Yarn-Out State Detection with Image and Laser Feature Fusion
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Solution Overview
Problem
The challenge in the chemical fiber industry is accurately detecting the state of extremely thin and fast-moving yarns produced by a melt spinning box, as traditional visual inspection is inadequate, leading to inefficiencies and potential yarn breakages due to delayed detection of abnormalities.
Innovation Solution
A yarn-out state detection method combining image processing and laser scanning technologies, utilizing a yarn-out state detection model trained with machine learning to analyze image and laser features for precise yarn state assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional visual inspection is used to detect yarn state, then the detection method is simple, but the detection accuracy is insufficient due to the extremely thin and fast-moving nature of the yarns
Solution Approach 1:
The patent combines image processing technology and laser scanning technology into a unified detection system. The image processing component captures visual information of the yarn path, while the laser scanning component provides precise positional and dimensional data. By merging these two different detection approaches, the system achieves high detection accuracy for extremely thin and fast-moving yarns without relying on a single complex device
Solution Approach 2:
The patent introduces a yarn-out state detection model as an intermediary that processes data from both image processing and laser scanning. This model integrates the information from multiple sources and makes the final determination about yarn state, allowing the system to maintain high accuracy while managing complexity through a structured intermediate processing layer
2Reliability
If shoveling and cleaning the spinneret plate is done only after abnormal situations occur or at fixed periods, then the operation is simple, but yarn quality cannot be ensured continuously
Solution Approach 1:
The patent implements a real-time feedback detection system that continuously monitors the yarn path and provides immediate information about yarn state abnormalities. This feedback mechanism allows the system to detect issues as they occur rather than relying on periodic checks, ensuring continuous yarn quality while maintaining spinning efficiency through timely intervention
Solution Approach 2:
The detection system performs preliminary detection of potential yarn quality issues before they develop into serious problems. By identifying early signs of abnormalities in the yarn path, the system can alert operators to take preventive actions, ensuring yarn quality consistency while avoiding unnecessary production interruptions
3Measurement precision
If the detection system uses only image processing, then the device complexity is lower, but the detection precision is insufficient for extremely thin yarns
Solution Approach 1:
The patent applies the concept of composite detection technologies by combining image processing and laser scanning methods. Just as composite materials combine different materials to achieve superior properties, this system combines different detection technologies to achieve detection precision that neither method could achieve alone for extremely thin yarns
Solution Approach 2:
The patent adds another dimension to the detection system by incorporating laser scanning technology that provides precise spatial measurements. While image processing provides two-dimensional visual information, the laser scanning adds precise dimensional and positional data, creating a more comprehensive detection capability for thin yarn structures
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
Enhances the accuracy and efficiency of yarn state detection, allowing for early identification of abnormalities and improving the stability and quality of the spinning process.
Implementation Method 1
a laser scanning device to obtain laser reflection data of the target yarn path
Data Source
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AI summary
The present disclosure provides a yarn-out state detection method and apparatus, a device and a storage medium; and relates to the field of computer, and in particular to the fields of detection technology, artificial intelligence technology and neural network model technology. The method includes: collecting (S210) a first image and laser reflection data of a target yarn path in a spinning box; obtaining (S220) an image feature of the target yarn path according to the first image; obtaining (S230) a laser feature of the target yarn path according to the laser reflection data; and using (S240) a yarn-out state detection model to obtain a yarn-out state of the target yarn path according to the image feature and the laser feature.