{"id":577388,"date":"2024-01-11T08:00:53","date_gmt":"2024-01-11T13:00:53","guid":{"rendered":"https:\/\/www.therobotreport.com\/?p=577388"},"modified":"2024-01-10T16:45:35","modified_gmt":"2024-01-10T21:45:35","slug":"grit-vision-system-applies-ai-kane-robotics-cobot-weld-grinding","status":"publish","type":"post","link":"https:\/\/www.therobotreport.com\/grit-vision-system-applies-ai-kane-robotics-cobot-weld-grinding\/","title":{"rendered":"GRIT Vision System applies AI to Kane Robotics’ cobot weld grinding"},"content":{"rendered":"
\"A

A worker manipulates a Kane cobot to instruct it. Source: Kane Robotics<\/p><\/div>\n

Kane Robotics Inc. has combined artificial intelligence with visual sensors to enable its collaborative robot to automatically track and grind weld seams with high accuracy and speed. The company said its GRIT Vision System applies computer vision to the GRIT cobot for material-removal tasks such as sanding, grinding, and polishing in manufacturing.<\/p>\n

Launched in 2023, the GRIT<\/a> robot works alongside humans to perform labor-intensive finishing for any size and type of manufacturer, stated Kane Robotics in a release. Though initially designed for material removal in the aerospace industry, the robot can be configured for metalworking, woodworking, and other types of manufacturing, explained the Austin, Texas-based company<\/a>.<\/p>\n

Kane Robotics cited the case of Paul Mueller Co.<\/a>, which sought a more efficient way to grind welds on large steel tanks. The Springfield, Mo.-based stainless steel equipment manufacturer also wanted to reduce fatigue-related injuries and improve working conditions.<\/p>\n

The GRIT Vision System demonstrated its skill in live object detection and adaptive recalibration, allowing Paul Mueller to grind different-sized tank shells and various types of weld seams, said Kane Robotics.<\/p>\n

Engineer explains the GRIT Vision System<\/h2>\n

Dr. Arlo Caine<\/a>, a consulting engineer at Kane Robotics and a member of the company’s advisory board, helped design the GRIT Vision System. He is an expert in robot programming, mechanical product design, collaborative robotics, machine learning, and computer vision.<\/p>\n

Caine is also a professor in the Department of Mathematics and Statistics and associate chair and faculty fellow of the Center for Excellence in Math and Science Teaching at California State Polytechnic University \u2013 Pomona. He replied to the following questions from The Robot Report<\/em><\/a> about the GRIT Vision System.<\/p>\n

We’ve seen a lot of interest in the past few years around applying machine vision<\/a> and cobots<\/a> to welding and finishing. How is Kane Robotics’ approach different from others?<\/strong><\/p>\n

Caine:<\/strong> The GRIT Vision System includes a camera integrated with Kane’s cobot arm and proprietary AI software. The AI uses the camera\u2019s images to \u201cthink\u201d as it directs the cobot to follow the weld seam. When weld seams are imperfect, the AI\u2019s automatic steering tracks the uneven pattern and redirects the robotic arm accordingly.<\/p>\n

Kane engineers teach the AI to recognize a variety of welds prior to installation. Through software updates, the vision system learns to detect variations in the welds and improve grinding accuracy.<\/p>\n

This varies from other cobot welding systems because the Kane AI vision system \u201csees\u201d the path for the grinding tool to follow, even as the weld seam disappears. Most vision systems can see and react to objects, but those objects don\u2019t disappear, as does a weld seam.<\/p>\n

Kane’s vision system overcomes this problem by learning the various stages of each particular weld-grinding process so the cobot recognizes when to continue or stop grinding. The cobot world hasn\u2019t seen this before.<\/p>\n

\"Grind

Kane’s proprietary AI software reports on robotic welding operation. Click here to enlarge.<\/a> Source: Kane Robotics<\/p><\/div>\n

How does the GRIT robot<\/a> know when a job is completed?<\/strong><\/p>\n

Caine:<\/strong> The cobot does the dull and dirty work of holding the grinder, and the vision system handles the monotony of tracking large seams for long periods. But the human operator still selects how hard to push for the given abrasive, how fast to move along the seam, and how many passes to make to achieve the required finish. A human operator makes the final judgment about when the grinding job is \u201ccomplete.\u201d<\/p>\n

What constitutes “done” means different things to different customers and different applications. For Kane customer Paul Mueller Co., the applications were so numerous and varied that teaching GRIT fully was out of scope for the bid.<\/p>\n

Kane taught the system to do the basic work required and left the management to the human operator. This relates back to Kane\u2019s philosophy of simplicity and automating only what is most crucial to help humans do their jobs better.<\/p>\n

Kane cobot keeps humans in the loop<\/h2>\n

While many people think that automation is about robots replacing human workers entirely, why is keeping a human in the loop “indispensable” for these manufacturing<\/a> tasks? <\/strong><\/p>\n

Caine:<\/strong> Vision systems use machine learning to correct cobots\u2019 movements in near real time. But manufactured parts are rarely exact replicas of their perfect CAD models and precise cobot movements, so human judgment is still needed. Human operators ultimately determine how to best grind the welds.<\/p>\n

Human operators decide how to position the tool, what abrasive to use, and when to meet finish specifications. After the GRIT Vision System takes control of the cobot, a live custom interface allows the human operator to assess and adjust as needed.<\/p>\n

It’s important to highlight that our system is truly collaborative. The cobot does the tedious and monotonous work, and the skilled operator shapes the finish.<\/p>\n