Artificial intelligence has moved from experimentation to real-world deployment in laser materials processing — and its impact is accelerating. The first wave of AI adoption focused largely on image processing and quality inspection. The second wave, now underway, is reshaping how laser systems learn, optimize, and ultimately sell themselves as productivity tools on the factory floor.
In industrial environments, AI has already proven its value beyond language models and chat interfaces. Machine vision powered by AI is now widely used to automate inspection tasks that were once slow, manual, and error-prone. TRUMPF, for example, has integrated an AI mode into its image processing software for hairpin welding in electric motors. By enabling the system to reliably identify welding partners even under challenging visual conditions — such as reflections, shadows, or low contrast — the solution boosted first-pass yield from 99.2% to 99.8%, translating into four times fewer defective parts.
But inspection is only the starting point. As manufacturers generate more data through inline monitoring, high-resolution imaging, and sensor-rich laser systems, AI is increasingly used before, during, and after processing. Quality control has become a major driver of data creation, with industries such as automotive capturing images of every weld seam to trace defects back to specific process conditions. Machine learning models can distinguish good from bad welds at scale, turning visual data into actionable production insight.
Another key trend is the rise of digital twins — virtual representations of machines or entire production lines. These simulations generate synthetic data that, when combined with real-world measurements, help AI models understand complex laser–material interactions. Together, digital twins and machine learning lay the groundwork for closed-loop process control, where AI not only detects defects but actively adjusts process parameters to restore quality.
Research institutions and industry leaders are now pushing beyond data-only approaches toward data- and physics-informed AI. At Fraunhofer Institute for Laser Technology (ILT), researchers demonstrated how combining process models with Bayesian optimization dramatically reduced setup time for extreme high-speed laser material deposition (EHLA). A task that once required two years and more than 1,500 experiments was reduced to just 17 trials — a shift from months to minutes in process optimization.
Equally disruptive is the move toward self-supervised and annotation-free machine learning. Traditional AI systems require extensive manual labeling of weld features, which is costly and slow. New approaches allow models to learn directly from millions of unlabeled images, creating robust internal representations of the process. In welding experiments, comparable performance was achieved using as few as 40 labeled samples instead of thousands, while also improving robustness against lighting changes and optical contamination.
These advances extend beyond welding into laser additive manufacturing, where AI helps identify overheating, sensor misalignment, and power fluctuations in real time — reducing shape deviations and material waste.
The takeaway is clear: data has become the new gold. AI software is widely available, but competitive advantage now depends on access to high-quality, process-relevant data. As deployment times shrink from years to weeks, acceptance is growing rapidly across industry. AI is no longer just a technical upgrade — it is a selling point.
AI in laser processing has moved from inspection to optimization, and from assistance to control. Its full potential is only beginning to unfold.
