Enhancing Defect Detection in Circuit Board Assembly Using AI and Text Analytics for Component Failure Classification

This article investigates the application of text analytics for defect detection and characterization in electronics manufacturing of printed circuit board assembly by analyzing structured and unstructured textual data from circuit board and packaged chip testing. Traditional defect detection methods often overlook the valuable insights found in unstructured textual observations recorded by technicians and engineers during manufacturing processes. This research leverages text analytics to transform these descriptive narratives into structured, actionable data, thereby improving the precision and efficiency of defect identification. A Naïve Bayes model was employed for classification, and natural language processing (NLP) techniques were utilized to extract meaningful patterns from defect descriptions. The results indicate high classification accuracy for components, such as 'capacitor,' 'FPGA,' and 'resistor,' while also identifying challenges in distinguishing 'capacitor' from 'transistor.' The expected outcomes of this research include the enhancement of defect detection precision and efficiency, leading to more effective quality control processes in electronics manufacturing. This study highlights the integration gap in real-time text analytics and demonstrates the potential of machine learning algorithms in manufacturing defect characterization, offering actionable insights for optimizing quality control strategies.