Helios designed for the corrugated converting industry
Glen Arm, Maryland – Helios, SUN Automation Group’s new artificial intelligence and machine learning platform specifically designed for the corrugated converting industry, brought its solution for the corrugated industry to the SuperCorr 2021 in Orlando, Fla. after launching it earlier in the year.
The platform is OEM independent and designed to provide corrugated manufacturers with access to solid information and exploitable on the performance of their machines, thereby minimizing downtime, optimizing maintenance schedules and maximizing profits.
Helios CTO Matthew C. Miller said:”Many corrugated cardboard factories rely on human intuition and experience to make their decisions. With Helios, anomalies imperceptible to even the best trained operators can be detected in real time and addressed. Machine learning capabilities mean the platform only gets smarter as it processes more data and user feedback.” Helios is designed to minimize downtime, maximize profitability, and reduce the opportunity cost associated with taking machines offline for preventative maintenance only (as opposed to major malfunctions). These high-level benefits translate into specific operational benefits of cost and resource optimization and actionable insights.
Some of the most beneficial information is ordering preventative / proactive parts, knowing the exact time and cost of part replacements, the ability of operators to identify the source of slowdowns and other problems, and training on how to do it and operator efficiency to help machine operators learn and adapt to best practices.
Helios provides essential information from an accessible and user-friendly dashboard offering three key benefits: remote monitoring, predictive maintenance and anomaly detection.
Remote monitoring provides indepth information on current and historical machine operation and performance, which can be viewed and accessed in real time from any device. Predictive maintenance optimizes machine maintenance intervals using artificial intelligence that adapts to the operation and use of the machine. Anomaly detection informs users of abnormal machine conditions, allowing operators to react to a potential problem before the failure occurs.
