Steel service centers and large metal fabrication shops look for production efficiencies in many areas. They are typically willing to invest in storage systems and automated sawing equipment that offer quick and clean cuts, but where they tend to absorb costs is in labor related to material handling.
When bar stock, which is notoriously difficult to manage via automated handling systems, needs to be separated manually and picked one by one by an operator, the thought of evolving the production process into a lights-out operation seems lofty.
Production goals rank high on the priorities list, but so too does safety. Stefan Dolipski, vice president of Kasto, says that while steel service centers and larger fab shops spend a lot of money on manpower required to fill orders, there are also safety-related costs to consider.
“When lifting bars by hand or with slings,” he says, “there’s always a risk.”
One of Kasto’s most recently released products, Kastopick Bar, offers a solution. The robotic material handling system is designed to meet customer demand for a safer, more efficient way to support a lights-out or near-lights-out sawing operation.
Bar stock solution
Automated material feed systems are fairly straightforward: They are designed for uniform, straight, dry and predictable material that is also square, flat, rectangular or tube. Bar stock, however, is irregularly shaped and often covered in grease, rust or adhesive residue. One of the more significant complexities is that in most storage situations, bars do not follow a trajectory of uniformity like other materials.
“Bars are the big challenge,” Dolipski says. “And that’s exactly where our Kastopick Bar system steps up to the plate. It has a vision system that evaluates the individual bars laying in the storage cassette and calculates the best bar to be removed with no obstructions. Using two robots rather than one, the system can grab and remove bars even if they aren’t perfectly straight within the cassette.”
Kasto’s bar stock handling technology relies on 3-D sensor measuring probes mounted on the robots. The sensors scan the material in the cassette, and Kasto’s algorithm analyzes the data and determines the best possible gripping points on each individual bar.
“At the same time,” Dolipski says, “a collision check is performed to determine if there will be any collisions with other system components.”
Once collision-free pathing has been identified, the robot removes the bar and transports it to the target position. Collision-free pathing is more than just a convenience feature – it is critical in an unmanned environment where the system must identify bars correctly, avoid overlapping them and move them without creating a stoppage or safety incident.
While the new material handling system overcomes bar stock challenges, it is not limited to bar stock alone. It can also separate flat, square, hexagonal and round material as well as round and profile tube that is not wrapped or labeled. To achieve this, the system uses different gripper technologies.
“It can be equipped with magnetic, vacuum and jaw grippers,” Dolipski says. “With the ERP/Kastologic WMS system, data can be provided regarding which type of material is being processed, and it aids in the detection of materials to match the selection of grippers.”
Virtual versatility
Connecting to ERP and warehouse management system (WMS) systems also reduces the risk of manual errors that can occur when material movement and order status are tracked separately. Instead of relying on employees to verify inventory picks or update order progress by hand, an integrated system can automatically document each step.
With integration with ERP and WMS software being the norm today, it is something Kasto has configured into its material handling solutions. For example, Kastopick can operate semi-standalone, but when it is fully integrated into the user’s system, the full value of the solution is revealed as it creates feedback loops on order submission, order processing and material verification.
“In today’s world of data streams and visibility past the point of visual feedback by human verification or video cameras,” Dolipski says, “the system connected to an ERP/WMS system can let the order taker in Los Angeles see that the system filled the order in a facility in upstate New York. A clear data stream indicates what time the order was filled with what material and all other relevant datapoints.”
Less labor
Labor remains one of the biggest pressure points for steel service centers and large fabrication shops, especially with tasks related to manually sorting and picking bar stock. They are not only repetitive and time-consuming tasks, but they also tie up skilled workers in jobs that do not directly add value.
As a result, one employee can end up separating and staging material, creating a bottleneck ahead of the saw. When that happens, the investment in a fully automated, fast and efficient saw cannot deliver its full ROI.
Kastopick changes the equation by automating the picking process, which removes the human involvement that otherwise leads to delays, mistakes and potential injury risks. Dolipski notes that with this system, material handling becomes a “verified process” because it is not just moving material from one place to another; it is choosing the material correctly and ensuring the process is recorded, virtually eliminating human error.
Dolipski notes that the labor advantage becomes even more significant in lights-out environments. For example, in shops where an overhead crane must be shared throughout the facility, Kastopick frees up the crane for other work.
“As long as the system has power,” he says, “it is order-ready and can fill a customer’s order, making the process less dependent on all other warehouse operations.”
But even the best-planned scenarios can encounter challenges. For example, Kastopick recognizes material size and weight during the picking process, but if a given parameter is not within tolerance, the system reacts accordingly.
“The system can block the current order and proceed to the next one and continue the picking process while sending a message to an operator for manual intervention,” Dolipski says.
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