Abstract
One of the metrics that establishes the carpet quality is its compress and decompress behaviors. In a research, addressed later in the paper, carpet samples with the acrylic pile were exposed to the ultraviolet (UV) exposure for 0, 8, 12, 16, and 20 h. The wear test was then carried out using drum revolutions of 0, 4000, 6000, and 8000. Following that, the samples were subjected to a static stress of 700 KPa for 2 h. After removing the load, the sample thickness was measured every 2 min. Finally, the thickness loss and recovery features were determined. Now in this study, the statistical analysis demonstrated the significance of recovery time, UV exposure, and drum revolutions on the features mentioned above, and in the next step, they were modeled using an artificial neural network (ANN) structure. The genetic algorithm, particle swarm optimization, and gray wolf optimization (GWO) were applied as the alternatives to the backpropagation (BP) algorithm for updating the weights and biases of ANN. In the end, it was found that the thickness loss and recovery could be predicted with 6.92 and 9.14% errors, using ANN-GWO and ANN-BP-GWO, respectively.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
