Abstract

Project No. G94-2 titled Real-time.Fabric Defect Detection Kr Control in Weaving Processes was initiated on March1. 1994. This brief progress report covers the period from March 1. 1994 toSeptember 15, 1996 and details the major accomplishments during this reportingperiod. This collaborative research between Georgia Tech and N.C. State addresses the monitoring requirements for performance assessment of a weavingmachine under on-lint real-time conditions and control of the machineparameters to minimize fabric defects. Novel ideas for fault detection andidentification of woven textile structures are introduced and implemented. Asurvey of major textile defects has been conducted as well as the associatedtangible and intangible costs identified. Fractal scanning, a new technique, isdeveloped to scan the digitized image of textile fabrics. A fuzzified wavelettransform algorithm with adaptive noise rejection and on-lint learning is usedto extract features and a knowledge based inference engine is called upon todeclare the defect categories. Off-line learning is introduced to maximize thedetachability and identifiably measures. The viability of this technique isshown by test results of major textile fabric defects.

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