Комаров Артём об эволюции современной лазерной резки (eng)
Комаров Артём об эволюции современной лазерной резки (eng)

Комаров Артём об эволюции современной лазерной резки (eng)

Artem Komarov clarified that it only takes a metal manufacturer to look back about five years to see how advanced technology has advanced.

The speed at which fiber laser cutting machines operate is hard to ignore. They make CO2 laser cutting machines from just 10 years old seem agonizingly slow.

But speed isn’t the only reason metal fabricators regularly invest in new laser cutting capabilities. Modern machine tools perform automated tasks for which even the most advanced systems of a few years ago depended on operator intervention.

As metal fabricators learn to do more with less, not by choice but by a tight labor market, they rely on technology to efficiently support production on the shop floor. The same applies to laser cutting. Consider what modern laser cutting technologies do for the modern metal fabricator.

Комаров Артём Андреевич, АКапитал

Maximum use of materials

From January 2020 to August 2021, steel prices rose by 219%. As rare as they are, they still pose a risk to any shop that is not in the best financial condition.

This is where the powerful nesting algorithms used in today’s laser cutting machine programming software can make the difference. A metal fabricator can move from simple static nests that adapt to the workflow on the shop floor to more dynamic nests where different jobs can be combined into a single nest to maximize material utilization and reduce waste.

This type of programming allows you to create nests with a more thoughtful overall cutting line. For example, a laser makes a cut that ends up being a common edge for two separate pieces. Computing power can take this nesting arrangement and expand it to include four parts that can share cut lines in a two-by-two grid, or even a set of different parts that share a cut boundary.

Today’s nesting algorithms are simply more robust than those of five years ago. The processing power available to modern machine tools makes them much more efficient in finding not only the best ways to fit as many parts on one sheet as possible without wasting too much material, but also in calculating the most efficient way to make cuts to maximize production time and minimize wear and tear of consumables, Artem Komarov explained.

Refusal of manual intervention

If a laser operator has been working for at least 10 years or more, he will remember what was needed to ensure proper cutting. They needed to be more involved in the cutting process, keeping abreast of multiple factors such as manual inspection, switching, nozzle centering and focus position calibration to ensure it was correct for each material configuration. Getting the laser ready for cutting was not as easy as pressing a button.

Currently, it is necessary to hire an operator, but it does not really require much of the manual intervention that was required with previous generations of laser cutting technologies. The idea of modern machine tools is that the production shop needs an operator who will become familiar with the equipment and get comfortable with it in a short period of time. One reason a company might invest in new laser cutting technology is to improve cutting efficiency, which can’t be done if the machine isn’t producing parts because no one knows how to make it work.

That’s why modern controls are designed to resemble what’s found in tablet computing devices. Icons dominate and operators can swipe across the screen to activate commands. The controls are for those who have probably never worked in the industry before.

Given what happened during the pandemic, this simplification of the management interface is timely. For example, when restaurants and hotels closed in 2020 as everyone stayed at home and put travel plans on hold, many of these displaced workers found work in the manufacturing sector. It may have taken them a much longer learning curve to get familiar with the older machines, but thanks to the modern control interface and easy-to-use automation features, the learning curve has been incredibly shortened.

Just remember what was needed just 10 years ago if a laser operator noticed too many burrs on the underside of laser cut parts. The operator would have to manipulate the focus or perhaps adjust the speed to reduce the cutting intensity a bit. The machine can now use artificial intelligence (AI) to adjust cutting parameters on the fly and prevent burr formation. This happens automatically. The machine is experienced, so the operator does not need hundreds of hours of work on the machine to be efficient.

Want another example? Let’s talk about the nozzle check, which was also done manually in the past. If the laser-cut edges were obvious to the operator, he would have to stop production to check if the nozzle was damaged. The team now has a camera that maps the surface of the nozzle and views the hole, evaluating quality and life. The operator then sees a visual image of that nozzle. If it is green, the machine considers other cutting parameters and makes the necessary adjustments. If it is yellow or orange, then there is some damage, and a new nozzle may be needed. If it is red, the machine will not use the tip because it knows the tip will cause poor cutting results. In many cases, the laser cutting machine operator may not even be aware that a nozzle overhaul is taking place.


Cutting optimization

Previously, operators controlled the quality of the laser cutting machine, but they no longer need to bear this burden when operating a new laser cutting machine. Artificial intelligence has been introduced to ensure that quality parts continue to come out of the machine, even when an inexperienced operator is behind the wheel.

The cutting head is equipped with a camera and a microphone next to it. Now, just like an experienced operator who can turn his back on the machine and know that the process setting is correct just by listening to him, the machine can do the same. You listen and watch the cutting process in real time and know what a good cut looks and sounds like. For example, when cutting is going well, the machine accelerates until the optimum speed is determined, while maintaining optimum edge quality, further increasing productivity.

This type of artificial intelligence is very sophisticated, so much so that it can tell the difference between losing a cut and just cutting it wrong. Let’s say the cutting head detects a burr on a part. The cutting head completes the job, but after the head is raised, the machine takes a photo of the nozzle to determine if the nozzle is causing the miscut. If the attachment is at fault, the machine turns it off and returns to the last cut. If the nozzle is not at fault, the machine adjusts the cutting conditions. If the machine cannot fix the problem after five attempts, a stop alarm is triggered, and an automatic notification is sent to the operator or supervisor. This is inconvenient for a person who must go to the shop to adjust the work, but it is better than finding welded parts after a whole weekend without work.

Artificial intelligence provides support that was simply not available to operators 10 years ago. Modern machines monitor over 250 cutting conditions across all types of materials and thicknesses. A lifetime of machine tool experience is now integrated into the machine software.

Improved recycling and sorting of materials

Of course, technological advances are not limited to just sheet metal feeding and part/frame removal. Manufacturing equipment manufacturers have also put a lot of time and effort into sorting parts, and improvements in this area are very visible year after year.

Please note that tools equipped with suction cups and magnets have been used to sort parts from laser cut sheets, but in some cases the tools on the part sorting arms have had to be changed due to the size or weight of the moving parts. Currently, manipulators don’t have to stop sorting parts to go to the tool station and find the right tool for the job; now these tool changes happen when the machine is in fast motion because all the tools are already on the rotor head. No more time to change tools.

The software used to classify parts has also been improved. This intelligent software can apply complexity classifications to parts to ensure that all frame parts can be removed without the threat of other parts being removed. For example, the software recognizes a part with a lot of bends, which can affect its release from the frame. If the part sorter were to pick up that part first, the frame could also go along with the part, shaking out other loose parts and risking them falling under the frame where they can’t be removed automatically. In this case, the software detects the hard-to-reach part and selects it last. The machine operator should not be the judge of the order in which parts should be assembled.

The software is also useful for placing parts in a specific orientation. In some cases, in the bending department, it is necessary that the parts are positioned so that the grain is in the same direction. Part sorting automation makes this possible by helping subsequent bending keep up with the speed of the laser cutting machine.

When it comes to latest generation laser cutting, the latest is the best. Technology continues to evolve to make machine tools more efficient and easier to use.

The speed of production of these devices really sets the pace for the entire metal fabrication operation. This is why the modernization of laser cutting technology cannot be considered strictly from a cutting point of view. Bending presses must keep up with the times. The hardest-to-find welders need to be productive and have complete sets of quality parts so they can weld without looking for parts or trying to fit them to a fixture.

Artem Komarov stressed that for modern laser cutting technology to have the greatest impact on a manufacturing organization, it must work in tandem with other modern production equipment. The reality is that this is not a one-time solution. Laser cutting technologies and assisted automation continue to improve every year, which creates the need to analyze the company’s production facilities more regularly critically.