Enhancing PCBA Using AI Techniques: Tiny ML, Computer Vision, and Reinforcement Learning for Precision Assembly and Repair

The rapid growth in electronics manufacturing has created an increasing demand for its basic unit, the PCB. To increase the production of PCB, many techniques were adopted, leading to advancements in technologies like Printed Circuit Board Assembly (PCBA). The main challenges faced during PCBA are the misplacement of Surface Mounted Devices (SMDs), and cutting errors.This leads to wastage of huge number of PCBs, producing a lot of E-waste causing the company lose money. Also, traditional Automated Optical Inspection (AOI) and machine vision systems often rely on centralized computing, leading to high operational costs and latency. This paper proposes an AIdriven autonomous PCBA system that integrates TinyML, deep learning-based defect detection, reinforcement learning based robotic repair, and other Computer Vision algorithms to identify faults and solve the problem. This approach aims to solve the issues related to misplacements, and prevention of usage of the defected PCBs in PCBA. The model helps in real-time component verification before placement, AI-guided SMD placement correction, predictive maintenance and anomaly detection for pick-and-place robots, and error prevention by indicating what error the system has encountered and it also suggests the next steps to be done and by which methods. These help in preventing the usage of defected PCBs, identifying the faults in placement of SMDs, and robotic failures before they impact production. By embedding optimized machine learning models directly into pick-and-place robots and PCBA systems, this approach eliminates the need for clouddependent processing. The proposed system eliminates scrape rates and increases placement accuracy. These advancements can benefit other sectors that rely on accurate electronic components, such as aerospace, automobile, and medical industry.