SPONSORED: AI IN VISUAL INSPECTION


consumer goods, food and beverage, print and packaging and manufactured parts (for example, for automotive assemblies). Electronics assembly lines present a particularly big opportunity for AI assistance. ‘One of the areas where AI visual inspection is used the most is for incoming, in-process and final inspection for electronics manufacturing,’ revealed Goffin. ‘In particular, the system is used for higher value, lower volume products not well- suited to full automation, or to help catch errors commonly missed by automated optical inspection.’


Efficient integration Te latest advances in no-code algorithm development and edge processing platforms have made it more feasible for manufacturers of products like those detailed above to incorporate AI decision- support. ‘Pre-packaged and easily trained plug-in applications around common use cases mean manufacturers can get up and running quickly,’ said Goffin. Te added advantage is that it helps to


overcome some of the other perceived challenges of incorporating AI into manufacturing processes, in terms of time and expense. Goffin continued: ‘Typically when manufacturers think about AI, there’s a perception that it takes a lot of images and training time, or external expertise


is required. Tere’s also terminology that comes with the technology which can be confusing. So it could be a little bit daunting – especially for a smaller manufacturer who is reading about the advantages of AI, but then trying to understand how to deploy it. Some companies see the advantages of AI, but they also have proven processes that they don’t want to risk. However, today’s systems are designed to be user friendly. So even with just one good image, a manufacturer can start incorporating AI into its existing processes.’ One company that has taken advantage


of the benefits of such a system is electronic component manufacturer DICA, which manufactures high-quality electronic assembly services for a small-to-medium volume market. Not all of the company’s products are suited to automation; human inspection is still very important, ensuring that the right components are being used in the right places and that nothing has changed or become defective since the previous step. DICA has adopted the image compare plug-in from Pleora Technologies to incorporate AI into its visual inspection process. Te tool is a visual application that is ‘trained’ to make errors obvious to a human inspector. All that is needed is a single image of a known good product to which all future products can be compared.


New whitepaper now online


‘Tere’s a perception that it [AI] takes a lot of images and training time, or [needs] external expertise’


Te plug-in’s AI capabilities are used


to match the approved layout and final production – it compares the placement of components on the circuit board, highlighting any differences for the human inspection before moving on to the next step in the manufacturing process or to final packaging. Te plug-in also learns and is continuously trained as the operator accepts or rejects possible detected errors, allowing the system to make more accurate suggestions.


Te plug-in is also used for quality checks on incoming components from suppliers and for batch tracking. With an image save application, the operator captures an image of the final board, the barcode and can add any notes on the product. ‘Visual inspection is often a “data black hole” in manufacturing processes,’ said Goffin. ‘Tis application lets them know a product has left the manufacturing sites in good working order and can speed up the resolution process if there’s an issue in the field.’ O


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IMPROVING VISUAL INSPECTION QUALITY WITH AI


Tis white paper from Pleora Technologies discusses how AI can provide decision-support for visual and manual tasks throughout incoming, in-process and outgoing (final) inspection steps of manufacturing.


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