پيش بيني صداي پارچه تاري-پودي با استفاده از پردازش سيگنال و شبكه عصبي

چكيده انگليسي :

Isfahan University of Technology Department of Textile Engineering Woven Fabric Sound Prediction Using Signal Processing and Neural Network Maryam Gholamzadeh m gholamzade@tx iut ac ir Department of Textile Engineering Isfahan University of Technology Isfahan 84156 83111 Iran Degree M Sc Language Farsi Supervisor s Name and Email address Dariush Semnani d semnani@cc iut ac ir Mohammad Zarrebini zarrebini@cc iut ac ir Abstract Apparels are the main end use of textile fabrics In addition to general requirements that have to be satisfied by textile fabrics when converted to human apparel fabrics are expected to meet certain other specific requirements These are collectively known as textile comfort Fabric comfort comprises fabric sound which is the sound generated by fabric during wear by users Fabrics are regarded as flexible flat sheets Fabrics may generate unique sound which can be indicative of its other properties For example variation in hand of fabric is related to fabeic sound There for fabric sound can be employed to manipulate fabric properties Fabric sound also have psycho physiological effect not only on wearer but also on others Depending on the type of garment fabric sound can be a source of comfort or discomfort Therefore the amount of sound generated by fabric can be considered as an index of apparel comfort This index can determine the suitability of fabrics for their intended end use In this work sound generated by samples of fabric woven by cotton and cotton polyester yarns was investigated Specifications of the compared samples in warp direction were identical In order to stimulate sound generated by the samples an apparatus capable of sound induction was designed and developed The recorded sound signals were analyzed using Discrete Fourier Transform together with Discrete Wavelet Transform Sub bands energy of FFT and energy coefficients of wavelet transform were calculated Additionally stepwise multiple regression technique was employed Results showed that generated fabric sound is affected by both fabric pick density and weft yarn linear density Sound energy is increased when pick density of cotton polyester is increased However in case of polyester fabrics increases in pick density at certain frequencies resulted in reduction of sound energy It was also found that in case of cotton polyester fabrics an inverse relation exists between weft yarn linear density and sound energy This was confirmed by the observed increases in sub bands energy of the relevant sound frequencies It was found that the induced fabric sound was affected by both physical and mechanical properties of the samples In this respect tensile shear bending and surface properties as well as weft fractional cover of fabric were found to affect the amount of induced sound The effect of above factors on sound characteristics of samples was predicted using Multilayer Feed forward network with back propagation learning algorithm The network results showed that as far as sound volume is concerned surface roughness and drape coefficient of fabric are the most and the least effective parameters respectively Based on their sound characteristics samples were categorized into five different class using Kohonen neural network It is shown that fabrics may be categorized according to their sound characteristics Fabric sound is related to sound energy Sound characteristics were found to be related to physical and mechanical properties of fabrics such as tensile shear properties and weft fractional cover of fabric Key Words Comfort Sound Frequency Wavelet Fourier transform pick density tensile bending shear surface roughness neural network

غلام زاده، مريم

پيش بيني صداي پارچه تاري-پودي با استفاده از پردازش سيگنال و شبكه عصبي

راحتي , فركانس , تبديل موجك , تبديل فوريه , تراكم پودي , خمش , كشش , برش , زبري سطحي