Automatic wavelength spectrometer calibration during arc-welding process

Spectroscopic analysis techniques are widely used in a variety of scientific areas. The availability of low-cost CCD spectrometers has also allowed their development in several industrial applications, like on-line analysis of arc and laser welding processes. A correct spectrometer wavelength calibration is always required, specially when changes in ambient temperature are to be found, or when the optical fiber attached to the spectrometer is replaced. This calibration procedure commonly involves the recalculation of a pixelwavelength polynomial by means of regression techniques after having defined a new experimental setup. Besides, specific calibration lamps are needed to use some known emission lines in the regression stage. In this paper, a technique which allows a real-time, in-process automatic wavelength calibration of CCD spectrometers in arc-welding processes is presented. The key point in the automatic calibration process is the real-time identification of some particular emission lines emitted from the plasma generated during the welding process. TIG welding tests performed on stainless steel plates will show the feasibility of the proposed technique. As well as for laser welding, the automatic wavelength calibration procedure could be easily extended to some other spectroscopic techniques

Hyperspectral imaging sustains production-process competitiveness

A newly developed imaging system aids companies in the agri-food and industrial sectors to achieve high-speed online inspection and enhanced quality control

Hyperspectral imaging for diagnosis and quality control in agri-food and industrial sectors

Optical spectroscopy has been utilized in various fields of science, industry and medicine, since each substance is discernible from all others by its spectral properties. However, optical spectroscopy traditionally generates information on the bulk properties of the whole sample, and mainly in the agri-food industry some product properties result from the heterogeneity in its composition. This monitoring is considerably more challenging and can be successfully achieved by the so-called hyperspectral imaging technology, which allows the simultaneous determination of the optical spectrum and the spatial location of an object in a surface. In addition, it is a nonintrusive and non-contact technique which gives rise to a great potential for industrial applications and it does not require any particular preparation of the samples, which is a primary concern in food monitoring. This work illustrates an overview of approaches based on this technology to address different problems in agri-food and industrial sectors. The hyperspectral system was originally designed and tested for raw material on-line discrimination, which is a key factor in the input stages of many industrial sectors. The combination of the acquisition of the spectral information across transversal lines while materials are being transported on a conveyor belt, and appropriate image analyses have been successfully validated in the tobacco industry. Lastly, the use of imaging spectroscopy applied to online welding quality monitoring is discussed and compared with traditional spectroscopic approaches in this regard

Use of the plasma RMS signal for on-line welding quality monitoring

In this paper a new spectroscopic monitoring parameter is proposed for the on-line monitoring of welding processes, the plasma RMS signal, which is determined by considering the contribution from the spectral samples over a particular spectral window. This parameter is directly related to the heat input that can be estimated by measuring both welding voltage and current, but it exhibits a higher sensitivity to the appearance of weld defects. A comparison between the results obtained from the different spectroscopic parameters will be presented, with data from both experimental and field arc-welding tests

Welding diagnostics by means of particle swarm optimization and feature selection

In a previous contribution, a welding diagnostics approach based on plasma optical spectroscopy was presented. It consisted of the employment of optimization algorithms and synthetic spectra to obtain the participation profiles of the species participating in the plasma. A modification of the model is discussed here: on the one hand the controlled random search algorithm has been substituted by a particle swarm optimization implementation. On the other hand a feature selection stage has been included to determine those spectral windows where the optimization process will take place. Both experimental and field tests will be shown to illustrate the performance of the solution that improves the results of the previous work.This work has been supported by the TEC2010-20224-C02-02 and OPENAER CENIT 2007–2010 projects

Welding diagnostics based on feature selection and optimization algorithms

In a previous paper a new approach was explored where the output parameters of a welding monitoring system based on plasma spectroscopy were the participation profiles of plasma ions and neutral atoms. They were obtained by the generation of synthetic spectra and the use of an optimization algorithm, showing correlation to the appearance of defects on the seams. In this work a feature selection algorithm is included in the model to determine the most discriminant wavelengths in terms of defect detection, thus allowing to reduce the spectral range where the synthetic spectra are generated. This should also give rise to an improvement in the overall computational performance of the algorithm. Alternatives to the use of controlled randomn search algorithms will be also explored, and the resulting model will be checked by means of experimental and field tests of arc-welding processes

Arc welding quality monitoring by means of near infrared imaging spectroscopy

The search for an efficient on-line monitoring system focused on the real-time analysis of the welding quality is an active area of research, mainly due to the widespread use of both arc and laser welding processes in relevant industrial scenarios such as aeronautics or nuclear. In this work, an improvement in the performance of a previously designed monitor system is presented. This improvement is accomplished by the employment of a dual spatial-spectral technique, namely imaging spectroscopy. This technique allows the simultaneous determination of the optical spectrum components and the spatial location of an object in a surface. In this way, the spatially characterization of the plasma emitted during a tungsten inert gas (TIG) welding is performed. The main advantage of this technique is that the spectra of all the points in the line of vision are measured at the same time. Not only are all the spectra captured simultaneously, but they are also processed as a batch, allowing the investigation of the welding quality. Moreover, imaging spectroscopy provides the desired real-time operation. To simultaneously acquire the information of both domains, spectral and spatial, a passive Prism-Grating-Prism (PGP) device can be used. In this paper the plasma spectra is captured during the welding test by means of a near infrared imaging spectroscopic system which consists of input optics, an imaging spectrograph and a monochrome camera. Technique features regarding on-line welding quality monitoring are discussed by means of several experimental welding tests

Estimation of the plasma spectrum RMS signal as an alternative spectroscopic approach for arc-welding quality monitoring

Plasma spectroscopy has demonstrated its potential within the framework of welding process quality monitoring. The analysis of the welding plasma spectrum, which is formed by several emission lines from the different elements participating in the process, gives rise to spectroscopic parameters exhibiting a direct correlation to the quality of the resulting seams. The plasma electronic temperature has been the traditional selection in this regard, mainly by using an approximation where only two emission lines from the same species are involved in the calculations. However, for a completely automated system, the computational cost involved in the process could be a serious drawback. In this paper we propose the use of the plasma spectrum RMS (Root Mean Square) signal as an alternative spectroscopic approach, as it will be demonstrated that this parameter can be also used to identify the appearance of weld defects in an on-line quality monitoring system

Arc-welding quality assurance by means of embedded fiber sensor and spectral processing combining feature selection and neural networks

A new spectral processing technique designed for its application in the on-line detection and classification of arc-welding defects is presented in this paper. A non-invasive fiber sensor embedded within a TIG torch collects the plasma radiation originated during the welding process. The spectral information is then processed by means of two consecutive stages. A compression algorithm is first applied to the data allowing real-time analysis. The selected spectral bands are then used to feed a classification algorithm, which will be demonstrated to provide an efficient weld defect detection and classification. The results obtained with the proposed technique are compared to a similar processing scheme presented in a previous paper, giving rise to an improvement in the performance of the monitoring system