Issue link: https://iconnect007.uberflip.com/i/1534953
88 SMT007 MAGAZINE I MAY 2025 these limitations by incorporating depth per- ception (3D vs. 2D), allowing for more pre- cise inspection of solder joints and compo- nent placement. e ability to analyze height and volumetric information has made defect detection more accurate. e downside, however, is the upfront investment in programming these machines. Correctly programming an AOI requires sig- nificant human input, with engineers manu- ally defining inspection parameters, thresh- olds, and pass/fail criteria. is process is not only time-consuming but prone to errors and inconsistencies. For contract manu- facturing companies, this laborious invest- ment is compounded by the vast number of assemblies and components that could come through the door at any given time. AI-powered Programming: A Game-changer AI in 3D AOI systems has fundamentally changed how these machines operate. Instead of relying solely on pre-set rules and operator programming, AI-driven programming uses machine learning (ML) and deep learning algorithms to continuously improve inspec- tion capabilities. Here are some key ways AI is revolutionizing 3D AOI programming: • Automated parameter adjustments: AI eliminates the need for tedious manual configuration by learning from vast datas- ets of PCB images and inspection results. Instead of requiring engineers to fine- tune inspection parameters, AI can auto- matically adjust settings based on histori- cal data, significantly reducing setup times and human error. • Enhanced defect recognition: Traditional AOI systems can struggle to distinguish between acceptable variations and actual defects. AI-powered systems use deep learning models trained on thousands (or millions) of PCB images, allowing them to identify defects with higher precision while minimizing false calls. is is partic- ularly important when focusing on compo- nent text, which is vulnerable to legibility issues, contamination, and font variations. Using AI in programming enhances text detection by analyzing many text images with different components and fonts, thus better handling text variations. • Reduction in skilled labor dependence: Traditional AOI programming requires skilled engineers to create and maintain inspection data. Programming can take hours depending on the complexity and the number of components on a given product. AI-driven AOI systems lower the dependency on skilled labor and reduce the time to create a program, oen from hours to minutes. • Adaptive learning and continuous improvement: Unlike conventional AOI machines that require frequent man- ual updates, AI-enabled systems contin- uously refine their inspection processes. As they analyze more data, their accuracy improves, making them more adept at rec- ognizing new defect types and adapting to changes in production environments.