AI Yarn Inspection System for Defect Classification
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Solution Overview
Problem
The existing methods for inspecting yarn packages are costly and inefficient, leading to imperfections in textile products.
Innovation Solution
An image-based classification system utilizing artificial intelligence is implemented to recognize package imperfections, which includes an imager, a transporter, a sorter, and a controller configured to use an artificial engine classifier for sorting or adjusting manufacturing parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual inspection methods are used to inspect yarn packages, then inspection can be performed, but the cost is excessive and efficiency is low
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated optical inspection system that uses cameras and image processing algorithms to detect yarn package imperfections, thereby eliminating labor-intensive manual inspection while maintaining high accuracy
Solution Approach 2:
The system enables self-inspection by equipping the yarn packaging machine with integrated imaging devices and automated defect detection software, allowing the machine to automatically identify and flag imperfections without external manual intervention
2Productivity
If automated optical inspection systems are implemented, then inspection efficiency improves, but system complexity increases
Solution Approach 1:
The patent designs a multi-functional inspection system that can detect multiple types of imperfections (surface defects, winding irregularities, package shape issues) using a single integrated platform, reducing the need for multiple separate inspection devices
Solution Approach 2:
The system uses image processing software and algorithms as intermediaries to bridge the gap between raw optical data and actionable inspection results, automatically processing images to identify defects without requiring complex manual analysis procedures
3Manufacturing precision
If comprehensive imperfection detection is performed, then quality control improves, but inspection time increases
Solution Approach 1:
The patent implements continuous inspection during the yarn packaging process itself, with cameras capturing images of packages as they are being formed on the rotating bobbin, allowing defect detection without interrupting production flow
Solution Approach 2:
The system performs preliminary defect detection during the packaging process before packages are fully completed and removed from the machine, allowing early identification of imperfections and immediate corrective action while the production line is still running
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
The system provides a cost-effective and efficient means to identify and classify imperfections in yarn packages, enabling improved quality control and reduced production costs.
Implementation Method 1
The imager has at least one optical detector and an optical emitter... configured to generate an optical image for a textile package
Data Source
AI summary
A textile package production system includes an imager, a transporter, a sorter, and a controller. The imager is configured to generate an optical image for a textile package. The imager has at least one optical detector and an optical emitter. The imager has an inspection region. The transporter has a test subject carrier configured for relative movement as to the carrier and the inspection region. The sorter is coupled to the transporter and is configured to make a selection as to a first classification and a second classification. The controller has a processor and a memory. The controller is coupled to the imager, the transporter, and the sorter. The controller is configured to implement an artificial engine classifier in which the sorter is controlled based on the optical image and based on instructions and training data in the memory.