Facing a shortage of workers, manufacturers are forced to raise wages and automate with robotics. Robotic welding is an investment in safety, as well as productivity and quality. Fuselage assembly and paint spray processes become automated.
AI improves welding performance
However, fully manual welding simply isn’t an option for higher-turnover jobs: Bidding competitively for new jobs often increasingly means using some level of automation. What level, exactly? That all depends, but what’s certain is that preprogramming weld parameters never covers all the variables at play.
More than mere quality assurance, it is welding’s fundamental unpredictability that Novarc Technologies wants to help fabricators master with the NovEye vision-based, artificial intelligence (AI)-guided control system on its latest Spool Welding Robot (SWR). And while the orbital welding that the SWR does is nothing new, the AI and machine learning Novarc is using to drive the unit is.
“Welding science and engineering … includes many disciplines—from physics, plasma, electronics, electrical engineering, and material science,” Asadi said. “All that comes together in a small arc in the weld tool that you are seeing in the tip of the electrode. There are many sciences going on in that small portion. When you formulate something and you understand the physics behind it and all the governing equations, regardless of how complicated the process is, they are more or less following those governing equations.
Robot welding: Load programs and swap between parts in just 15 mins 🦾 pic.twitter.com/v5hESJ2ATf— Universal Robots (@Universal_Robot) May 19, 2022
Hybrid machine learning-enabled adaptive welding speed control
This research presents a preliminary study on developing appropriate Machine Learning (ML) techniques for real-time welding quality prediction and adaptive welding speed adjustment for GTAW welding at a constant current. In order to collect the data needed to train the hybrid ML models, two cameras are applied to monitor the welding process, with one camera (available in practical robotic welding) recording the top-side weld pool dynamics and a second camera (unavailable in practical robotic welding, but applicable for training purpose) recording the back-side bead formation. Given these two data sets, correlations can be discovered through a convolutional neural network (CNN) that is good at image characterization. With the CNN, top-side weld pool images can be analyzed to predict the back-side bead width during active welding control.
Sparks fly as BAE Systems brings innovation to welding
Funded by the U.S. Government, BAE Systems engineers collaborated with the U.S. Army Research Laboratory and Wolf Robotics to develop an Agile Manufacturing Robotic Welding Cell customized for aluminum structures that comprise the combat vehicle’s hull.
Prior to welding automation, large aluminum pieces that form the hull were hand-welded together, requiring numerous weld passes at each seam to build the hull. Hand welding requires the welder to hold the weld gun with both hands, pull the trigger to feed wire into the weld joint that creates an arc. The gun is then moved over the metal slowly to create a weld. The number of weld starts and stops in a single seam is based on the length and reach of the welder’s arms. The further a welder can reach, the less he or she needs to stop and start again.
Making Welding Accessible to All
With the ongoing shortage of skilled workers and the pickup in the economy, suppliers of welding equipment are finding ways to making welding easier for those working in manufacturing. Automation is the leading technique among many.
“Manual welding is an area with a very high degree of repetitive motion injury, resulting in turnover and associated costs,” he said. “OSHA puts out a statistic that says any investment in safety yields a six-to-one payback. So, robotic welding is an investment in safety, as well as productivity and quality. Take all these factors into account and you get a pretty big payback number.”
John Deere and Audi Apply Intel’s AI Technology
Identifying defects in welds is a common quality control process in manufacturing. To make these inspections more accurate, John Deere is applying computer vision, coupled with Intel’s AI technology, to automatically spot common defects in the automated welding process used in its manufacturing facilities.
At Audi, automated welding applications range from spot welding to riveting. The widespread automation in Audi factories is part of the company’s goal of creating Industrie 4.0-level smart factories. A key aspect of this goal involves Audi’s recognition that creating customized hardware and software to handle individual use cases is not preferrable. Instead, the company focuses on developing scalable and flexible platforms that allow them to more broadly apply advanced digital capabilities such as data analytics, machine learning, and edge computing.
Tractor Maker John Deere Using AI on Assembly Lines to Discover and Fix Hidden Defective Welds
John Deere performs gas metal arc welding at 52 factories where its machines are built around the world, and it has proven difficult to find defects in automated welds using manual inspections, according to the company.
That’s where the successful pilot program between Intel and John Deere has been making a difference, using AI and computer vision from Intel to “see” welding issues and get things back on track to keep John Deere’s pilot assembly line humming along.