Laser derusting on low carbon steel surface based on coordinated application of machine vision and laser-induced breakdown spectroscopy

Abstract

Online monitoring and closed-loop control are essential to accurately remove the rust layer and effectively avoid damage to the substrate. A collaborative utilization of machine vision and laser-induced breakdown spectroscopy (LIBS) to monitor and control the laser derusting process on Q235B steel is reported. The optimum overlap ratio of 50% is obtained by using machine vision. Monitoring derusting with different thicknesses relies on the Pearson correlation coefficient of the LIBS spectrum between the rust layer and substrate. By developing a collaborative monitoring and control system on LabVIEW, the functions of date acquisition, coordinate transformation, and data calculation are realized to automatically control the laser derusting process on rusty steel in a large area. The cooperation of two methods can achieve high-quality laser derusting with a derusting degree of 99.1%, roughness of 1.45 µm, and extremely low oxygen content on the surface, which verifies the accuracy and practicability of the developed monitoring system. Moreover, the potentiodynamic polarization curves demonstrate that the performance of the corrosion resistance of the Q235B steel is effectively improved after laser derusting.