National Textile Center

Year 8 Proposal

Project No.: I97-S01

Competency: Intelligent Systems

Real Time Yarn Characterization and Data Compression Using Wavelets

Project Team:

Leader Warren J. Jasper Expertise Real-Time Acquisition and Control

Email: wjasper@tx.ncsu.edu Phone: (919) 515-6565

Name/school/expertise

Members: Moon Suh / NCSU / Statistics, Textile Process Control

Jae Woo / NCSU / Textile Technology and Instrumentation

Objective:

To design a holistic system for measurement and quality maximization in spun yarn manufacturing by combining on-line measurement (data fusion), data analysis, compression, and regeneration for fabric quality visualization using "wavelet" theories and stochastic models. The system being developed extracts, retains, and synthesizes only the essential information required for characterizing the salient qualities of yarns and fabrics, without having to process and store vast amounts of data acquired on-line. This task will provide the textile industry with a powerful and cost-effective quality monitoring and measurement system.

Relevance to NTC Mission:

To compete internationally, the U.S textile industry must capitalize on its strength: information and computer technologies, and a high-technology infrastructure. These strengths facilitate an environment for high quality and customer-driven quick-response networking. The proposed project incorporates these strength through the PI's diverse backgrounds (Tex. Eng., Mech. Eng., Statistics), educating students and transferring the technology to the industry

State of the Art:

The Zweigle G-580â and Uster 3â are two of the most reliable instruments for measuring variation in yarn mass and density, respectively. However, neither of these instruments measures both mass (absolute) and diameter, nor do they perform compression or rendering. Additionally, effects due to tension variation, measurement speed, sampling rate, and sampling zone are either uncontrolled or unknown. The Cyrosâ system by CIS renders knitted and woven fabrics from a single yarn sample, but does not take into account differences in the warp yarns (woven) or different courses (weft knits). While new spinning frames are being marketed with capabilities to measure and store the yarn density or diameter signals from every 2-mm segments, the information processing and process control systems have yet to benefit from these high acquisition rates.

Approach:

The progress made thus far in this project provides an excellent bases for completing the following technical tasks within a year.

A. Completion of a Signal Capturing and Processing System

Variation in mass per unit length and diameter of yarns have long been the most important characteristic in textile processing and quality control. The existing commercial systems, such as Zweigle G-580â and Uster 3â , however, are inadequate for measuring the real features of yarns as they measure either the mass or diameter, but not both. To overcome these limitations, we will complete development of a multi-sensor, simultaneous yarn measurement system by combining an optical and capacitor sensors. In the new system, both the mass density and the diameter are measured and combined in such a way that the two quantities are matched in spatial domain and transformed into a new physical quantity. The new system will consist of 1) optical and capacitor type sensors, 2) a tension control device, 3) a yarn guide for dampening tension variation, and 4) a signal processor. These will be integrated into one simultaneous yarn measurement system equipped with a tension control device.

B. Data Reduction Using Wavelet-Stochastic Hybrid method

With faster sensors and modern data-acquisition systems, the data flow rate has increased exponentially over the last few years. For a yarn length of only 600m, the size of data file required has to be at least 1.2 MB. Yet, the presently available analytical methods either compress the data into a single number (CV%), or convert the entire data set into a spectrogram deleting the important spatial information completely. In practice, we have succeeded in compressing the original 1.2MB data set to less than 1% by applying the wavelet analysis alone. Using a combination of Wavelets and stochastic models, we have developed a prototype system capable of achieving over 100,000 to 1 data compression. By rendering the data as both a woven and knitted structure using the entire data set and the compressed data set, we found no appreciable difference in the quality of the fabric rendered.

This Year’s Goal:

The direction in the coming year is to focus our efforts on how to best represent the totality of yarn characteristics with a minimal amount of data and to complete a data reduction system with 100,000:1 compression capability. This involves measuring one or more physical characteristics (mass density, diameter, hairiness) on-line, combining the information (data-fusion), analyzing the data (filtering, data compression), and displaying the data in a meaningful form for decision support and feedback control. In addition, we will attempt to develop a "finger-printing" method for the fabrics generated from the compressed yarn signal data.

Outreach to Industry:

Our project hopes to bring together an instrument maker (Lawson-Hemphill and Keisokki), an equipment maker (Reiter), and a manufacturer (Harriet & Henderson Yarns, Unifi, National Textiles, Cone Mills). Most of these companies have been approached and have shown interest in this project.

New Resources Required:

Optical and capacitor type sensors that will be incorporated into on-line measurement systems. Our industry partners will facilitate the interface issues