Supervised learning method and quality capability of process used in an optical transmission inspection of on-line nonwoven basis weight

Abstract

The supervised learning method and quality capability of process used in an on-line optical transmission inspection system of the basis weight for nonwoven material are investigated. A near-infrared light transmission inspection is applied in the production machine of a nonwoven fabric to detect the basis weight and support the producing quality. Using least squares method, the parameter transfer equations of the piecewise ploynomials functions between the measured voltage and the nonwoven basis weight are found. Supervised learning method is adopted to improve the producing capability. It is shown that the capability index of process Cp and Cpk is testing samples when the supervised learning algorithm is used. Obvious, the supervised learning method is effective to improve the producing capability and support the producing quality.