the electrical energy requirement of 3D printing jobs. EEES applied the combination of Maximum Likelihood Estimation and a dynamic programming–based algorithm for estimating the electrical energy consumption of 3D printing jobs. Findings: The proposed algorithm decently estimates the electrical energy required for 3D printing and able to obtain optimal accuracy measures. Experiment results show that the electrical energy usage pattern can be reconstructed with the EEES. It is observed that EEES architecture reduces the peak power demand by scheduling the manufacturing process on low electrical tariff rates. Practical implications: Proposed algorithm is validated with limited number of experiments. Originality/value: IIoT with 3D printers in large numbers is the future technology for the automated manufacturing process where controlling, monitoring and analyzing such mass numbers becomes a challenging task. This paper fulfills the need of an architecture for industries to effectively use 3D printers as the main manufacturing tool with the help of IoT. The electrical estimation algorithm helps to schedule manufacturing processes with right electrical tariff. [ABSTRACT FROM AUTHOR]