1
Background and Problem Statement
With over 20 different CNC machines and 3 different CNC controllers, including FANUC and Mitsubishi, a large tool manufacturer has a complex factory. They were leaving crucial data intelligence locked in machines because there was no solution available to establish the connection to the shop floor. By implementing a CNC monitoring system across all facilities to assess both machine status and part quality, the customer aimed to detect the issue s ahead of time, eliminate down -time and increase Assets life time value. The client partnered with Ranial Systems to rollout CognitIoT™ platform for the platform automation
2
Approach
To connect all of their different CNC machine types and collect all available machine data, the customer was offered the CognitIoT™ platform where the edge infrastructure and data connectivity solution was offered as a turnkey project. In order to quickly develop two new drivers and connect two additional machine types, Ranial Systems integrated the majority of machine types to the edge controllers using out-of-the-box connectors. The system was integrated with a cloud for the monitoring system to perform real-time monitoring, remotely control the assets, and receive real-time intelligent notification and status reporting. Proactive automation has enabled customers to visualize the data and gain AI-powered actionable insights in a timely fashion.
The Cognitive edge computing and AI-based condition monitoring platform were customized to enable a predictive operational strategy. The aim was to improve the performance of the machines using real-time predictive operational intelligence and autonomous controls without any additional high-priced equipment procurement. The target-state solution should minimize manual supervision, early detect the anomalies and improve energy efficiency.
3
Solution
Ranial Systems collected historical data from the existing machines and fed those inputs to the cognitive algorithm for the purpose of training the AI agent. It correlated the various machine parameters to perform real-time monitoring.
Upon testing the model performance, the models were deployed on the edge (site controller). The security requirements, specifications of the edge devices, and maintenance strategy were mutually discussed and agreed upon. The deployed model could intercept the inbound measurement streams and incrementally train the models to gain highly accurate predictive and prescriptive insights.
The patented platform, CognitIoT™, has allowed the customer to easily scale the solution with a centralized management platform for bootstrapping, data consolidation, dashboarding, and visualizing all aggregated data in one location. The platform also handled license and firmware management, application deployment, and optimal data storage to maximize ROI.
The implementation has helped the client to achieve responsive condition monitoring, machine optimization, and process improvement for 20+ different CNC machines, with plans to expand the use case to additional plants. The cloud app displays real-time and historical trends to share operating status with all stakeholders on the shop floor and, send email/ SMS notifications in case of detecting any potential risk of a catastrophic event. The early phase of the implementation focuses on intelligent monitoring, and system is customized to improve the health of machines, reduce scrap and improve the utilization rate using AI application services.
The offered CognitIoT™ solution supports multi-protocol integration (Modbus, BACnet, and OPC to communicate with the industrial assets and offers extensible communication/intelligence infrastructure through highly scalable AI-powered edge computing runtime. The interoperability and self-healing of endpoint IoT systems ensure resilient and low-latency interactions with CNC Control systems. The cognitive edge runtime intercepts the sensory feeds to gain situational awareness and facilitate requisite control operations. The flexibility in integration with legacy control system infrastructure allowed Ranial systems to leverage such intelligence at the point of action with minimal hardware and software upgrades in the existing control systems.
4
Result
CognitIoT™ could integrate with multiple CNC control systems in a protocol-agnostic way and offer real time monitoring and site management functions through a centralized web-based console, while the edge controller performs the autonomous operations at the plant. Moreover, the multi-channel alerts based on intuitive monitoring and diagnostics were delivered to the stakeholders in a timely fashion.
The solution allowed the customer to gain 3X operational efficiency by introducing an automated condition monitoring and predictive maintenance services. The responsive alerts and autonomous controlling measures have helped customers in achieving a 33% reduction in operating costs.
The solution has enhanced real-time quality control measures. It aimed to keep an eye on the process parameters infused by the connected machinery, and tools and, thereby improve KPIs. The unique implementation of our Edge-native CognitIoT™ platform in manufacturing has ensured significant cost reductions and raised life-time value of the assets across the value chain.