Robotic Welding Research Papers - Academia.edu (original) (raw)

Arc welding is an important technology for joining metals and repairing metal products in manufacturing industries. It is usually performed by a skilled worker. Due to the shortage of skilled workers and due to the hazardous and unpleasant working conditions, the automation of arc welding is gaining importance nowadays. Existing welding robots require that the dimensions, position and orientation to be programmed
prior to the process. These robots suffer from an evident lack of flexibility. This paper presents an automatic welding system assisted by a machine vision system to compute the dimension, position and orientation of the workpieces. By analysing the image taken by the camera, a simple algorithm is developed to detect the dimension, position and orientation of the workpieces. The calculated data is then fed to a controller which controls the welding electrode ovement. Use of machine vision system has eliminated the need to pre feed the workpiece data to the robot and hence the system is made flexible.

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- Image Processing, Machine Vision, Robotic Welding, Gas Tungsten Arc Welding

Recent advances in automation and sensor technology have enabled the use of industrial robots for complex tasks that require intelligent decision making. Vision sensors have been the most successfully used sensor in many high value industrial applications. Over the recent years, weld seam
tracking has been a topic of interest, as most of the existing
robotic welding systems operate on basis of pre-programmed
instructions. Such automated systems are incapable of adapting to unexpected variations in the seam trajectory or part fit-up. Applications such as tungsten inert gas (TIG) welding of aerospace components require high tolerances and needs intelligent decision making. Such decision making procedure has to be based on the weld groove geometry at any instance. In this study, a novel algorithm along with an automated system was developed for estimating the joint profile and path tracking of a three dimensional (3D) weld groove. A real-time position based closed-loop system was developed with a six axis industrial robot and a laser triangulation based sensor. The system was capable of
finding the 3D weld joint profile and position in real-time, and
make intelligent decisions accordingly. Raw data from a vision
sensor was processed through a novel algorithm to obtain X and Z co-ordinates at an accuracy of 8.3μm and 43μm respectively at an acquisition speed of 2.5 profiles per second. The algorithm was also capable of measuring the weld gaps with an accuracy of 28μm. Finally, the developed system was successfully used for three dimensional seam tracking, and demonstrates an accuracy of ±0.5mm at a tracking a speed of 2mm/s.

Generally the quality of a weld joint is strongly influenced by parameters during the welding
process. The welding process variables of welding current, arc voltage, welding speed, heat input
and wire feed speed which influence weld bead shape, are coupled with each other but not
directly connected with weld bead shape individually. Therefore, it is very difficult and time
consuming to determine the welding process variables necessary to obtain the desired weld bead
shape. Mathematical modeling in conjunction with many experiments must be used to predict the
magnitude of weld bead shape. Even though experimental results are reliable, prediction is
difficult because of the coupling characteristics. In this study, mathematical analysis and backpropagation
neural networks are used to associate the robotic arc welding process variables with
the features of the bead geometry and penetration on SAF 2205 fillet joint shape. The results
show that not only the proposed models can predict reinforcement with reasonable accuracy and
guarantee the uniform weld quality, but also neural network model could be better than empirical
models.

- by Carolina Payares-Asprino and +1
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- Artificial Neural Networks, Robotic Welding

In this paper, the relation between B-splines and FIR (Finite Impulse Response) filters is demonstrated and exploited to design a digital filter for trajectory planning, combining the very simple structure and computational efficiency of FIR filters with the flexibility of splines. In particular, the trajectory generator consists of two main elements. The former is devoted to the solution of an optimization problem that, given a set of points to be interpolated (or approximated), provides the control points defining the spline. The latter, a cascade of moving average filters, gives the trajectory profile at each sampling time on the basis of such points. The proposed method has been applied to several robotic and industrial applications, and in this paper two case studies are reported as examples:an industrial robot performing a welding operation and a mobile robot moving in an environment with obstacles. With respect to these tasks, the main features of the trajectory generator are shown: the possibility of planning trajectories with high degree of smoothness (continuity of the derivatives), the possibility of easily changing the duration of the trajectory (and therefore the velocity, acceleration, jerk, etc. of the trajectory) maintaining the same geometric path, the possibility of modifying locally the pre-planned path.

- by Luigi Biagiotti
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- Path planning, Optimization Problem, Trajectory, Mobile Robots