Energy consumption modeling and parameter identification based on system decomposition of welding robots

• Published in: International journal of advanced manufacturing technology Vol. 130; no. 3-4; pp. 1579 - 1594
• Main Authors: Xiao, Wei, Han, Guirong, Ally, Ahmed Suleiman, Chen, Xubing
• Format: Journal Article
• Language: English
• Published: London Springer London 2024; Springer Nature B.V
• Subjects: Angular velocity; Arc welding; CAE) and Design; Computer-Aided Engineering (CAD; Decomposition; Energy consumption; Engineering; Industrial and Production Engineering; Mathematical models; Mechanical Engineering; Media Management; Modelling; Original Article; Parameter identification; Robots; Welding machines; Welding parameters; Parameter identification; Energy consumption model; Welding robots; System decomposition
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-023-12780-5

Accurate prediction of the energy consumption (EC) of welding robots is the basis for energy efficiency optimization. However, it is difficult to establish an accurate EC model for predicting the EC of welding robots when the electromechanical parameters are unknown. This study proposes a modeling method based on the system decomposition of welding robots, which enables rapid calculation of their EC. An arc welding robot (AWR) is considered to be consisted of two independent systems: the robot system (RS) and the welding machine system (WMS). Models are established to map the relationship between EC and parameters such as joint torque and joint angular velocity for the RS, and parameters such as the current and voltage during welding for the WMS, respectively. EC data of operating ABB robots and Fronius CMT welding machines are collected separately for parameter identification of the EC models. The experimental results show that this method can accurately and fast forecast the real-time power and total EC of AWRs, without the knowledge of the electrical parameters of the robot and welding machine.