Excerpt—The Printed Circuit Assembler’s Guide to... SMT Inspection: Today, Tomorrow, and Beyond, Chapter 3

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Chapter 3: The Data Dilemma
Data-driven analytics, using inspection data, can be used to overcome production challenges and to enable the smarter factory of today and of the future. Industry 4.0 and its associated benefits will doubtlessly advance the industry, so equipment suppliers need to continue to work diligently to accelerate M2M communication standards to aid the use of machine data to accelerate this trend.

To this end, the role of inspection becomes two-fold. The first is to ascertain if boards are good or bad at the point of inspection. The second is to provide data for the greater good of the process, the line and—ultimately—the factory.

Connectivity is the Foundation of the Smart Factory
Initiatives like the IPC Connected Factory Exchange (CFX) and IPC-Hermes-9852 underpin efforts within the industry to develop standards and help create a smart factory. These M2M communication standards, guided in part by Industry 4.0, are altering the manufacturing process by improving metrics such as first pass yield and throughput by applying autonomous process adjustments.

Far beyond an automatic line changeover, this two-way communication allows equipment to automatically adjust production parameters to increase board quality and lower costs by eliminating manual labor, rework, and scrap. As part of this mission, advanced process control with interconnected PCBA equipment will revolutionize process optimization and lay the foundation for the smart factory.

Measurement-based Inspection
Every electronics manufacturer—and most equipment suppliers, including automated inspection providers—is looking to optimize the assembly process. However, this is difficult, or even impossible, with the limitations of 2D imaging, the former industry standard. Not only is it difficult for 2D AOI systems to identify defects on curved and reflective solder joints, but 2D AOI systems simply do not generate data that is reliable enough to actually deliver consistent results.

Every aspect of the 2D inspection process relies on contrast—not quantitative measurement. As such, 2D users must either suggest the repair or scrapping of defective boards, which increases costs and eliminates the potential for process improvements.

Finding the Component Body
The introduction of 3D imaging to the inspection market solved many of these problems. By measuring components and solder joints, and then offering critical height information to the inspection algorithms, users could identify errors such as pad overhang and insufficient solder.

The 3D data is only valid when 3D technology is used for all component types to extract the exact body dimensions. Systems that use “blob detection,” which may be susceptible to external factors such as board warpage and component proximity, are less reliable. The combination of measurement and process data piles collected from its SPI and AOI systems, as well as from printers and mounters, allows delivery of advanced AI features with reliable “big data.”

To overcome this, true 3D technology must extract the component body data.

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