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.

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 of the 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.

Laser beam welding offers high productivity and relatively low heat input and is one key enabler for efficient manufacturing of sandwich constructions. However, the process is sensitive to how the laser beam is positioned with regards to the joint, and even a small deviation of the laser beam from the correct joint position (beam offset) can cause severe defects in the produced part. With tee joints, the joint is not visible from top side, therefore traditional seam tracking methods are not applicable since they rely on visual information of the joint. Hence, there is a need for a monitoring system that can give early detection of beam offsets and stop the process to avoid defects and reduce scrap. In this paper, a monitoring system using a spectrometer is suggested and the aim is to find correlations between the spectral emissions from the process and beam offsets. The spectrometer produces high dimensional data and it is not obvious how this is related to the beam offsets. A machine learning approach is therefore suggested to find these correlations. A multi-layer perceptron neural network (MLPNN), support vector machine (SVM), learning vector quantization (LVQ), logistic regression (LR), decision tree (DT) and random forest (RF) were evaluated as classifiers. Feature selection by using random forest and non-dominated sorting genetic algorithm II (NSGAII) was applied before feeding the data to the classifiers and the obtained results of the classifiers are compared subsequently. After testing different offsets, an accuracy of 94% was achieved for real-time detection of the laser beam deviations greater than 0.9 mm from the joint center-line.

This study presents a vision-based closed-loop tracking system designed specifically for robotic laser beam welding of curved and closed square butt joints. The proposed system is compared against 11 existing solutions reported in the literature, which employ various sensor principles for the same application. The system employs a non-contact, non-intrusive machine vision approach, seamlessly integrated into the laser beam welding head to mitigate challenges associated with sensor forerun. Key features include an off-axis LED illumination, an optical filter, and a movable actuator, facilitating real-time image processing and closed-loop control during the welding process. Experimental validation was conducted on stainless-steel plates with complex closed square butt joints. The system achieved a mean absolute joint-to-beam offset of 0.14 mm across four test cases, with a maximum offset of 0.85 mm, demonstrating its robustness and precision. Comparative analysis underscores the proposed method’s advantages, showcasing its potential for industrial applications in laser beam welding of geometrically challenging joints.

Directed energy deposition using laser beam and wire feed is a critical technology in high-productivity metal additive manufacturing, enabling precise deposition and fabrication of components with high quality and deposition rates. The interaction between the laser beam and metal is highly temperature-sensitive, affecting metal transfer, melting, solidification, and phase transformations. Effective control of heat input is crucial for influencing the geometry, microstructure, and overall integrity of the fabricated parts. This study investigates the correlation between the melt pool top surface boundary contour and the immediate solidification temperature, utilizing machine vision and radiation pyrometer measurements. The research highlights the significance of melt pool length as an indicator of temperature variations during deposition. System identification techniques were employed to develop a predictive model of the dynamic relationship between melt pool length and temperature, aimed at enhancing process control strategies. Our approach substitutes off-axis pyrometry with a cost-effective coaxial vision camera integrated into the laser head. By incorporating a vision camera without active illumination, the study offers a compact, omnidirectional solution. Comparative analysis of melt pool length and area measurements demonstrates the superiority of length-based correlations with temperature data. This work addresses the challenges of heat-input control in directed energy deposition laser beam wire processes and suggests future research directions in various metal alloys, complex geometries, and advanced sensing and automatic control methodologies.