Textile Raw Material Quality Assessment via Optical Imaging
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Solution Overview
Problem
The textile industry faces challenges in efficiently determining the quality attributes of raw materials, particularly for small and medium-sized enterprises, due to the high cost and complexity of existing methods, which can lead to quality issues and misrepresentation of materials to buyers.
Innovation Solution
A system and method utilizing a processor with an imaging device, such as a digital camera, to capture and analyze images of raw materials, enhancing features, extracting attributes like staple length and fibre fineness, and comparing them to reference values for real-time, cost-effective, and portable quality determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sophisticated material checking R&D machines are used to determine quality attributes of raw materials, then measurement precision is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent replaces complex mechanical testing machines with an optical imaging system using digital cameras to capture images of raw materials. Image processing algorithms then extract quality attributes such as staple length, fibre fineness, and short fibre content from these images, substituting mechanical measurement methods with optical and computational approaches.
Solution Approach 2:
The system creates digital copies (images) of the raw material samples and performs quality analysis on these copies through image processing. This eliminates the need for physical manipulation and complex mechanical testing apparatus, allowing multiple measurements to be performed on the same sample without damage.
2Measurement precision
If laboratory testing methods are used to check quality attributes, then measurement precision is improved, but loss of time increases due to sample preparation and testing duration
Solution Approach 1:
The imaging system allows for continuous quality assessment without interruption. Multiple images can be captured and processed in rapid succession, and the system can analyze samples continuously as they move through the production line, eliminating the start-stop nature of traditional laboratory testing.
Solution Approach 2:
The system performs quality attribute extraction directly from images without requiring preliminary sample preparation steps such as mounting, sectioning, or staining that are necessary for laboratory testing. The raw material can be imaged in its natural state, significantly reducing preparation time.
3Reliability
If traditional quality management systems are implemented, then reliability of quality determination is improved, but device complexity and cost increase
Solution Approach 1:
The system compares extracted quality attributes against reference values stored in a database to automatically determine whether the raw material meets quality specifications. This feedback mechanism provides objective, consistent quality determination without requiring complex manual evaluation procedures.
Solution Approach 2:
The imaging and analysis system is designed to be operated with minimal human intervention. The automated image processing and quality determination algorithms perform the quality assessment functions that previously required skilled operators and complex procedural systems.
4Measurement precision
If sophisticated R&D machines are deployed for quality checking, then measurement precision is improved, but ease of operation deteriorates due to specialized training requirements
Solution Approach 1:
The system is designed to operate autonomously with minimal human intervention. The automated image capture, processing, and quality determination functions eliminate the need for operators to perform complex manual measurements or interpret test results, making the system easy to operate despite its sophisticated capabilities.
Data Source
AI summary
Systems and methods are described for determining quality attributes of raw material of textile. According to an embodiment the for determining quality attributes of raw material of textile can include a processor coupled with a memory, the memory storing instructions executable by the processor to: receive one or more images of said raw material captured by an imaging device; enhance one or more features of said received one or more images by varying dynamic range of said one or more features of said received one or more images to obtain dynamically enhanced one or more images; extract values of one or more attributes of said enhanced one or more images, wherein said one or more attributes includes any or combination of staple length, fibre fineness, short fibre content, yarn hairiness, yarn count, yarn elongation, maturity and moisture content; and compare the extracted values with reference attribute values stored in a first database, wherein quality of said raw material is determined based on comparison of the extracted values with the reference attribute values.