Greif-Velox develops self-organizing systems for Industry 4.0 packaging efficiencies
22 Oct 2021 --- Greif-Velox is overcoming the challenges of decreasing warehouse capacities and product customizations by developing customized, self-organizing packaging systems. These systems optimize processes through intelligent networking, offering measurable benefits for planning, production, logistics and maintenance.
PackagingInsights speaks with the German packaging machinery specialist about how Industry 4.0 advancements and the Internet of Things (IoT) are increasing packaging efficiencies.
Due to Greif-Velox’s system flexible data interface, machine parameters in the filling process, including system notifications, current weights, bag counts, current pressures and torques, are measured by sensors and can be collected in the cloud.
This information can be locally stored in the company’s control system, customer system, and cloud services. The data is linked with the customer’s process control system via application-specific IoT gateways.
Upgrading to smart industry
Greif-Velox’s technical interfaces are standard interfaces, such as OPC UA or ProfiNet, allowing customers to access the programmable logic controller’s data directly.
“As for what data should be transferred, we develop solutions that are completely tailored to the customer,” explains Benjamin John, director of engineering at Greif-Velox.
“We adjust for the requirements at hand and provide targeted advice because, usually, customers have a lot more options than they think.”
According to Greif-Velox, any existing system can be upgraded to make it compatible with smart industry, saving users integration costs as not every component has to be replaced.
Transforming packaging processes
The use of sensor-monitored, cyber-based systems is fundamentally changing production and packaging processes. “There is no longer an order planner issuing a paper order to the machine operator who then sets the desired machine parameters,” says John.
Instead, the process control system decides for itself the quantity of product to fill to optimize performance at any given time and sends the information directly to the machine via the product management system.
“This is made possible through the central evaluation of sensor data and the communication between the individual components, for example, pre-production components informing the system of the status of the upstream product,” continues John.
“This fully digitized process allows users to avoid downtime due to poorly coordinated sub- processes. If transportation logistics are coordinated, warehouse capacity can also be optimized, so the product is picked up right after production.”
Identifying efficiency issues
The system controller intervenes during the filling process to optimize processes based on an intelligent data processing algorithm and information from neighboring processes.
“Take the continuous comparison of the target and gross weight, for example. If the gross weight deviates from the target, the dosing unit immediately responds and adjusts the filling quantity accordingly,” explains John. This dosing unit ensures optimal filling of bags at all times under the most varied conditions.
“Based on the process data, we can automatically view the efficiency of a system and see where efficiency issues arise.”
In the future, this information can be clearly displayed on a dashboard, giving machine operators an overview of the machine’s performance at any time and from any location via a VPN connection, letting them adjust the production process as required, he adds. As a result, customers benefit from increased output and efficiency.
Quality management advantages
Intelligent, networked systems also offer significant advantages for quality and complaint management. “If barcode labels and the corresponding scanners are used to label the products, specific containers can be identified, and their unique data can be saved,” details Janis Feye, software developer at Greif-Velox.
These intelligent systems make it possible to save the torque used to fasten a drum for later verifying the drum was correctly fastened. If a complaint is received for a certain product batch, a data analysis from that production period can be used to identify the cause of the problem faster and remedy it for the future.
“This [data analysis] reduces recall costs and improves the reputation of the manufacturer in terms of product quality,” adds Feye.
Predictive maintenance advances
Greif-Velox is also developing models that utilize the process data for predictive maintenance to increase machine performance and efficiency further. “This involves compiling and evaluating time-, cycle- and condition-based data,” explains John.
If a system component needs replacing, the system can inform the user in advance before a potential breakdown. Indicators can include how long a part has been in service and deteriorating performance data.
Predictive maintenance can minimize breakdowns and downtime in the production process. This optimized machine utilization will lead to an overall increase in productivity, asserts John.
Greif-Velox concludes it is pursuing continuous improvements in flexibility, autonomy, performance, availability and transparency along the value chain with IoT developments for packaging systems.
By Joshua Poole
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