Line Laser 3D Imaging for Weld Gap Measurement
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Solution Overview
Problem
Existing machine vision detection solutions for post-welding inspection are limited by structural design, cost, and application scenarios, resulting in low detection accuracy and efficiency.
Innovation Solution
A machine vision detection method using a line laser to collect three-dimensional images, which are then converted to two-dimensional grayscale images to calculate the gap between components by averaging the length of perpendicular lines between their boundaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional industrial cameras are used for machine vision detection, then detection can be performed, but detection accuracy and detection efficiency remain low due to limitations in structural design, cost control, and matching with actual application scenarios
Solution Approach 1:
The patent transitions from traditional two-dimensional camera imaging to three-dimensional laser scanning imaging. By using a laser line scanner to capture depth information and create 3D point cloud data, the system achieves higher measurement precision for gap detection while maintaining efficient automated detection processes, resolving the contradiction between accuracy and efficiency
Solution Approach 2:
The patent replaces traditional mechanical measurement tools and manual inspection methods with automated laser scanning and image processing systems. The laser line scanner automatically captures component surfaces and welding seams, eliminating the need for manual measurement and significantly improving both detection accuracy and efficiency
2Measurement precision
If manual visual detection with auxiliary tools is used, then comprehensive inspection can be performed, but detection efficiency is low
Solution Approach 1:
The system enables automated self-detection through laser scanning and image processing algorithms. The laser line scanner automatically captures component surfaces, the system autonomously processes the 3D point cloud data to identify welding seams and calculate gap dimensions, eliminating the need for manual intervention and dramatically improving detection efficiency while maintaining comprehensive inspection capabilities
Solution Approach 2:
The laser scanning system performs continuous automated detection without interruption. Unlike manual inspection that requires tool changes and repositioning, the laser line scanner continuously scans component surfaces and welding seams, maintaining uninterrupted detection flow and significantly improving productivity
3Productivity
If three-dimensional imaging with line laser is used, then continuous sampling without pre-calibration is enabled, but system complexity increases
Solution Approach 1:
The patent introduces a calibration plate as an intermediary element that simplifies the 3D imaging system setup. The calibration plate with known geometric features serves as a reference for the laser line scanner, enabling automatic calibration and eliminating complex pre-calibration procedures. This intermediary component makes the system easier to deploy while maintaining continuous sampling capability
Solution Approach 2:
The system changes the imaging parameter from traditional 2D grayscale images to 3D point cloud data with depth information. By capturing Z-axis depth data alongside X-Y position information, the laser line scanner enables direct measurement of gap dimensions without requiring complex calibration procedures, simplifying the system while enabling continuous sampling
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 improves detection accuracy and efficiency by allowing continuous sampling without pre-calibration and effectively eliminating interference from image distortions.
Implementation Method 1
receiving a three-dimensional image from a line laser
Data Source
AI summary
A machine vision detection method includes receiving a three-dimensional image from a line laser, converting the three-dimensional image into a two-dimensional grayscale image, obtaining a boundary of a first component and a boundary of a second component in the two-dimensional grayscale image, determining N perpendicular lines between the boundary of the first component and the boundary of the second component, and calculating an average length of the N perpendicular lines as a gap between the first component and the second component. The three-dimensional image includes at least a portion of the boundary of the first component, at least a portion of the boundary of the second component, and a welding spot located on the boundary of the first or second component. N is a positive integer.