AI-Driven Process Optimization in Automated Microsoldering for Fine-Pitch PCB Assembly

Abstract

Fine pitch printed circuit board assembly requires highly accurate microsoldering performance to avoid defects such as bridging, voiding, misalignment, and insufficient solder deposition. Traditional rule based inspection approaches lack predictive intelligence and do not provide real time decision capacity for improving solder quality during production. This research investigates an artificial intelligence driven methodology for optimizing microsoldering processes by combining machine vision inspection, predictive stencil printing models, and statistical process control. A consolidated dataset was developed consisting of optical solder joint images, stencil paste measurements, printing parameters, and microsoldering variables. Convolutional neural networks were applied to identify defect types. Recurrent neural network prediction was used to estimate stencil cleaning frequency and support vector regression was implemented to forecast paste deposition behavior. Statistical evaluation showed reductions in bridging occurrence from 12.4 percent to 4.1 percent and misalignment frequency from 9.3 percent to 2.7 percent after AI integration. Inspection recognition improved from 82 percent to 96 percent. The proposed AI supported SPC structure enhances control chart interpretation, automatic defect tagging, and real time process capability monitoring. Findings indicate that AI based hybrid optimization reduces variation in solder paste application, strengthens pattern detection for microscale solder behavior, and improves consistency in fine pitch PCB assembly quality. The study concludes that artificial intelligence offers a reliable path toward improved production stability, lower defect rates, and greater operational efficiency in automated microsoldering systems.