ML Object Detection for Automated QA Inspection Changeover
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Solution Overview
Problem
Advanced quality assurance in manufacturing and supply chain processes is hindered by the complexity of implementing automated camera-based inspection systems, particularly due to high throughput, frequent part changes, and increased demands, which require more efficient and user-friendly solutions for hardware and software configurations.
Innovation Solution
A machine learning-based approach that generates image representations for objects passing in front of inspection camera modules, allowing for the identification of object types and subsequent selection of appropriate image analysis tools, reducing the need for hardware triggers and enabling software-controlled focus and camera module switching, thereby simplifying the setup and operation of camera-based quality assurance systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated camera-based inspection systems are implemented to ensure quality assurance, then inspection accuracy and reliability are improved, but system complexity and difficulty of configuration increase
Solution Approach 1:
The patent replaces manual hardware configuration and mechanical setup with software-based automated inspection routines. The system uses software-controlled camera modules that can be configured through inspection routines rather than physical hardware adjustments, reducing the complexity of setup while maintaining inspection reliability.
Solution Approach 2:
The system enables automated object classification and inspection routine selection without requiring manual intervention. The machine learning model automatically identifies object types and selects appropriate inspection routines, making the system self-configuring and reducing the burden on operators while ensuring consistent quality assurance.
2Productivity
If high-speed automation systems are used to increase throughput, then productivity is improved, but the ability to perform quality assurance inspection deteriorates
Solution Approach 1:
The patent implements continuous quality assurance inspection that operates alongside high-speed production without interruption. The automated inspection system runs continuously as objects pass through, ensuring that quality checks are performed on every item regardless of production speed, maintaining reliability while supporting high throughput.
Solution Approach 2:
The system replaces slow manual inspection processes with automated computer vision technology that can analyze objects at high speeds. This substitution enables quality assurance to keep pace with high-speed automation systems, maintaining inspection capability even as throughput increases.
3Measurement precision
If multiple image analysis inspection tools are used to comprehensively analyze objects, then inspection thoroughness is improved, but computing resource consumption and processing time increase
Solution Approach 1:
The patent segments the inspection process into two stages: first, a machine learning model performs rapid object classification to identify object types; second, only relevant inspection tools specific to each object type are applied. This segmentation avoids running all possible inspection tools on every object, reducing computing resource consumption while maintaining comprehensive analysis where needed.
Solution Approach 2:
The system applies different levels of inspection thoroughness based on object type. Common objects receive standard inspection routines, while rare or complex objects receive more comprehensive analysis. This local quality approach ensures measurement precision is maintained for critical cases while reducing overall computing resource consumption across the entire inspection stream.
Data Source
AI summary
Data is received that includes a feed of images of a plurality of objects passing in front of an inspection camera module forming part of a quality assurance inspection system. Thereafter, a machine learning model is used to generate a representation of each image. These representations are analyzed to determine a type of object captured in the corresponding image. This analysis can be provided to a consuming application or process for quality assurance analysis.