Possibilities, Limitations, and What it Means for the Shop Floor
Artificial intelligence (AI) is one of those buzzwords that seems to be everywhere: finance, healthcare, and even the apps on your phone. But what about welding? Could AI make its way into the booth or onto the shop floor? The short answer is maybe, but not in the way most people think. AI can potentially make welding “smarter” and more efficient, but it faces real obstacles. To understand what it might mean for welders, we need to first look at how today’s machines already work and why adding intelligence is not as simple as flipping a switch.
The Promise and the Reality
Modern welding machines are already surprisingly smart. Compared to the old transformer boxes, today’s digital inverters monitor voltage and current thousands of times per second. They use carefully designed algorithms — step-by-step procedures coded by engineers — to adjust the arc in microseconds, smooth out spatter, stabilize joint penetration, and optimize performance across different materials. In that sense, these algorithms act like the tricks a welder learns over years of practice, only locked into code. However, welding processors are already working at their limits to keep the arc stable. Asking them to run complex AI models would be like asking a welder to grind, fit, tack, and weld production beads simultaneously. Something would give.
A simple comparison can help make sense of where AI fits in. Traditional welding programming is like a procedure specification: Every step is written out. If the plate is this thick, run this amperage. If the joint is overhead, adjust it in this way. Today’s algorithms are more adaptable, like a machine setting that has been fine-tuned, fixing errors in stickout or joint prep in real time. AI, by contrast, would be more like an experienced welder with thousands of passes under their belt. They don’t just follow a set of rules. They learned through repetition to hear, feel, and see when something is off, even before it shows in the bead. That is what AI promises: machines that not only follow instructions but can learn patterns and adapt to problems.
Hannes Hinterbichler, a data scientist at Fronius, noted the gap between that promise and today’s reality: “Currently, AI is not in the loop . . . there is no AI processing the voltage we measure during welding or providing ideas or feedback,” he said.
AI’s biggest challenge is that it only learns from data. And in welding, the data problem is real. In the lab, welds are made under clean conditions with perfect prep and carefully tuned parameters. Those welds are flawless, and while they make impressive demonstrations, the data cannot teach much. Training AI on perfect welds is like teaching an apprentice using only x-ray-quality beads. They will be lost the first time they encounter porosity, bad gas flow, or worn consumables. What AI really needs is the messy stuff: the bad welds, the spatter, the porosity, the birdnesting wire. Without examples of failure, the system cannot learn to recognize or predict it. That makes the problem even harder because error rates are already low in real production. There simply is not enough failure data to go around.
Realistic Applications for AI
Another big question is how to get AI into the welding process physically. One path is through advanced inverters with enough processing power to run AI directly. That would allow the machine to “think” about what the arc is doing. But adding that kind of computing power drives up costs, raises energy demands, and introduces heat management issues inside the machine. Another path uses external sensors, including cameras, microphones that listen to arc sound, or additional monitoring equipment that sends information to an external processor. This path could make AI accessible even with simpler machines, but it comes with its own price tag. The challenge isn’t just installing sensors. All that collected data must be translated, processed, and transmitted back to the machine quickly enough to be useful.
Because of these challenges, the most realistic applications for AI in welding today are indirect rather than direct. Instead of trying to control the arc in real time, AI can help in supporting roles. Systems can monitor consumable wear and predict when a contact tip or nozzle needs to be replaced to prevent downtime or bad welds before they happen. Machine vision can inspect bead geometry or joint penetration depth more quickly and consistently than the human eye, speeding up quality assurance. Training simulators already use AI to help new welders learn faster by analyzing their hand motions and arc behavior then giving targeted feedback. These applications do not require the machine to make arc decisions in real time, but they still improve the welding process in ways welders can see and appreciate.
Even so, adoption of AI comes down to economics. Outfitting a manual welding shop with sensors and processors for AI would be costly. For a fabricator running 20 or 30 stations, the investment must pay off in fewer weld failures, less downtime, or faster training or it simply does not make sense. That is why most near-term AI development will likely appear in robotic welding cells or high-end automated production, where errors are expensive and the environment is more controlled.
Hinterbichler explained, “For the product that will be sold to the customer, we have to justify the costs of the chips and so on. So that’s far away.”
Looking Ahead
It is worth noting that not just anyone can make this happen. Developing AI for welding requires three very different skill sets: deep knowledge of welding physics, advanced digital inverter design, and expertise in data science. Only a handful of global welding technology companies have the resources and experience to attempt it. And even for them, development will not be quick.
Realistically, welders should expect AI to enter the field in stages. In the next few years, it will mostly appear in training systems, quality inspection, and predictive maintenance tools. A few years later, it could play a bigger role in robotic or automated welding, where data is easier to capture. True AI models that influence the arc in live production welding are even further away and would be possible only if the hardware, data, and economic challenges can be solved. Though these predictions are based on conversations with experts in AI and manufacturing, technological leaps are possible.
Rapid advances could shorten the timeline if the industry focuses enough on AI, but the takeaway is straightforward for welders. AI will not replace them and won’t be in every machine tomorrow. But over the next decade, it may become another tool that helps welders do their jobs better by spotting problems earlier, training apprentices faster, and keeping weld quality high.
“I don’t think AI will replace the expert,” Hinterbichler said. “You will have the expert who has the information from the AI and will maybe get more efficient because he has all his experience and the AI on top to support.”
Just as the shift from transformer machines to modern inverters changed how welding feels, AI may be the next major evolution. The difference is that this one will take even more time.