Issue link: https://iconnect007.uberflip.com/i/1504794
AUGUST 2023 I SMT007 MAGAZINE 65 • Production analytics: Many CMs and OEMs use tools to analyze production (throughput, asset utilization, line utiliza- tion) and understand causes of downtime, maximizing production yield while at the same time minimizing waste and human labor due to rework. You come from the inspection side of the electronics manufacturing industry. How has the integration of artificial intelligence been implemented into inspection systems and what benefits have resulted from this integration? AI—specifically machine learning—significantly reduces programming time for inspection systems while improving inspection perfor- mance. We've seen program- ming times for inspection of a new product reduced from hours to days in a traditional AOI (e.g., Expert System) to five to 10 minutes. Inspection performance improved, espe cially for areas that are difficult to address through traditional computer vision, like through-hole components, wires, FOD, and visual cosmetic inspec- tion. When comparing inspection systems without AI to those with AI capabilities, has the integration of AI into inspection systems expanded their capabilities, and if so, what are the added capabilities? Systems with machine learning-based AI capa- bilities are more adaptable, perform better on various inspection tasks, and can provide bet- ter end-to-end analytics. Machine learning- based AI systems are more adaptable than tra- ditional AOIs. ey maintain system perfor- mance as new products come into production and alternative parts are used. For example, because a machine learning system can gener- ally learn the difference between components such as resistors and capacitors, it is better able to handle components from new suppli- ers without additional programming. Further- more, it can learn to adapt and improve its per- formance over time as it sees more data. Many defect types are challenging to inspect with traditional inspection systems. As a result, instead of using such systems, given the variability seen, humans oen inspect foreign object debris and damage and cos- metic defects, such as scratches and dents. A machine learning-based system can handle such variability by learning the subtleties of these challenging defects directly from data. For example, machine learning-based systems can effectively distinguish between a scratch and a cleanable mark on a box build, which would be impossible to define the rules to accomplish reli- ably in practice. Newer AI inspection sys- tems oen generate large amounts of meta-data— descriptive data about an inspection. Examples include the location of the inspection in the produc- tion line, component des- ignators, the board serial number, when the defect happened, etc. e systems can ingest additional data from other places in production, such as ICT, reflow oven, and results from other inspection systems, and use AI to help narrow down why a defect is happening and how to fix it. Many manufacturers are emphasizing process optimization as a method for becoming more competitive. How has artificial intelligence impacted optimization on a manufacturing floor? In high-mix, low-volume manufacturing, fast setup and turnaround time are crucial to increase throughput for EMS companies. How- Many defect types are challenging to inspect with traditional inspection systems.