Automated Textile Pattern Detection in Composite Reinforcements
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Solution Overview
Problem
Current methods for identifying textile defects in composite material reinforcements are time-consuming and prone to errors due to reliance on visual inspection, making it difficult to ensure conformity with theoretical geometry and resulting in systematic rejection of parts.
Innovation Solution
An automatic method using an artificial neural network trained on a database of three-dimensional images to detect and reconstruct textile patterns in composite materials, allowing for accurate identification of defects by comparing actual geometry with theoretical models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If visual inspection is used to identify textile defects, then the method is simple to implement, but it is extremely time-consuming and prone to errors
Solution Approach 1:
The patent replaces the manual visual inspection system with an automated image processing system using computers and image analysis software. The mechanical/optical inspection process is substituted with digital image acquisition and automated pattern recognition algorithms, eliminating the need for human inspectors to visually examine each yarn individually, thus dramatically reducing inspection time while maintaining accuracy.
Solution Approach 2:
The patent creates digital copies (images) of the textile reinforcement structure and analyzes these copies rather than physically inspecting the actual textile. By acquiring images of the reinforcement and processing these visual copies through computer algorithms, the system can rapidly identify defects without repeatedly examining the physical material, thereby reducing inspection time and effort.
2Device complexity
If visual inspection is used to identify textile defects, then no additional equipment is needed, but it is prone to errors and cannot reliably distinguish defects
Solution Approach 1:
The patent replaces unreliable human visual inspection with an automated image processing system that uses computer vision and pattern recognition algorithms. This substitution eliminates the variability and error-proneness of human judgment while maintaining relative simplicity through software-based solutions rather than complex hardware systems.
Solution Approach 2:
The patent utilizes image processing techniques that enhance visual contrasts and detect variations in the textile structure through digital analysis. By processing images to highlight differences in patterns, textures, and structural variations, the system can reliably distinguish defects from normal textile features, improving detection accuracy without requiring complex physical inspection equipment.
3Measurement precision
If automated image processing is used to detect textile patterns, then detection accuracy is improved, but the system complexity increases
Solution Approach 1:
The patent creates digital images of the textile reinforcement and processes these copies using image analysis software. By working with digital copies rather than directly analyzing the physical textile structure, the system achieves high measurement precision through software algorithms while keeping the overall system relatively simple, as the complexity is confined to software rather than requiring complex hardware infrastructure.
4Measurement precision
If manual inspection of thousands of yarns is performed, then detailed examination is possible, but the operation becomes extremely time-consuming
Solution Approach 1:
The patent digitizes the textile structure by acquiring images that capture the arrangement of thousands of yarns. By processing these digital copies through automated image analysis, the system can examine the entire textile structure in detail much faster than manual inspection, as the computer can rapidly analyze patterns and detect defects across the entire image without the time constraints of human visual examination.
Solution Approach 2:
The patent replaces the manual process of individually examining thousands of yarns with an automated image processing system. The mechanical/optical inspection is substituted with digital image acquisition and computer-based pattern recognition, enabling detailed examination of the entire textile structure to be completed automatically and rapidly, thus dramatically improving inspection speed while maintaining thoroughness.
Data Source
AI summary
A method for automatically searching for at least one given textile pattern in a composite material reinforcement including a plurality of textile patterns, each textile pattern including a plurality of reinforcing yarns arranged according to a textile topology, the method including acquiring a three-dimensional image of the composite material reinforcement, and searching for the given textile pattern in the acquired three-dimensional image, using an artificial neural network trained on a training database to detect the given textile pattern in a three-dimensional image of a composite material reinforcement.