Tackling Spillage in SMT Placement Machines with Factory Insights

When you want to get more through on your SMT lines, you'll often want to first make sure your placement machines are running smoothly. These machines are the backbone of the assembly process, so keeping a close eye on them is key to eliminating waste and ensuring a smoother running line. This guide walks you through how to tackle spillage problems using Factory Insights, Cogiscan’s analytics platform designed specifically for electronics manufacturers.

Kick off your efficiency initiative by diving into the "Placement Summary by Production Line" chart located within the Placement Summary tab of Factory Insights. Focusing your attention on the metrics for mispicks and the components that weren't successfully placed for each line, (in the above example) we're seeing a spillage rate of approximately 15% of the placed components, which definitely warrants a deeper dive. Despite this, an initial comparison doesn't reveal one line outperforming the other, indicating the necessity to further dissect the data to uncover the underlying issues.

In order to drill deeper, accessing the Machine tab provides a more granular view of the issue at hand. From there, you’ll be able to review the "Top Mispick - by machine" data to check if any specific pick and place machine is underperforming.

If no machine stands out, try to identify feeder issues by examining the "Top Mispick - by feeder" section. In our case, “Feeder_1” has a significantly higher rate of mispicks. Easily share this data/chart via email with a colleague or specialist who can further investigate this feeder's performance. However, this is just the tip of the iceberg, we can still push the analysis deeper.

Diving deeper into the "Not placed" information, you’ll can investigate the "Top Not Placed - by machine" to see if a particular machine is responsible for dropping more parts than others.

In this example, P&P1 is clearly dropping more parts that the other placement machines. Therefore, it requires further dissection by evaluating data by both "Head Id" and "Nozzle Position”. Again, we note that “Head #3” and “position #3” have more drops, which could point to specific hardware issues requiring attention (calibration, maintenance). Again, this chart is easily shareable via email to the team responsible for managing these issues.

Identifying the root causes of spillage and waste is a significant step forward; yet, given the higher incidence of dropped parts on “Line Demo 1”, it’s also advisable to establish a dedicated dashboard for this specific line. This will enable operators to meticulously track and oversee instances of mispicks and not-placed components, ensuring immediate attention to these critical issues. Factory Insights supports customization of all dashboards, including underlying data calculation models, for users to fully control and personalize their usage of this analytics platform.

In summary, by leveraging the data analysis and visualization power within Factory Insights, we've tackled the placement spillage problem head-on. We thoroughly analyzed the SMT line data, honed in on trouble spots like Feeder_1 and P&P1, and shared our findings with those who can make the necessary adjustments. Most importantly, we've set up a custom dashboard for Line Demo 1, giving our operators the upper hand in monitoring and quickly addressing any mispicks or placement issues. This hands-on approach promises a tighter production process, less waste, and a smoother factory ecosystem. A data-centric approach to factory management is key to keeping your production efficient.