This paper addresses the issue of evaluating and selecting optical sensors to be integrated with a 1μm laser beam welding system. The method used for this evaluation is mainly a survey of relevant research litterature. The result of this work is a matrix showing the different methods and distinct features related to relevant process conditions that can be estimated or detected with respective methods. This evaluation also includes considerations on the required sensor bandwidth in relation to inertia and time constants in the physical process.

Robotized laser beam welding of closed-square-butt joints is sensitive to the positioning of the laser beam with respect to the joint since even a small offset may result in a detrimental lack of sidewall fusion. An evaluation of a system using a photodiode aligned coaxial to the processing laser beam confirms the ability to detect variations of the process conditions, such as when there is an evolution of an offset between the laser beam and the joint. Welding with different robot trajectories and with the processing laser operating in both continuous and pulsed mode provided data for this evaluation. The detection method uses wavelet analysis of the photodetector signal that carries information of the process condition revealed by the plasma plume optical emissions during welding. This experimental data have been evaluated offline. The results show the potential of this detection method that is clearly beneficial for the development of a system for welding joint tracking.

The automated laser beam butt welding process is sensitive to positioning the laser beam with respect to the joint because a small offset may result in detrimental lack of sidewall fusion. This problem is even more pronounced incase of narrow gap butt welding, where most of the commercial automatic joint tracing system fail to detect the exact position and size of the gap. In this work, adual vision and spectroscopic sensing approach is proposed to trace narrow gap butt joints during laser welding. The system consists of a camera with suitable illumination and matched optical filters and a fast miniature spectrometer. An image processing algorithm of the camera recordings has been developed in order to estimate the laser spot position relative the joint position. The spectral emissions from the laser induced plasma plume has been acquired by the spectrometer, and based on the measurements of the intensities of selected lines of the spectrum, the electron temperature signal has been calculated and correlated to variations of process conditions. The individual performances of these two systems have been experimentally investigated and evaluated offline by data from several welding experiments where artificial abrupt as well as gradual excursions of the laser beam out of the joint were produced. Results indicate thata combination of the information provided by the vision and spectroscopic systems is beneficial for development of a hybrid sensing system for joint tracing.

Robotized laser beam welding of closed-square-butt joints is sensitive to how the focused laser beam is positioned in relation to the joint, and existing joint tracking systems tend to fail in detecting the joint when the gap and misalignment between the work pieces are close to zero. A camera-based system is presented based on a high dynamic range camera operating with LED illumination at a specific wavelength and a matching optical filter. An image processing algorithm based on the Hough transform extracts the joint position from the camera images, and the joint position is then estimated using a Kalman filter. The filter handles situations, when the joint is not detectable in the image, e.g., when tack welds cover the joint. Surface scratches, which can be misinterpreted as being the joint, are handled by a joint curve prediction model based on known information about the nominal path defined by the robot program. The performance of the proposed system has been evaluated off line with image data obtained during several welding experiments.

A vision and spectroscopic system for estimation of the joint gap width in autogenous laser beam butt welding is presented. Variations in joint gap width can introduce imperfections in the butt joint seam, which in turn influence fatigue life and structural integrity. The aim of the monitoring approach explored here is to acquire sufficiently robust process data to be used to guide post inspection activities and/or to enable feedback control for a decreased process variability. The dual-sensing approach includes a calibrated CMOS camera and a miniature spectrometer integrated with a laser beam tool. The camera system includes LED illumination and matching optical filters and captures images of the area in front of the melt pool in order to estimate the joint gap width from the information in the image. The intensity of different spectral lines acquired by the spectrometer has been investigated and the correlation between the intensity of representative lines and the joint gap width has been studied. Welding experiments have been conducted using a 6 kW fiber laser. Results from both systems are promising, the camera system is able to give good estimations of the joint gap width, and good correlations between the signal from the spectrometer and the joint gap width have been found. However, developments of the camera setup and vision algorithm can further improve the joint gap estimations and more experimental work is needed in order to evaluate the robustness of the systems.

Adding filler wire control to autogenous laser beam welding of squared butt joints offers a means to widen up the tight fit-up tolerances associated with this process. When the gap width varies, the filler wire rate should be controlled to assure a constant geometry of the resulting weld seam. A dual mode sensing system is proposed to estimate the joint gap width and thereby control the filler wire rate. A vision camera integrated into the welding tool together with external LED illumination and a laser line projection enables two sensing modes, one surface feature extraction mode and one laser triangulation-based mode. Data from the both modes are fused in a Kalman filter, and comparisons show that the fusing of the data gives more robust estimation than estimates from each single mode. A feed-forward control system adaptively adjusts the filler wire rate based on the estimations ofthe joint gap width in front of the keyhole. The focus is on keeping the data processing simple and affordable, and the real-time performance of the sensor and control system has been evaluated by welding experiments. It is shown that the proposed system can be used for on-line control of the filler wire rate to achieve a constant weld geometry during varying joint gap widths