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Page 50


www.us- tech.com


March, 2021


Optimizing Smart Factory Process Control with True 3D Inspection Data


By Joel Scutchfield, Director of Sales, and Brent Fischthal, Senior Manager of Americas Marketing, Koh Young America


play, it can be impossible to judge the quali- ty of a board based on a simple comparison with reference images or even 2D automated inspection. Automated 3D SPI and AOI machines


A


are now an integral part of high-quality elec- tronics production. Data generated from such systems supplies meaningful insights into the process and can help manufacturers to elimi- nate the root cause of defects. This requires that manufacturers trust the data from the system and then use it to monitor, control and transform the PCB assembly process. However, for these systems to make the


leap from inspection to process control, and ultimately, to automated process optimiza- tion, the data must be reliable, repeatable and relatable.


True 3D is Ideal Manufacturers demand more from their


process solutions than ever before. Ideally, the manufacturer should be able to monitor and adapt the process by accessing all the data at any time, from anywhere. Also, this


Koh Young Zenith AOI system. True 3D systems measure every aspect


of the component and solder joint per IPC-A- 610, while generating a significant set of


s board complexity increases and fac- tors like surface finish, PCB condition and component proximity come into


supply of data should be useful enough to continually optimize the process. This has been difficult for 2D, 2.5D and quasi-3D sys- tems that cannot accurately measure shape, coplanarity and solder volume.


measurement data. True 3D makes use of all measurement data, but 2.5D and many 3D systems do not. These systems rely on 2D technology


that is not based on measurements, while incorporating minor 3D capability simply to supply images, and in some cases, basic measured values. Data, especially from inspection and test


equipment, is a key part of the foundation of Industry 4.0 and the smart factory. This means that equipment must evolve from basic “pass/fail” systems to dynamic decision- making systems — emphasizing the need for reliable, repeatable and relatable data. Artificial intelligence (AI) engines can


bring this power to individual machines, then manage process data from connected SPI and both pre- and post-reflow AOI sys- tems. The ideal AI would collect all inspec- tion and measurement data from equipment along the line and send it anywhere in the network to be retrieved from a web interface. Machine-to-machine (M2M) communica-


tions standards are already improving met- rics, such as first-pass yield and overall throughput, by autonomously adjusting


Continued on next page


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