Our client is a leading American Industrial Automation company. Their Pick and Place machines needed to be monitored for nozzle performance. An early warning notification system in case of discrepancy, was to be established.
Our team of experts developed Predictive Machine Learning models based on performance data to determine if the nozzle was getting out of control. They also set up a trigger for early warning notifications, which could then be acted upon by the concerned
An Edge device from the shop floor publishes the data onto a Kafka topic. The Gateway consumes this data from the Kafka and runs the adaptive business, ML logic to decide the performance of the nozzle. The ML Logic is executed on the Data processing Gateway using a container running on Kubernetes.
If the nozzle performance is found to be out of control, a notification is generated and this data is pushed to the cloud for Analytics. The real-time dashboard renders the performance charts of the nozzle.
- Reduced losses due to predictive nozzle performance insights and proactive notifications sent to operator.
- Increased productivity due to reduced downtime and smart scheduled maintenance.
- Technicians have deeper understanding of nozzle performance and maintenance.