Automated Material Inspection via Light Pattern Distortion Analysis
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Solution Overview
Problem
Current methods for material surface quality inspection, particularly in the automotive industry, rely on subjective human evaluation and are inefficient in detecting subtle defects like waviness and undulations, especially in reflective materials, as they focus on height variations rather than curvature changes, which limits precision and requires complex optical setups.
Innovation Solution
The implementation of automated systems that project a defined light pattern onto the material, capture the reflected or refracted image, and use artificial intelligence to identify and classify defects by analyzing distortions in the image, enabling objective, reproducible, and rapid inspection of both real and virtual material surfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated systems project light patterns and capture reflected images for defect detection, then measurement precision and objectivity improve, but device complexity increases
Solution Approach 1:
The patent creates a virtual material surface that is a digital copy of the real material surface, allowing inspection of the virtual copy instead of requiring complex optical setups for the real surface. This copying approach enables precise defect detection through computational methods while simplifying the physical inspection system.
Solution Approach 2:
The patent replaces complex mechanical/optical inspection systems with a computational approach. Instead of using elaborate optical setups to physically measure surface defects, the system uses image processing and curvature analysis of captured images to detect defects, substituting mechanical measurement with computational analysis.
2Ease of operation
If human inspectors perform visual evaluation of material surfaces, then ease of operation is maintained, but productivity and measurement precision deteriorate
Solution Approach 1:
The system enables automated self-inspection of material surfaces through computational analysis. The defect detection system automatically captures images, processes them, and identifies defects without requiring human inspectors, making the inspection process self-sufficient and dramatically improving productivity while maintaining ease of operation.
3Manufacturing precision
If focus is placed on height variation measurement, then manufacturing precision is improved, but measurement precision for subtle defects deteriorates
Solution Approach 1:
The patent changes the measurement parameter from height variation to curvature variation. Instead of measuring how much the surface height deviates from a reference plane, the system measures how the curvature of the surface changes, which is much more sensitive to subtle defects like waviness and undulations that have minimal height variation but significant curvature changes.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the objectivity and speed of quality inspection, improves defect detection sensitivity, and allows for early correction of issues during the design stage, reducing manufacturing costs and improving material quality.
Implementation Method 1
capturing the image reflected or refracted by the material
Implementation Method 2
capturing the image reflected or refracted by the material
Data Source
AI summary
The present document describes methods and systems for the automatic inspection of material quality. A set of lights with a geometric pattern is cast on a material to be analyzed. Depending on the material being inspected, same may act as a mirror and the reflected image is captured by a capture device, or the light passes through the material being inspected and the image is captured by a capture device. Defects in the material can be detected by the distortion caused by same in the pattern of the reflected image or passing through. Finally, software is used to identify and locate these distortions, and consequently the defects in the material. This classification of defects is carried out using artificial intelligence techniques.