Optimal selection of cotton warp sizing parameters under system research limitation

Warp sizing is the process of applying the sizing agents to the warp yarn to improve its weavability along with improving the economic performance of weaving. We consider a finite set of sizing agents or parameters mapped into a finite set of sizing quality indicators. Due to various limitations of material and time resources, exhaustive system research and constructing an information technology to interpret and optimize sizing data is impossible. Therefore, we suggest an algorithm for controlling warp sizing quality under system research limitation, where optimal selection of cotton warp sizing parameters is exemplified. The algorithm utilizes a set of basis vectors of sizing parameters corresponding to a set of respective vectors of quality indicators. The method of radial basis functions is used to determine the probabilistically appropriate vector of quality indicators for any given vector of sizing parameters. The uncountably infinite space of sizing vectors is uniformly sampled into a finite space. The finite space may be refined by excluding sizing vectors corresponding to inadmissible values of one or more quality indicators. A set of Pareto-efficient sizing vectors is determined within the finite (refined) space, and an optimal, efficient sizing vector is determined as one being the closest to the unachievable sizing vector. The suggested algorithm serves as a method of optimal selection of warp sizing parameters, resulting in improved performance of warp yarns that can withstand repeated friction, stretching, and bending on the loom without causing a lot of fluffing or breaking. The algorithm is not limited to cotton, and it can be applied to any yarn material by an experimentally adjusted radial basis function spread.