Robotic automation transformed machining practices by unlocking new levels of efficiency and accuracy. Now, artificial intelligence (AI) could usher in a similar revolution in software automation. The combination of AI and robotic laser cutting is disrupting this already once-disrupted process.
AI can integrate with laser cutting robots in several ways, from their control software to operational monitoring to maintenance. Here’s a closer look at how these AI use cases are reshaping the robotic laser machining field.
1. Adaptability
One of AI’s biggest implications for robotic laser cutting is that it brings needed adaptability to automated systems. As efficient as robots are, they’re typically not great when dealing with change. AI fixes that.
While laser cutting’s repetitive nature makes it ideal for automation, it still involves a good deal of variability. Some metals, like copper, can reflect laser radiation, leading to manufacturing defects, and varying thicknesses and purities require different settings and approaches.
Humans can adapt to these changes, but are slow. Conventional robots are fast, but can’t account for this variety. AI can do both. It maintains robotic efficiency while recognizing when changing conditions require adaptation, letting automated laser equipment become more flexible.
2. Faster Setup
AI also makes it easier to set up a laser cutting robot. Technical skills gaps are a major barrier to automation, with 25% of consumer goods manufacturers saying they lack the knowledge to implement it. Similarly, 21% say they don’t have the technological readiness.
Because AI is itself a complicated technology, it may initially seem counterproductive in light of these challenges. However, it removes a few crucial obstacles. AI-enabled robotics control systems can eliminate the need for code, letting employees program robots with conversational commands.
Alternatively, AI assistants can suggest machine parameters to streamline the process of setting up a cut. Whatever the specifics, AI automates the more complicated parts of the setup, letting shops take advantage of robot welders faster.
3. Fewer Input Errors
When AI automates steps like setting parameters and aligning tools before a cut, it also reduces errors. Even in a robotic system, these are often manual measures because they require adaptability. However, humans are prone to making mistakes, especially when there are so many numbers to account for and so many cuts to make in a day.
Reducing input errors is all the more valuable in light of ongoing labor challenges. Manufacturers are on track to be short 2.1 million workers by 2030, leaving the remaining workforce with work meant for more people. Even veteran workers are liable to make mistakes when overworked and stressed, so automation can offer some relief.
A lower likelihood of mistakes also helps facilities get new hires up to speed in less time, as they’ll need less supervision. That’s a big edge when trying to counteract widespread talent gaps.
4. Optimized Maintenance
AI is useful in robotic laser cutting even when it doesn’t direct the cutting process. It can also supercharge robot performance and repair through predictive maintenance (PdM).
Regular, proactive maintenance is key to safe and effective laser operations. Accumulating debris can obstruct the laser, affecting cut accuracy, and failing to fix issues while they’re small can lead to costly breakdowns. At the same time, normal maintenance inspections aren’t always necessary and lead to considerable downtime.
PdM uses AI to analyze laser performance and recognize the early signs of wear and tear. By predicting when machines will need repairs, it informs more relevant and timely fixes. Shops can schedule downtime to address an issue before it causes larger problems while avoiding lost time from any unnecessary maintenance.
5. Self-Calibration
PdM sees AI alerting workers to the need to adjust laser cutting robots. In other cases, AI can do the adjusting itself to minimize errors and save time.
Self-calibrating sensors employ AI to recognize when data readings drift from their benchmarks and recalibrate the system accordingly. As with other data-heavy and repetitive tasks, AI is faster and more accurate than humans when performing such work. Today’s AI-enabled sensors can self-calibrate with error rates as low as 0.17% in just a few seconds.
Avoiding mistakes here is crucial, as any calibration error will have compounding effects on laser cutting accuracy. However, that also means any improvements from AI create a snowball effect of precision and efficiency benefits.
Best Practices for Implementing AI in Laser Cutting Robots
Like robotic laser cutting itself, AI’s effectiveness depends on facilities’ ability to implement it correctly. Consequently, shops must pay attention to a few key considerations to get an acceptable return on their investment.
Only one in 10 manufacturers says their AI applications have generated significant financial benefits. The biggest reason behind this shortfall is a mismatch between AI’s capabilities and the facility’s needs. Investing in AI for AI’s sake is a surefire recipe for wasted money. Instead, start with current laser cutting workflows to see where they must improve and ask how AI can help with that use case.
Some manufacturers will benefit most from self-calibrating sensors or PdM. At other plants, speed of cut setup is more impactful. These varying applications require unique approaches to AI, including different hardware, model types and implementation steps. Consequently, determining the best AI use case for the specific situation is key to success.
Data availability and governance are other common barriers. Poor-quality data costs companies an average of $12.9 million annually, as AI is only as reliable as the information it analyzes. The answer is to gather large amounts of relevant training data, clean it before feeding it to AI models and ensure all operational sensors are accurate.
Protecting AI and its data is also crucial to prevent tampering. Only the IT team in charge of programming the AI should have access to the model and its data. Their accounts must use multi-factor authentication to prevent breaches, and network monitoring is a good idea, too.
AI Takes Robotic Laser Cutting to New Heights
Laser cutting robots are a huge improvement over manual machining, but they still have room to improve. AI can guide them to those needed advancements.
Learning where AI is most impactful in laser cutting is the first step in optimizing this process. When manufacturers understand what this technology can do and how to ensure it does that, they can transform their laser operations.
