Yarn Tensile Properties Modeling Using Artificial Intelligence

Yarn quality is an important factor that influences the subsequent products quality. Employing artificial intelligence technologies can lead to more objective yarn testing systems and higher product specifications that meet the demands of the manufacturing and end-user. This work was carried out in order to employ image processing and artificial neural networks for modeling yarn tenacity and elongation% for different yarn types using a feasible method in cost and time. To evaluate yarn parameters, yarn samples were collected from the yarn testing lab and wound around a blackboard using the appearance tester. Sample images were captured and preprocessed and vectors were defined and used as network inputs. Feed-forward neural networks trained with the back-propagation rule were employed. Two systems were developed; the first one was used to assess the elongation% and tenacity of cotton yarns and the second was utilized for blended yarn parameters evaluation. By applying image enhancement combined with a multilayer neural network, better results were obtained for estimating different yarn parameters. Therefore, a modeling system can be implemented successfully. It was also proved that the system can be applied for diverse yarn types and different tested parameters. This research is an attempt to model several yarn properties using a cost effective system that employs machine vision and learning. In addition, utilizing these techniques can improve properties assessment and overall quality control.