PCB Defect Inspection
Objective
Printed Circuit Boards (PCBs) are the backbone of technological advancement in industries such as automotive, medical, and agriculture. The growing integration of technology into business and consumer markets has introduced challenges such as increased complexity, rising material and workforce costs, and environmental concerns. Additionally, as PCB technology advances, components are becoming increasingly smaller, making manual inspection more challenging and less effective. In this case study, Neatco Engineering explored the application of AI to address these challenges by demonstrating its transformative impact on quality assurance, production efficiency, and defect detection in PCB manufacturing. By reducing waste at the source through precise defect detection and process optimization, this study contributes to advancing the circular economy and promoting sustainability alongside enhanced manufacturing outcomes.
Solution
Neatco’s engineering team leveraged advanced computer vision techniques to design and train a neural network capable of detecting electrical components on PCBs and then compare them to a labelled golden data set for defects such as incorrect, misalignment, or even missing components before the reflow process to secure the components to the PCB. A Graphical User Interface (GUI) with real-time feedback was created to keep track of the workflow and display results.
Status
The AI-powered PCB defect detection system is currently in the refinement and integration phase of the project. Early results have been highly encouraging, with the neural network successfully detecting a wide range of component defects a variety of aluminium PCBs.
Future
Neatco is actively working to secure partnerships for continued development and implementation. We aim to include systematic improvements and changes comprehensive to our client's needs.
