AI PCB Inspection Microservices for Automated Defect Diagnosis
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Solution Overview
Problem
AOI systems require human intervention for defect detection and decision-making, leading to inefficiencies and potential errors, and are limited to manufacturing contexts, lacking post-deployment defect diagnosis and remediation capabilities.
Innovation Solution
AI-based PCB inspection systems using machine learning models and microservices for automated defect detection, classification, and parameter adjustment to enhance efficiency and accuracy, providing context and troubleshooting capabilities beyond manufacturing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human operators are used to monitor and make decisions in AOI inspection processes, then judgment accuracy for complex defects may be maintained, but inspection efficiency decreases and human error risks increase
Solution Approach 1:
The system enables self-service automation where the AI model independently performs defect detection, classification, and decision-making without human intervention. The machine learning model analyzes PCB images, identifies defects, and generates inspection results autonomously, eliminating the need for human operators to manually monitor each inspection while maintaining high accuracy through advanced algorithms
Solution Approach 2:
The patent replaces the mechanical human decision-making process with an AI-based system. The human operator's visual inspection and judgment functions are substituted by computer vision algorithms and machine learning models that process inspection images, detect anomalies, and make defect determination decisions automatically, thereby increasing efficiency while maintaining or improving accuracy
2Reliability
If AOI systems are used during manufacturing, then quality control is improved, but post-deployment defect diagnosis capability is lost
Solution Approach 1:
The AI-based inspection system is designed with multi-functionality to serve both manufacturing quality control and post-deployment defect diagnosis. The same machine learning model can analyze PCB images from manufacturing processes and also diagnose defects in deployed PCBs by processing images captured in field conditions, making the system universally applicable across different operational phases
Solution Approach 2:
The system performs preliminary defect detection during manufacturing to ensure quality control, and the same capability is later applied to diagnose defects after deployment. By establishing the AI model's defect detection capability during manufacturing, the system prepares for future post-deployment diagnostic tasks, enabling seamless transition from production quality control to field maintenance
3Productivity
If AI-based automated defect detection is implemented, then inspection efficiency and accuracy are improved, but system complexity increases
Solution Approach 1:
The AI-based inspection system is segmented into distinct functional modules: image acquisition module, pre-processing module, defect detection module using machine learning models, classification module, and result generation module. This segmentation allows each component to be optimized independently and facilitates easier maintenance and deployment, reducing overall system complexity while maintaining high inspection efficiency
Data Source
AI summary
In various examples, a microservices-based architecture may be configured to detect, analyze, and/or reduce instances of PCB defects using AI-based models and other algorithms. For instance, image data representing an image of a PCB may be applied to a model running within a first service associated with an application. The model may process the image data to generate one or more predictions relating to defects associated with the PCB. In some instances, one or more second services associated with the application may use the predictions to generate visualizations and/or compute metrics corresponding to the defects associated with the PCB. Additionally, in some examples, the predictions, the visualizations, and/or the metrics may be used to update one or more parameters associated with one or more systems to, for instance, improve the performance of AOI systems, SMT systems, or any other systems.