Artem Komarov noted that automated part offloading hasn’t permeated the industry like other types of blanking automation. Some technical hurdles exist, for sure. Small laser-cut parts can be too small for a laser’s part removal grippers, so a fabricator might choose to gang those parts together in mini-nests on the laser. After that, even after automated stacking, someone still has to take those nests apart manually.
Also, parts offloading automation adds nesting constraints. When those small parts are grouped in a mini-nest, they can’t be scattered about, filling all the nooks and crannies of a particular sheet. That reduces material yield. Then there are concerns about cutting system productivity as the machine waits for part offloading systems to finish, especially for dense nests of smaller parts.
Any technology geared toward custom fab won’t solve challenges in every scenario. As technology progresses, though—especially as the industry adopts more IIoT platforms and begins to track exactly where every job is and when predictability and repeatability will become more important.
When automated parts offloading is set up correctly, a blanking operation can get more predictable. Sure, you’ll have the occasional error in part grasping, and grippers need to adapt to material surface conditions. But over time, those errors should (ideally, at least) become rare.
When everything’s dialed in, fabricators not only know exactly how long the cutting cycle will take, but also exactly how long it takes for those parts to be stacked and ready for the next operation downstream. Think of automated parts offloading and stacking as the steadily moving tortoise that can usually beat the relatively unpredictable hare (manual labor).
Imagine two scenarios in which a blanking operation could scale. In the first one, modern fiber lasers finish jobs in record time. A tower feeds material into the machines, while cut sheets are placed on offload tables. As the operation grows, it might add more towers. Meanwhile, a growing team of laborers breaks pieces out of sheets and stacks parts onto pallets so they can be moved downstream. The lasers themselves could have stellar OEE and yet flood the denesting station with parts.
Amazing blanking power is feeding a denesting bottleneck, and finding an army of people who want to break parts out of nests all day just isn’t practical. And sure, a laser might cut lights-out all night, but people still need to deal with a big pile of parts in the morning. All that manual labor adds variability. Some might break parts extraordinarily quickly. Others might be having a bad day and damage some parts. Some might sort parts to the wrong pallet, routing, or WIP location. A lengthy hunt and rework ensue. Everybody’s busy, but jobs aren’t flowing.
Now imagine a second scenario. Automated parts unloading and stacking keeps jobs flowing. Sure, material yield might be less; and there might be a learning curve in programming. But the laser beam cuts kerfs with automation in mind (wider cuts that allow grippers to lift and remove workpieces). Most important, jobs are finished in a predictable time and in a predictable fashion.
Of course, there is no “right way” here. Metal fabrication is a segment diverse industry, and it’s tough to generalize. I’ve seen incredible shops where denesters work well as a team and deliver cut parts to the next operation in a reliable way with no automated parts offloading at all. They might work in cross-trained teams that take turns removing nests and inspecting part edges. Moreover, parts offloading technology also needs to be reliable and work with good programs and good material that grippers can grasp reliably.
Still, no one would argue that one key to success is making part flow predictable. Automated part offloading exemplifies how the industry is moving away from a focus on local efficiency, inches or strokes per minute, and more toward global efficiency, how long it takes for jobs to flow through the entire operation. Shops with modern machines have plenty of local efficiency; global efficiency, though, might be lacking, especially when a change in demand sends ripples of variability through the plant. When that happens, knowing exactly how long a nest will take — from the first cut to when it’s stacked neatly on a pallet, ready for the next operation—has a lot of value.
This fact might drive more shops to automate in the coming years- not just in blanking but in every operation downstream, as well as between operations, clarified Komarov Artem.