Weld engineering has undergone a dramatic transformation in the last two decades since I became a weld engineer in 2008. No longer are weld engineers confined strictly to rely on thick code books, running beads on endless amounts of metal plates for welder training, and learning how to program welding robots “on-the-fly.” With the development of welding automation, artificial intelligence (AI), and rapid growth in industrial robotics, these advancements in technology are now reshaping how welding engineers design, monitor, and optimize weld processes across various industries.
Please note that the welding technology I am mainly discussing in this article all relate to gas metal arc welding (GMAW) or metal inert gas (MIG) welding.
The rise of robotic welding.
Robotic welding has moved from novelty to necessity in high-volume manufacturing. Mostly due to the rising number of available manual welding jobs, which, according to the US Bureau of Labor Statistics, is approximately +45,000 openings per year from 2024 to 2034, will be available, so companies must learn to automate welding processes. [1]
Welding engineers now must:
- Program industrial robots to make high-quality welds, have a repeatable process, and reduce waste, all with minimal human interference.
- Input the correct welding direction (push or pull), torch angles, and travel speeds using robotic simulation software.
- Implement multi-axis coordination robot paths for complex assemblies using welding trunnions, fixtures, or weld positioners.
This shift has demanded that welding engineers become fluent not only in welding processes and metallurgy, but in motion planning, robot kinematics, and sensor integration.
AI-driven welding quality.
AI and machine learning are revolutionizing quality assurance in the welding industry:
- Real-Time Weld Defect Detection: Vision systems and AI models now identify porosity, cracks, and undercut during the welding process, not after. [2]
- Predictive Analytics: Machine learning systems analyze historical weld data to predict failures before they happen, helping welding engineers proactively adjust settings and parameters.
- Weld Examination In Real-Time: Engineers can now use thermal and acoustic signals in real-time to monitor welding joint integrity.
This data-driven approach makes weld quality measurable, traceable, and drastically more reliable than tradition methods of visual inspection from humans.
Smart welding cells and the integration of the Industrial Internet of Things (IIoT).
Welding engineers are overseeing connected robotic welding systems that feed data to the cloud or their company servers.
- Weld Quality Management Systems: These monitoring solutions track heat input, voltage, travel speed, wire feed rates, gas flow, and feed this information to a programmable logic control (PLC) or database in real-time. [3] Weld data monitoring systems can be visualized on dashboards, allowing engineers to optimize productivity across entire production lines.
- Welding Sensors: High-precision sensors can provide seam tracking, arc length, or real-time defect detection to notify welding operators immediately of any issues. [4]
- Welding Digital Twins: This process simulates weld outcomes before metal is ever touched by identifying bottlenecks, production line inefficiencies, or identifying equipment failures. Digital twin technology can provide software to support preventative maintenance schedules, process optimization, and enable data-driven welding decisions during production. [5]
These connected IIoT systems support remote diagnostics, preventive maintenance, and zero-defect goals, which are the pillars of moving a factory towards Industry 4.0.
Robotic welding equipment has rapidly advanced.
Below, I list the advanced welding technologies most welding engineers will likely work with:
- Offline Robot Programming Tools: Robot simulation software such as Fanuc’s ROBOGUIDE, or ABB’s RobotStudio, helps engineers with programming robot paths.
- Weld Simulation Software: Simulation software such as SysWeld allows engineers to build structures and simulate heat treatment and welding processes to ensure that it improves the product’s performance and quality. [6]
- Welding Simulators: These weld simulators allow welders to train using virtual reality (VR) or augmented reality (AR) technology. It provides a cost-effective solution for companies, that offers a somewhat realistic environment for welders to practice welding techniques, without consuming excessive amounts of testing plates and materials.
- AI Training Tools: Artificial intelligence will enhance efficiency and manufacturing processes on the production floor, but it will also improve welder training and make programming industrial robots and teaching welding easier for all to learn.
- Weld Vision Systems: Industrial robots can be equipped with 2D & 3D vision systems or laser vision tracking systems to track or find the weld joint.
- Robotic Arc Sensing Systems: If weld vision systems are too expense, and alternative on most welding robot systems can be updated for touch sensing to find the weld joint location before welding or through arc seam tracking (TAST) to follow the weld joint during welding.
- Adaptive Welding: Uses sensors and intelligent control systems that will, in real-time, monitor and adjust weld settings. This welding technology enables the power supply to adapt to variations in the weld joint, to make sure the robotic welding system has the most optimal and consistent weld quality possible.
This evolution of welding technology has expanded in the last 20 years. The weld engineer’s traditional skillset, which was limited to being a joining and materials expert, is no longer sufficient, and they must now also possess knowledge in simulation, software, and data collection.
A move towards autonomous welding decisions.
We are approaching a new frontier of self-adjusting and autonomous welding systems, that are powered by AI, and featuring advanced feedback loops. In my opinion, welding systems will eventually:
- Modify welding parameters autonomously in response to real-time data that’s collected from the power supply.
- Learn how to improve weld quality from previous welds performed, along with destructive and non-destructive testing data.
- Reduce the need for manual visual inspection and manual welding.
Welding engineers will shift more from the programmer/operator/engineer role to an optimization specialist who will design better workflows, where robots perform the welding process and AI ensures it meets quality requirements.
Automation, AI, and robotic welding support.
Automation, AI, and robotics haven’t just enhanced the welding process, they’ve redefined the job of a weld engineer. The modern weld engineer is not just a metallurgist or process expert but a technologist, programmer, and data analyst.
If your company is struggling to find qualified weld engineers to support your project, get with JOINER Services to find welding experts who have the automation and robot background you need to get the job done! Those who embrace these new welding tools will be leading the next generation of smarter, safer, and more scalable manufacturing.
Join today and start making improvements with your welding applications.
FREQUENTLY ASKED QUESTIONS:
Below, I list some FAQs about weld engineering in the robotic, automation and AI space.
Data Resources:
- US Bureau of Labor Statistics – Welders, Cutters, Solderers, and Brazers https://www.bls.gov/ooh/production/welders-cutters-solderers-and-brazers.htm
- Cornell University -Unsupervised Welding Defect Detection Using Audio And Video https://arxiv.org/abs/2409.02290?utm
- Monitoring Overview https://www.cweldtech.com/product-Monitoring-Overview.html
- Keyence – Sensors for High-Precision Welding Automation https://www.keyence.com/products/measure/resources/measurement-sensors-resources/sensors-for-high-precision-welding-automation.jsp
- SH Auto Parts – What are the benefits of using a digital twin for production monitoring in automotive parts welding lines? https://shautoparts.com/answers/digital-twin-welding-auto-parts/
- SYSWELD – Welding & Assembly Simulation Software https://www.esi-group.com/products/sysweld
