With the advancement of Industry 4.0, the need to integrate intelligent technologies into production processes has grown, aiming for greater automation, efficiency, and quality control. Aligned with this context, this project developed a supervisory system for solder quality inspection on printed circuit boards (PCBs) in the surface-mount technology (SMT) process, using artificial intelligence (AI). Currently, inspections performed by AOI (Automated Optical Inspection) machines generate data in multiple formats (HTML, TXT, and PDF), but their collection and analysis are manual. This makes the process slow, error-prone, and poorly integrated, hindering real-time decision-making. To overcome these limitations, an automated system was developed, including a RESTful API for module integration, a relational database for efficient data storage, a graphical interface with dashboards, and AI models for predictive analysis. The AI, based on Convolutional Neural Networks using Keras and TensorFlow, enables precise detection of solder defects, reducing the need for human intervention. The project followed a hybrid methodology, combining Scrum for agile software development and the Waterfall model for the sequential stages of hardware and research. The team was divided into hardware and software groups, with defined tasks for each phase of the schedule. The main phases included planning, SMT process mapping, system module development and integration, functional and performance testing, and user validation. The system was delivered validated, with formal acceptance from the client. The result is an intelligent solution that automates inspection in the SMT process, increases reliability, reduces failures, and promotes digital transformation in line with the principles of Industry 4.0.
Pinheiro et al. (Wed,) studied this question.