| Author(s) |
Shems-Eddine Belhout, John Codevilla, Devon Goodspeed, William Bill Hamel, Bradley Howell Jared, Dylan Lewis, David Hansen, John Tatman, Greg Frederick |
| Abstract Scope |
There is a growing need for automation in the welding industry due to a growing shortage in skilled welders. TIG [Tungsten Inert Gas] welding, a method of welding that uses an electrode shielded by gas and is fed externally by a wire, is incredibly advantageous for its precise heat control and cleanliness. TIG welding is considered the standard for nuclear application which requires highly precise welds to be performed. Robotic welding can address this issue, and one major problem with many existing robots is the ability to precisely control movement, weld conditions, and sensing. Hence, the University of Tennessee Knoxville (UTK), partnered with the Electric Power Research Institute (EPRI), is developing TIG welding robot intended for adaptive welding.
We have developed a five degree-of-freedom robot. It can move in 3 translations; X along the track, Y perpendicular to the track, and Z up and down vertically which also incorporates automatic voltage control (AVC). We also have full control of the wire feed height and rotation to control its entry into the weld pool. In the future we will incorporate a pitch and roll axis for the robot to allow for more flexibility in groove geometries. Unlike commercially available welding robots, we can monitor the live position and velocity of all motors, allowing us to kinematically determine the orientation and position of the robot in space.
The robot also has three on-board sensors to monitor the weld process. By reading off the registers using Modbus communication on a Liburdi Power supply, we can measure the arc voltage and current as it is pulsing. Anything that the power supply uses to define settings and monitor to execute a weld can be monitored as well. A Keyence Laser Profilometer also actively scans the groove to reveal a 3D topology of the solidified weld. Two Cavitar cameras, which provide a high-resolution video feed of the weld, monitor the solidification boundaries via the trailing edge of the weld pool, as well as the leading edge to monitor the pool itself. These cameras also have the advantage of removing the glare of the arc during welding.
Results: Through this robot, we have demonstrated an ability to monitor the weld pool and wire feed location entering the weld pool using image-based processing. By tracking the location of the wire feed and the weld pool we can monitor crucial behaviors of the weld to maintain an optimal weld plan. Using the cameras, we can also detect defects.
Common defects that plague TIG are lack of fusion and tie-in. Traditionally, if a defect has occurred , the weld would be aborted, the solidified weld would then be ground down, and the weld would be repeated. Because of this, the faster a defect is detected, the quicker it can be corrected. By observing the solidification trailing side of the robot using cameras, we can monitor the reflections and chevrons in the solidified material and track the edges of the solidification over time. Using an image processing algorithm can identify discrepancies by measuring the location of the tungsten tip, the solidified melt pool edges and the groove geometry. The monitoring of these features can inform the operator if a particular defect is present. On-going work is refining detection algorithms, and quantifying their performance.
Work has been completed to design, build, integrate and demonstrate a five-axis TIG welding system targeted for nuclear piping. System design and operation will be discussed, with specific progress shown highlighting capabilities for in-situ defect detection. Future efforts for this robot are to develop adaptive welding capabilities, whereby a predictive multi-bead path plan can be programmed to optimize torch position and orientation for groove geometry and process deviations. |