An Industrial Dyeing Recipe Recommendation System for Textile Fabrics Based on Data-Mining and Modular Architecture Design

We report a new fabric dyeing recipe recommendation system which is based on mining industrial dyeing manufacturing data and a system design with modular architecture. Unlike traditional dyeing recipe recommendation systems, our method does not rely on labor-intensive calibration works between dye concentrations and the color. Also, the system is generally designed for different dyeing tasks. We describe the framework of our method and discuss strategies that are used for building the system. The system is built in the form of modular architecture which is made up of multiple gradient boosting regression tree models (GBRT). Each GBRT has been trained for predicting dye concentrations of a dye combination set (DCS) for a fabric type. Methods for model training and typical model performance are reported in the paper as well.