# Spectroscopic Approach for the On-Line Monitoring of Welding of Tanker Trucks
## 1 Introduction  
Although welding processes play a major role in several different industrial settings, from aeronautics to the automotive industries and in nuclear and energy sectors, the assurance of welding quality is still a problem to be solved in many cases where quality standards are especially demanding. In such settings, the appearance of defects due to the inherent complexity of the physics involved or subtle variations in the various input parameters or even in the ambient conditions (temperature, humidity) may affect the whole process. Traditionally, non-destructive techniques, such as ultrasound, magnetic particles or X-rays, have been employed to analyse the quality of welding seams, but it is obvious that the use of such techniques implies serious costs in terms of time expenditures and effects on productivity. It should be noted that these techniques are employed off-line, once the seams have been performed and, consequently, this limits the possible corrective actions that can be taken. Within this context, a reliable on-line welding monitoring system able to provide information in real-time would be extremely valuable: such a system would not only allow the acquisition of information about the relevant process in situ, it could also be used to design strategies to reduce the appearance of defects or at least enable on-line repair or the discarding of defective seams. In recent years, different techniques have been explored to find reliable on-line welding monitoring systems for specific welding processes, such as arc, laser, plasma and even electron-beam welding. Machine vision solutions have been proposed [1], for example, with illumination via lasers and subsequent filtering [2] or by applying fractal theory andthe wavelet transform method to images acquired in friction stir welding [3], as well as infrared thermography for surface temperature monitoring in laser welding [4] and the estimation of heat inputs [5] or molten pool temperatures in arc welding processes [6]. Acoustic-based monitoring [7] or even monitoring via FBGs (fiber Bragg grating sensors) glued to welding plates or in a non-contact configuration [8] have also been studied. Different sensor fusion approaches can also be found in the literature, for example, using IR, UV and acoustic sensors [9] or by analysing spectral, acoustic and electric signals in the process [10]. Plasma optical spectroscopy has also attracted attention in this field, as this technique allows powerful study of a process via analysis of the different atomic species participating in the plasma. It has been demonstrated that the processing of some spectral information, mainly provided by certain specific emission lines, is able to generate spectroscopic monitoring parameters that exhibit significant correlations with quality-related events in the seams [11,12]. In particular, argon emission lines have been typically employed in the literature, as they are commonly found in spectra associated with those processes that require a shielding/protection gas. In terms of studying the performance of different species in monitoring, [PERSON] et al. [11] studied plasma electronic temperature (Te) results calculated for species such as Mn I, Cr I and Fe I, although no clear differences were observed between them. [PERSON] et al. [12] used the covariance mapping technique (CMT) to obtain a covariance map to discriminate between optimal and defective conditions using species such as Al II, Mg II and O II. Studies have also been performed using algorithms to automatically select the most discriminant spectral bands in terms of defect detection [13], and LIBS (laser-induced breakdown spectroscopy) has been suggested as a suitable technique for on- and off-line monitoring of welding [14,15] and additive manufacturing [16]. Recently, the analysis of hydrogen lines in the welding of aluminium alloys has been proposed in an attempt to establish a correlation with the appearance of porosities [17].