Optical system for on line monitoring of welding: a machine learning approach for optimal set up

Abstract

In this paper a methodology is described for continuous checking of the settings of a low cost vision system for automatic geometrical measurement of welding embedded on components of complicated shape. The measurement system is based on a laser sheet. Measuring conditions and the corresponding uncertainty are analyzed by evaluating their p-value and its closeness to an optimal measurement configuration also when working conditions are changed. The method aims to check the holding of optimal measuring conditions by using a machine learning approach for the vision system: based on a such methodology single images can be used to check the settings, therefore allowing a continuous and on line monitoring of the optical measuring system capabilities.

According to this procedure, the optical measuring system is able to reach and to hold uncertainty levels adequate for automatic dimensional checking of welding and of defects, taking into account the effects of system hardware/software incorrect settings and environmental effects, like varying lighting conditions. The paper also studies the effects of process variability on the method for quantitative evaluation, in order to propose on line solutions for this system.