Inspection of faults in textile web materials using wavelets and ANFIS

Quality is the watchword of any type of business. A product without quality leads to loss and lack of customer satisfaction. This is true in case of textile industries also. Textile manufacturing is a process of converting various types of fibers into yarn, which in turn woven into fabric. Weaving process is used to produce the fabric or cloth by interlacing two distinct set of yarn threads namely warp and weft yarn. In textile industries, quality inspection is one of the major problems for fabric manufacturers. At present, the fault detection is done manually after production of a sufficient amount of fabric. The fabric obtained from the production machine are batched into larger rolls and subjected to the inspection frame. The nature of the work is very dull and repetitive. Due to manual inspection of the manufactured fabric, there is a possibility of human errors with high inspection time, hence it is uneconomical. This paper proposed a PC-based inspection system with benefits of low cost and high detection rate. Both normal and faulty images are processed and features are extracted by using Gray Level Co-occurrence Matrix (GLCM) and classification is done using Adaptive Neuro Fuzzy Inference System (ANFIS). Proposed scheme performs 36.66% better than the existing microcontroller based classification system.