Abstract


Project No. G94-2 titled Real-time. Fabric Defect Detection Kr Control in Weaving Processes was initiated on March 1. 1994. This brief progress report covers the period from March 1. 1994 to September 15, 1996 and details the major accomplishments during this reporting period. This collaborative research between Georgia Tech and N.C. State addresses the monitoring requirements for performance assessment of a weaving machine under on-lint real-time conditions and control of the machine parameters to minimize fabric defects. Novel ideas for fault detection and identification of woven textile structures are introduced and implemented. A survey of major textile defects has been conducted as well as the associated tangible and intangible costs identified. Fractal scanning, a new technique, is developed to scan the digitized image of textile fabrics. A fuzzified wavelet transform algorithm with adaptive noise rejection and on-lint learning is used to extract features and a knowledge based inference engine is called upon to declare the defect categories. Off-line learning is introduced to maximize the detachability and identifiably measures. The viability of this technique is shown by test results of major textile fabric defects.


 

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