Laser Cut Edge Image Evaluation for Precise Quality Inspection
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Solution Overview
Problem
Current methods for evaluating laser cut edges in metal cutting, such as laser beam cutting, often rely on manual inspection or expensive measurement systems, and struggle to consistently achieve high quality due to factors like striation, burring, and varying material states, which can be influenced by parameters like focus position, feed speed, and gas flow.
Innovation Solution
A method involving image capture and processing using a camera to segment and evaluate the laser cut edge, employing neural networks and algorithms like spatial pyramid pooling and encoder-decoder networks for segmentation, and frequency domain analysis for sharpness detection, providing user feedback on parameters like contrast, illumination, and noise to optimize image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection or expensive measurement sensor systems are used to evaluate laser cut edges, then measurement precision can be achieved, but device complexity and cost increase significantly
Solution Approach 1:
The patent uses a camera to capture optical images of the laser cut edge, creating a visual copy of the physical feature. This optical copy is then processed through image analysis algorithms to evaluate cut quality parameters such as striation, burring, and overall edge quality, replacing the need for complex physical measurement sensors while maintaining evaluation accuracy
Solution Approach 2:
The patent replaces mechanical measurement sensor systems with an optical-based image capture and processing system. By using a camera to capture images and applying computational image analysis techniques, the system substitutes complex mechanical measurement infrastructure with a more accessible optical approach, reducing device complexity while preserving measurement capability
2Device complexity
If image capture is used to evaluate laser cut edges, then device complexity is reduced, but image quality (contrast, illumination, noise) may be insufficient for accurate evaluation
Solution Approach 1:
The patent implements a feedback mechanism where image quality parameters (contrast, illumination, noise) are analyzed and used to provide feedback for optimizing the image capture process. This feedback loop enables continuous improvement of image quality by adjusting capture parameters, ensuring that the simplified optical system produces sufficient image quality for accurate cut edge evaluation
Solution Approach 2:
The patent employs parameter optimization techniques to adjust image capture settings such as exposure time, gain, resolution, and focal length. By dynamically changing these parameters based on the specific evaluation requirements and lighting conditions, the system maximizes image quality within the constraints of the simplified camera-based apparatus
3Measurement precision
If comprehensive image processing and segmentation algorithms are applied, then measurement precision improves, but loss of time increases due to complex processing
Solution Approach 1:
The patent applies image segmentation to divide the captured image into distinct regions corresponding to different features of the laser cut edge (e.g., cut surface, burring areas, striation zones). This segmentation allows the system to focus computational resources on analyzing only the relevant regions rather than processing the entire image, thereby maintaining measurement precision while reducing overall processing time
Solution Approach 2:
The patent implements a multi-level analysis approach where basic cut quality parameters are detected using simplified algorithms for rapid assessment, while more detailed analysis is applied only when necessary. This partial application of complex processing ensures that most evaluations are completed quickly, with enhanced analysis reserved for cases requiring higher precision, thus balancing measurement accuracy with processing efficiency
Data Source
AI summary
A method for evaluating a laser cut edge of a workpiece includes capturing image data of the laser cut edge and its surroundings, segmenting the image data, and identifying a segment of interest of the image data. The segment of interest comprises image data of the laser cut edge. The method further includes carrying out an image quality detection for the segment of interest and generating, based on the image quality detection, an output for a user.