Client Introduction & Business Requirement
Our client wished to check for solder joint defects during the manufacturing process of their Printed Circuit Board (PCB). By augmenting their SPI defect-level detection, they wanted to improve their panel testing and reduce false positives.
- We developed a predictive real-time analysis component for processing. The SPI parametric data flows from the machine into Analytics edge, and into the Data Processing Gateway, which runs Kafka.
- The IoT Edge device subscribes to the raw data topic, executes an advanced machine learning model and uses the output to display the risk of testing failure on an HMI device, which the operator can view. Additionally the IoT Edge device will send control commands to the manufacturing line.
- A dockerized machine- learning algorithm is deployed on the edge, on premise or in the cloud. The Cloud based ML training continuously improves the capabilities and accuracy of the models. A containerized workload consumes the raw data feed and publishes it to a data lake. The ML Suite enables the turning on and off of the elastic compute infrastructure.
- Reduces soldering defects by a considerable amount.
- Helps improve yield, as well as the print quality, thereby improving the performance of the PCB.