Optical sensor for real-time monitoring of CO_2 laser welding process

Abstract

An optical sensor for real-time monitoring of laser welding based on a spectroscopic study of the optical emission of plasma plumes has been developed. The welding plasma's electron temperature was contemporarily monitored for three of the chemical species that constitute the plasma plume by use of related emission lines. The evolution of electron temperature was recorded and analyzed during several welding procedures carried out under various operating conditions. A clear correlation between the mean value and the standard deviation of the plasma's electron temperature and the quality of the welded joint has been found. We used this information to find optimal welding parameters and for real-time detection of weld defects such as crater formation, lack of penetration, weld disruptions, and seam oxidation.

Optical Methods for On-line Quality Assurance of Welding Processes in Nuclear Steam Generators

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Characteristics of plasma plume in fiber laser welding of aluminum alloy

“…The T e reflects the heat input, while the N e is closely associated with the evaporation and the ionization of the materials. The T e can be deduced by relative intensities of two spectral lines of the same element under same ionization stage if the plasma is in local thermal equilibrium (LTE) [19][20][21][22]. The assumption of LTE is fulfilled when the N e is high enough so that 18 …”

Emission spectrometry evaluation in arc welding monitoring system

“…This method is better than the previous in a sense of implementing a real time monitoring system. The emission lines to be selected must satisfy the condition E m(1) − E m (2) > kT in higher energy levels [5].…”

“…The innovations generated by these studies are based on the physical phenomena involved in the arc welding processes, mainly those related to the plasma arc and its influence on the weld pool [1]. The applied techniques range from numerical simulation of the arc [2], image analysis [3], sound spectrum analysis and electromagnetic emission analysis [4][5][6] to the use of intelligent systems, based on neural networks and fuzzy logic [7].…”