Towards Better Data Pre-Processing for Building Recipe Recommendation Systems from Industrial Fabric Dyeing Manufacturing Records: Categorization of Coloration Properties for a Dye Combination on Different Fabrics

Abstract

Intelligent manufacturing for the fabric dyeing industry requires high-performance dyeing recipe recommendation systems. Nowadays, recommending dyeing recipes by mining dyeing manufacturing data has become a new direction for the development of recipe recommendation systems. As one of the indispensable parts in the system development, data pre-processing needs more than routine steps such as the removal of missing data and outliers. Considering that dyes can have very different coloration properties on different fabrics, dyeing manufacturing records for a given dye combination to different fabric types should be properly categorized before they are used for training regression models for dyeing recipe prediction. In this paper, we propose a simple but effective method for this categorization work. Our method uses conventional K-means clustering analysis to find fabric types that have similar coloration properties for a given dye combination. We have applied the method on a dye combination formed by Colvaceton reactive dye-navy blue CF (CRD-navy blue), Colvaceton reactive dye-bright red 3BSN150% (CRD-red) and Colvaceton reactive dye-yellow 3RS150% (CRD-yellow) on 28 different types of fabrics. We show that these 28 types of fabrics can be well categorized into 8 groups based on the coloration properties. Our proposed method can be listed as one of the standard data pre-processing steps in the development of data-mining based recipe recommendation systems.

Textile Dyeing Process and Dyeing Recipe Prediction Using Artifical Intelligence

“…Results from the suggested model and the Datacolor 650 spectrophotometer were compared. Tu et al 2022 For this categorization, a straightforward but successful methodology is presented in this study. To identify categories of fabrics with comparable coloring characteristics for a particular combination of dyes, the method employs the classic K-Means clustering analysis.…”