The hybrid approach of Neuro-Genetic and Genetic Algorithm techniques is developed to model, to simulate and to predict fibre to yarn spinning process and cost optimization. Starting with cotton, desired yarn is produced on ring frame. The quality and cost of resulting yarn play a significant role to determine its end application. The challenging task of any spinner lies in producing a yarn as per customer demand with added cost benefit. In this study, a Neuro-genetic concept is used to predict fibre properties for desired yarn. Genetic Algorithm approach is used further for cost optimization. These are combined into the so-called hybrid modeling frame work. The performance of Hybrid innovative model is superior compared to current manual machine intervention. The present model may be a fine framework for development of similar applications for complex model that require prediction and multi-objective optimization.
The spinning process is an important process in the Textile Industry. The yarn (output) coming out of the spinning process has a unique relationship with the input fibers. The input and output properties show a non-linear relationship between them. Many techniques, such as multiple regression, Artificial Neural Network (ANN) model, etc, have been used in an attempt to develop the said relation. In this paper, the Adaptive Neuro-Fuzzy Inference System (ANFIS), combined with subtractive clustering, is used to predict yarn properties. The ANFIS technique is evaluated for different datasets and its performance is compared with the ANN model.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.