Observing the weld pool and measuring its geometrical parameters are key issues for developing the next generation intelligent welding machine and modeling the complex welding process. In the past few years, different techniques have been applied, but the dynamic specular weld pool surface and the strong weld arc complicate these approaches and make observation difficult. To resolve the problem, a new three-dimensional sensing system using structured light is proposed for a gas tungsten arc welding (GTAW) process. In the system, a dot-matrix laser pattern is projected on the specular weld pool surface, which can reflect light onto an imaging plane. The reflected images are captured by a high-speed camera and can successfully be processed by image processing algorithms developed. With the acquired information, a three-dimensional reconstruction scheme is proposed and discussed in this paper. A surface reconstruction method with several slope-based algorithms is first developed to rebuild the region of weld pool surface which reflects the laser pattern. Then a two-dimensional piecewise model is provided to calculate weld pool boundary by utilizing the edge condition. Finally the optimal estimate of the three-dimensional weld pool surface is synthesized. The acceptable accuracy of the results verified the effectiveness of the reconstruction scheme. List of notation and definitionsBase point (points). The reflection point (points), whose position(s) is (are) assumed and used to calculate the positions of other reflection points as a base, is (are) called base point(s).Column plane. For a point p i,j located in the ith row and the jth column of the projected dot matrix, the plane passing through all the dots in the jth column and the dot of laser diode is called the column plane of point p i,j .Center reference point. The center point in the projected dot matrix of structured light. For a 19-by-19 dot matrix, it is located in the 10th row and the 10th column. It is not an actual visible point but a reference position, which can be used to find the corresponding relationship between projected and reflected points.GTAW. Gas tungsten arc welding Normal. A normal to a flat surface is a three-dimensional vector which is perpendicular to that surface, and a normal to a non-flat surface at a point P on the surface is a vector which is perpendicular to the tangent plane to that surface at P.Reflected dots (points). The dots reflected on the imaging plane.Reflected image. The captured image including the reflected dots on the imaging plane.Reflection dots (points). The dots projected on the weld pool surface, also called projected dots.Row plane. For a point p i,j located in the ith row and the jth column of the projected dot matrix, the plane passing through all the dots in the ith row and the dot of laser diode is called the row plane of point p i,j .Tangent plane. P is a point on the surface S. If the tangent lines at P to all smooth curves on the surface S passing through P lie on a common plane, then that plane is called ...
Double-electrode gas metal arc welding (DE-GMAW) is a novel welding process in which a second electrode, non-consumable or consumable, is added to bypass part of the wire current. The bypass current reduces the heat input in non-consumable DE-GMAW or increases the deposition speed in consumable DE-GMAW. The fixed correlation of the heat input with the deposition in conventional GMAW and its variants is thus changed and becomes controllable. At the University of Kentucky, DE-GMAW has been tested/developed by adding a plasma arc welding torch, a GTAW (gas tungsten arc welding) torch, a pair of GTAW torches, and a GMAW torch. Steels and aluminum alloys are welded and the system is powered by one or multiple power supplies with appropriate control methods. The metal transfer has been studied at the University of Kentucky and Shandong University resulting in the desirable spray transfer be obtained with less than 100 A base current for 1.2 mm diameter steel wire. At Lanzhou University of Technology, pulsed DE-GMAW has been successfully developed to weld aluminum sheets. At the Adaptive Intelligent Systems LLC, DE-GMAW principle has been applied to the submerged arc welding (SAW) and the embedded control systems needed for industrial applications have been developed. The DE-SAW resulted in 1/3 reduction in heat input for a shipbuilding application and the weld penetration depth was successfully feedback controlled. In addition, the bypass concept is extended to the GTAW resulting in the arcingwire GTAW which adds a second arc established between the tungsten and filler to the existing gas tungsten arc. The DE-GMAW is extended to double-electrode arc welding (DE-AW) where the main electrode may not necessarily to be consumable. Recently, the Beijing University of Technology systematically studied the metal transfer in the arcingwire GTAW and found that the desired metal transfer modes may always be obtained from the given wire feed speed by adjusting the wire current and wire position/orientation appropriately. A variety of DE-AW processes are thus available to suit for different applications, using existing arc welding equipment.
Controlled metal transfer in gas metal arc welding (GMAW) implies controllable weld quality. To understand, analyse and control the metal transfer process, the droplet should be monitored and tracked. To process the metal transfer images in double-electrode GMAW (DE-GMAW), a novel modification of GMAW, a brightness-based algorithm is proposed to locate the droplet and compute the droplet size automatically. Although this algorithm can locate the droplet with adequate accuracy, its accuracy in droplet size computation needs improvements. To this end, the correlation among adjacent images due to the droplet development is taken advantage of to improve the algorithm. Experimental results verified that the improved algorithm can automatically locate the droplets and compute the droplet size with an adequate accuracy.
Welding is a major manufacturing process that joins two or more pieces of materials together through heating/mixing them followed by cooling/solidification. The goal of welding manufacturing is to join materials together to meet service requirements at lowest costs. Advanced welding manufacturing is to use scientific methods to realize this goal. This paper views advanced welding manufacturing as a three step approach: (1) pre-design that selects process and joint design based on available processes (properties, capabilities, and costs); (2) design that uses models to predict the result from a given set of welding parameters and minimizes a cost function for optimizing the welding parameters; (3) real-time sensing and control that overcome the deviations of welding conditions from their nominal ones used in optimizing the welding parameters by adjusting the welding parameters based on such real-time sensing and feedback control. The paper analyzes how these three steps depend on process properties/capabilities, process innovations, predictive models, numerical models for fluid dynamics, numerical models for structures, real-time sensing, and dynamic control. The paper also identifies the challenges in obtaining ideal solutions and reviews/analyzes the existing efforts toward better solutions. Special attention and analysis have been given to (1) gas tungsten arc welding (GTAW) and gas metal arc welding (GMAW) as benchmark processes for penetration and materials filling; (2) keyhole plasma arc welding (PAW), keyhole-tungsten inert gas (K-TIG), and keyhole laser welding as improved/capable penetrative processes; (3) friction stir welding (FSW) ......
It has been well recognized that the weld pool geometry plays a critical role in the welding process. In this study, a diode laser welding control system is established. The authors performed a series of open-loop experiments to investigate the interaction and the correlation among of the laser energy, the welding speed, and the weld pool geometry. A digital camera with a high speed shutter is equipped to take pictures of the weld pool in real time. Custom computer vision software and image processing programs are applied to acquire the surface width of the weld pool. Based on the experimental study, the authors propose an applicable method to identify a SISO nonlinear continuous model for the established diode laser welding process. The identified nonlinear model takes the reciprocal of the welding speed as the input and the surface width of the weld pool as the output. To validate the model, the authors conduct further experiments including step and PRTS (Pseudo-random Ternary Signal) responses. In addition, the authors simulate the nonlinear model with same inputs and check the data agreements between the simulated results and the experimental data. The validation results confirm the applicability of the proposed nonlinear identification method and show that the model can successfully predict the surface width of weld pool for later use.
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