Laser Machining Defect Detection via Dynamic Field Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current laser-machining processes face challenges in detecting defects in arbitrarily shaped weld seams and cutting gaps due to the complexity and computational intensity of evaluating radiation across the entire detection field, especially when the machining head and scanner optics move relative to the workpieces, leading to time-consuming and inefficient defect detection.
Innovation Solution
A method and device that selectively evaluate radiation in predefined detection field sections based on control data and actual-position data of the laser beam, synchronizing the detection field with the focal spot of the moving laser beam, allowing for real-time detection of defects without interrupting the machining process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire detection field is evaluated to ensure defect detection in arbitrarily shaped weld seams, then detection reliability is improved, but computational complexity and processing time increase significantly
Solution Approach 1:
The detection field is segmented into multiple sub-regions based on the arbitrary weld seam path. Instead of evaluating the entire detection field uniformly, the system divides it into relevant sections where defects are likely to occur, reducing computational complexity while maintaining detection reliability in critical areas.
Solution Approach 2:
Different evaluation strategies are applied to different regions of the detection field. High-resolution evaluation is focused on areas along the weld seam path where defects are most likely, while other regions receive reduced or no evaluation, optimizing the balance between reliability and computational load.
2Measurement precision
If the entire detection field is evaluated in real-time for defect detection, then detection accuracy is improved, but processing speed deteriorates
Solution Approach 1:
The detection field is divided into sub-regions along the weld path, allowing parallel processing of smaller image segments. This segmentation enables real-time evaluation with reduced computational burden per segment while maintaining overall detection accuracy through comprehensive coverage of critical areas.
Solution Approach 2:
The system performs partial evaluation by focusing computational resources on the most critical regions along the weld seam path rather than uniformly evaluating the entire detection field. This selective approach maintains sufficient detection accuracy while achieving real-time processing speeds.
3Reliability
If the detection system continuously monitors the entire detection field, then defect detection capability is improved, but data processing volume increases
Solution Approach 1:
The system extracts and isolates only the relevant portions of the detection field that correspond to the weld seam path and immediate surrounding areas. By extracting only the necessary data regions and excluding irrelevant areas from evaluation, the system reduces data volume while maintaining defect detection capability in critical zones.
Solution Approach 2:
The continuous detection field data is segmented into discrete regions of interest along the weld path. This segmentation allows the system to process only the necessary data portions, reducing overall data volume while ensuring comprehensive defect detection coverage in areas where defects are most likely to occur.
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 enables fast, simple, and reliable detection of defects in laser-machining processes by minimizing the data to be evaluated and allowing for continuous monitoring, thereby improving manufacturing efficiency and enabling closed-loop control of the welding process.
Implementation Method 1
detecting, in a two-dimensionally spatially resolved manner, radiation emitted by the workpiece
Data Source
AI summary
Detection of defects during a machining process includes: moving a laser beam along a predefined path over multiple workpieces to be machined so as to generate a weld seam or a cutting gap in the workpieces; detecting, in a two-dimensional spatially resolved detector field of a detector, radiation emitted and/or reflected by the multiple workpieces; selecting at least one detection field section in the detection field of the detector based on laser beam control data defining movement of the laser beam along the predefined path or based on a previously determined actual-position data of the laser beam along the predefined path, wherein each detection field section comprises a region encompassing less than the entire detection field; evaluating the radiation detected in the selected detection field section; and determining whether a defect exists at the weld seam or the cutting gap based on the evaluated radiation.