Robot Welding

Assembly Line

SWR-TIPTIG Cobot for Gas Tungsten Arc Welding

Two-Platen System for Die-Casting Production

AF-FTTSnet: An end-to-end two-stream convolutional neural network for online quality monitoring of robotic welding

📅 Date:

✍️ Authors: Yuxiang Hong, Xingxing He, Jing Xu

🔖 Topics: Robot Welding, Quality Assurance, Machine Vision

🏢 Organizations: China Jiliang University

Online welding quality monitoring (WQM) is crucial for intelligent welding, and deep learning approaches considering spatiotemporal features for WQM tasks show great potential. However, one of the important challenges for existing approaches is to balance the spatiotemporal representation learning capability and computational efficiency, which makes it challenging to adapt welding processes with complex and drastic molten pool dynamic behavior. This paper proposes a novel approach for WQM using molten pool visual sensing and deep learning considering spatiotemporal features, the proposed deep learning network called attention fusion based frame-temporality two-stream network (AF-FTTSnet). Firstly, a passive vision sensor is used to acquire continuous dynamic molten pool images. Meanwhile, temporal difference images are computed to provide novel features and temporal representations. Then, a two-stream feature extraction module is designed to concurrently extract rich spatiotemporal features from molten pool images and temporal difference images. Finally, an attention fusion module with the ability to automatically identify and weight the most relevant features is designed to achieve optimal fusion of the two-stream features. The shop welding experimental results indicate that the proposed AF-FTTSnet model can effectively and robustly recognize five typical welding states during helium arc welding, with an accuracy of 99.26%. This model has been demonstrated to exhibit significant performance improvements compared to mainstream temporal sequence models.

Read more at Journal of Manufacturing Systems

CRG Automation | Dual Robot, Dual Process Welding Cell

Welding and the Automation Frontier

📅 Date:

✍️ Author: Brian Potter

🔖 Topics: Robot Welding

🏢 Organizations: ABAGY

There are two main types of welding to consider when talking about welding automation. The first is resistance welding. With resistance welding, the parts to be welded are pressed between two electrodes. Current then runs across the electrodes, and the electrical resistance of the metal between them causes the parts to heat up, melt, and weld together. Resistance welding can be done as “spot welding” (where just a single point is welded), or as “seam welding” (where a continuous seam is welded). Resistance welding is generally used to join thin materials, such as sheet steel. The second type of welding is arc welding. With arc welding, an electric arc is created between a metal electrode and the metal to be welded, and the heat of the arc melts the metal. The arc is then moved along the joint to be welded. There are several different types of arc welding, such as MIG, TIG, and SMAW, which differ in things like the material of the electrode, whether the electrode is consumed in the process, and how the weld is shielded from the air. In addition to these, there are other types of welding such as forge welding, laser welding, friction welding, and oxyacetylene welding. But for the last 100 years most welding, and most welding automation, has been done with either resistance or arc welding.

Advancing welding automation technology, then, seems to have mostly taken tasks that were already automated to some degree, and made them more efficient. Better welding robots and weld sensors reduced the need for expensive machine retooling, and reduced the number of machine operators. It’s had comparatively less effect on skilled welder employment - better sensors, cobots, and portable welding rigs have changed the calculus somewhat, but a robotic welding system is still far less capable than a manual welder in terms of the sort of variation that it can cope with and the sorts of problems it can solve.

Read more at Construction Physics

Smart Welding with MAiRA

Behind the A.I. tech making BMW vehicle assembly more efficient

📦🦾 DTC startups are breathing new life into dying American factories

📅 Date:

🔖 Topics: Robot Welding

🏢 Organizations: Buck Mason, Breeo, American Giant

When DTC startups like Warby Parker, Everlane, Away, and Allbirds popped up in the early 2010s, they were skilled at connecting with a new generation of customers online. When it came to product, however, most outsourced their manufacturing to factories overseas. Across industries, the number of U.S. factories has fallen by 25% since 1997. That decline is steeper in apparel manufacturing, which has decreased by 85% since the early 1990s.

Jonathan Miller, Breeo’s co-founder and CEO, had no experience in manufacturing before he launched the company; his background was in marketing, enabling him to build a digitally-native business. But his partner, Andy Kaufman, had spent years in metalwork, which gave him the skills to invent Breeo’s smokeless fire pit technology. “We’re a DTC business but also a very traditional manufacturer,” says Miller. “Marrying the two brings unique opportunities, but also unique challenges.”

One way that the company kept up with demand was by investing in technology. In 2021, Breeo invested in robotic welding machines that could automate some of some strenuous and repetitive labor. Miller says this has improved the efficiency of the company’s workforce. “We can put our craftsmen towards tasks that require more skill,” he says.

Meanwhile, Miller says he is on Breeo’s factory floor almost every day. “When the customer service team hears of an issue, they walk over to the factory to talk to the workers who can fix the problem immediately,” he says. “This happens daily.”

Read more at Fast Company

🦾🧑‍🏭 The Robotization of High-mix, Low-volume Production Gains Momentum

📅 Date:

✍️ Author: Kate Degai

🔖 Topics: Industrial Robot, Robot Welding

🏢 Organizations: ABAGY

One company, ABAGY, overcomes the limitations of traditional robotics. With this software, manufacturers can use robots for custom projects or even one-of-a-kind parts. No robot programming is required. The software automatically generates a robot program to produce a specific product, which only takes minutes. Using machine vision, the system scans the parts and adjusts the robot’s path depending on the actual position and deviations of the product.

A manufacturer in Sabetha, Kan., already had a robotic cell, but wanted to increase its utilization. The robotic cell was used for a limited number of parts because the programming was tedious. After implementing a new system with AI and machine vision, the setup time reduced dramatically — only 10-to-15 minutes for a new product — and the robot can now be used for many more products. It used to take 90-to-120 minutes to program a robot to produce one rotor. That’s a big win for a manufacturer with high-mix production. In the first month of the robotic cell’s operation, the company created 50 different technical charts. The company plans further robotization of production.

Read more at Fabricating & Metalworking

🦾 Xaba and Rolleri Partner to Develop a Cognitive Autonomous Cobot Workcell

📅 Date:

🔖 Topics: Partnership, Cobot, Robot Welding

🏢 Organizations: Xaba, Rolleri

Xaba, developers of xCognition, the first AI-driven robotics and CNC machine controller, today announced a collaboration with Rolleri Holding SpA focused on the development of a cognitive, autonomous collaborative robot (cobot) workcell for welding operations in manufacturing. The collaboration enables the integration of xCognition with Rolleri Robotic cobots.

To showcase the benefits of xCognition, Xaba and Rolleri recently completed ISO 9283 tests in Xaba’s robotic lab. A FARO Vantage Laser Tracker System was used to acquire all data needed to train the xCognition machine learning model and to validate trajectory accuracy improvements. The successfully completed tests showed 10 times performance improvements in absolute positioning and trajectory accuracy, and five times improvements in relative positioning and trajectory accuracy. As a follow up to the initial tests, Xaba and Rolleri will be undertaking Tig and Laser welding tests to further validate welding quality improvements such as improved accuracy and repeatability.

Read more at Business Wire

🦾 Factory Visit: Investment bankers tour client’s robot-filled machine shop

📅 Date:

🔖 Topics: Cobot, Robot Welding, Quality Assurance

🏢 Organizations: Universal Robots, Vectis Automation, New Scale Robotics

“Many shops can’t get the parts out because their quality control has gone from four days to six weeks. They just don’t have the staff and it becomes a major bottleneck in the company,” Dave Henderson explains. New Scale’s Q-Span workstation is a robotic arm that has grippers on the end that can pick up parts and then measure them using an automated dimensional gauging system.

“We saw a need for lower cost, easier to use, less risky, and more flexible automation to allow small- and medium-size enterprises to leverage automation just as the big guys have for decades,” according to Josh Pawley. “The shortage of welders and skilled fabricators is the biggest driver of our business,” says Pawley. “It’s largely the nature of welding – it’s dull, dirty and dangerous in many cases. There are not a lot of folks going into the space, and the average age of a welder is in the late 50s. But the most dull and dirty jobs can be supplemented with automation.”

“Incremental automation is very important, the ability to break it down into step-by-step pieces,” Henderson says. “We consistently get requests from people who were thinking of heavy integration, but they haven’t had any automation before, and they wanted a turnkey system which cost $1 million and take a year to implement. “But traditional automation for some fabricators is too much to jump into to begin with. We can get them up and running in three months for $100,000. By doing that you empower your staff to operate machines, as opposed to having turnkey systems that are dependent on the system integrator. So you get the best out of both automation and your people.”

Read more at Robotics and Automation News

Weldloop – Optimize your automated welding process by linking process and inspection data

How Does Advance Concrete Use a Robot for Welding

AI improves welding performance

📅 Date:

✍️ Author: Lincoln Brunner

🔖 Topics: Robot Welding

🏢 Organizations: Novarc Technologies

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.

Read more at The Tube & Pipe Journal

Hybrid machine learning-enabled adaptive welding speed control

📅 Date:

✍️ Authors: Joseph Kershaw, Rui Yu, YuMing Zhang, Peng Wang

🔖 Topics: machine learning, robot welding, convolutional neural network

🏢 Organizations: University of Kentucky

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.

Read more at Science Direct

Sparks fly as BAE Systems brings innovation to welding

📅 Date:

🔖 Topics: robot welding, robotics

🏭 Vertical: Defense

🏢 Organizations: BAE Systems, US Army Research Laboratory, Wolf Robotics

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.

Read more at BAE Systems

Making Welding Accessible to All

📅 Date:

✍️ Author: Ed Sinkora

🔖 Topics: robot welding, cobot

🏢 Organizations: Miller Electric, Universal Robots

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.”

Read more at SME Media

John Deere and Audi Apply Intel’s AI Technology

📅 Date:

✍️ Author: David Greenfield

🔖 Topics: AI, quality assurance, robot welding, machine vision

🏭 Vertical: Agriculture, Automotive

🏢 Organizations: John Deere, Audi, Intel

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.

Read more at AutomationWorld

Tractor Maker John Deere Using AI on Assembly Lines to Discover and Fix Hidden Defective Welds

📅 Date:

✍️ Author: Todd R. Weiss

🔖 Topics: AI, quality assurance, machine vision, robot welding, arc welding

🏭 Vertical: Agriculture

🏢 Organizations: John Deere, Intel

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.

Read more at EnterpriseAI

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BMW Spartanburg Welding Body Shop