A prediction approach of fiber laser surface treatment using ensemble of metamodels considering energy consumption and processing quality

Abstract

Laser surface treatment (LST) is essential for advanced manufacturing but is extremely energy intensive. Being energy-aware is imperative as the industry pays increasing attention to energy management and environmental protection. However, existing literature mainly focuses on the laser-material interaction in LST, while few studies have considered energy consumption when investigating the processing quality. In this article, three metamodels (Kriging, RBF, and SVR) are integrated into an ensemble of metamodels (EM) by suitable weight coefficients, and the EM incorporates the predictive advantages of different metamodels. The EM establishes the relationship between laser process parameters (laser power, scan speed, and defocusing amount) and three outputs (total energy consumption, surface roughness, and depth-width ratio of LST track). The effectiveness of the presented prediction approach is validated by the leave-one-out method and additional experiments. Furthermore, the main influences of process parameters on the three outputs are studied. According to the technique for order preference by similarity to an ideal solution (TOPSIS), the optimal process parameter is Group No. 2, with the relative closeness of 78.04%, while the worst one is Group No. 13, with the relative closeness of 2.21%. The presented prediction approach can serve as a reliable foundation in the energy-aware application of laser processing.

Life cycle oriented low carbon manufacturing of mechanical equipment: method and application

“…Low carbon processing, which refers to minimizing the use of resources and energy during processing [22,23] , has focused on both the machine level and the process level.…”