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Defect identification and classification has been a focal point in fabric inspection research, and remains challenging because of new microstructure defects occurring in twill grey panting fabrics weaved on modern looms such as Air jet looms and Rapier looms. The twill fabric defects that occur commonly on these auto looms are mostly localized microstructure defects such as looseweft and stitches. This paper focusses on the application of DC suppressed Fourier power spectrum obtained from Fourier Transform for the analysis of fabric images in terms of significant frequency contents, which depict the periodicity of fabric along with their magnitudes, magnitude sums between peaks and the fabric cover factor of the woven fabric, in order to identify the fabric faults. The analysis was carried out on real twill weave grey fabric of different fabric specifications by collecting as many as 27 statistical features along with fabric cover factor obtained from the marginals of DC suppressed Fourier power spectrum which were used as inputs to the neural network implementing Levenberg-Marquardt Back-propagation algorithm. The results of the neural network, optimized with 27(40) neurons in the input, a hidden layer and 3(2) neurons in the output layer respectively for the two fabric classes namely S1(S2), for identification of grey fabric defects are encouraging. The neural network converged in less than 35 iterations and gave a classification accuracy of almost 100% when compared to the NN classification rate of 89.28% without considering fabric cover factor. The details of the experimentation and the results thereof are presented in this paper.

Five series of block copolymers based on natural rubber and polyurethane were prepared from hydroxyl terminated liquid natural rubber (HTNR) and polyurethane (PU) formed by the reaction of diphenyl methane-4,4 0 -diisocyanate (MDI) with a chain extender diol, viz., ethylene glycol (EG)/propylene glycol (PG)/1,4-butane diol (1,4-BDO)/1,3-butane diol (1,3-BDO)/bisphenol A (BPA), by solution polymerization. Structural characterization of the block copolymers was done by infrared (IR) analysis. Thermal studies and kinetic analysis on thermal degradation of the block copolymers were undertaken with the view of characterizing them. Energy of activation and entropy change for the degradation were determined and a probable mechanism for the solid state degradation was suggested which corresponds to a three dimensional diffusion mechanism. DSC analysis has been used for the study of microphase separation in the block copolymers. Thermal transition of the hard segment significantly varies with the extender diol which highlights the effect of extender diol structure on the chain stiffening mechanism.

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