<![CDATA[Corona plasma technology has been studied as a surface modification for the adhesive bonding of polymers. Although corona plasma (C.P.) is becoming more popular in nanotechnology, the influence of corona plasma treatment parameters on the FT-IR spectra is a problem that has yet to be addressed. The purpose of this study is to use an artificial neural network to study the influence of corona plasma (C.P.) treatment parameters on textile polymer and evaluate the ability of this model to predict FT-IR spectral information from FT-IR measurements. In this study, polymers were modified under various corona plasma treatment conditions. We investigated FT-IR spectra information of polymers from FT-IR measurements by varying corona plasma treatment variables. We used three input parameters in this study: wavenumber, voltage, and exposure time—two output parameters: fabric roughness with SEM according to the degree of smoothness and percent transmission with FT-IR. The novel aspect of this study is that we used ANN to model the plasma treatment on polyester-cotton fabrics and the FT-IR spectra accurately enough for the first time. According to this study, the model that used four nodes (neurons) in the hidden layer, three input parameters (x1,x2,x3), and 20 iterations is appropriate for determining fabric surface roughness (S.R.) and percent transmission (T%). Based on this research, the values of R2 for determining fabric surface roughness (S.R.) and percent transmission (T%) were 99.79 percent and 67.18 percent, respectively. The results showed that the developed ANNs could accurately predict the experimental data in detail. This study is significant because it uses artificial intelligence to calculate and simulate the FT-IR spectra and fabric surface roughness of plasma treatment on textile fabrics. This study's scientific application is that it will help experts, researchers, and engineers understand the implications of plasma on the chemical structure of textile materials.]]>
