Laser Machining Error Detection Using Image and Height Data
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Solution Overview
Problem
Current laser processing quality assessment and error detection in laser welding and soldering are complex, requiring extensive expertise and time, with post-process inspections being inefficient and prone to errors due to the need for manual parameter adjustment and expert training, especially when switching materials or processes.
Innovation Solution
A system that uses a neural network to process both image and height data from a workpiece surface to automatically detect processing errors, eliminating the need for manual feature extraction and parameterization, allowing for quick adaptation to changing conditions and materials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual feature extraction and parameterization are used for quality assessment, then processing errors can be detected, but the system becomes complex and requires extensive expert knowledge and time
Solution Approach 1:
The patent replaces manual mechanical feature extraction and parameterization processes with an automated neural network system. The neural network automatically learns relevant features from raw image and height data, eliminating the need for expert-manual feature engineering and complex parameter setting, thus reducing system complexity while maintaining detection reliability
Solution Approach 2:
The neural network performs self-learning and automatic feature extraction without requiring external expert intervention. The system automatically adapts to different materials and processes through the learning algorithm, making the system self-sufficient and reducing dependency on expert knowledge for parameterization
2Measurement precision
If expert parameterization is used for quality assessment, then accurate error detection is achieved, but production interruptions occur during parameter adjustment
Solution Approach 1:
The neural network is pre-trained with extensive data before deployment, so that when the system is put into operation, it immediately possesses the capability for accurate quality assessment. This preliminary training action eliminates the need for time-consuming parameter adjustments during production, preventing production interruptions
Solution Approach 2:
The system transitions from fixed manual parameters to dynamic learned parameters. The neural network automatically adjusts its internal parameters based on the specific material and process conditions being analyzed, eliminating the need for manual parameter changes and associated production interruptions
3Ease of manufacture
If traditional image processing with multiple parameters is used, then quality assessment can be performed, but the process becomes time-consuming and requires frequent re-adjustment when materials change
Solution Approach 1:
The system transitions from static manual parameter settings to dynamic adaptive learning. The neural network continuously adapts its parameters based on the specific material and process conditions, making the system both easy to operate and highly adaptable to material changes without requiring manual re-adjustment
Solution Approach 2:
The neural network is designed to handle multiple materials and processing conditions with a single unified system. Through its learning capability, it automatically adapts to different materials and processes, eliminating the need for separate parameterization procedures for each material type and simplifying the overall manufacturing process
Data Source
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AI summary
The invention relates to a system for detecting machining errors for a laser machining system for machining a workpiece. The system comprises: a detection unit for detecting image data and height data of a machined workpiece surface; and a computing unit, wherein the computing unit is designed to generate an input tensor based on the detected image data and height data and to determine an output tensor on the basis of the input tensor using a transfer function, said output tensor containing information on a machining error.