Predicting the tear strength of woven fabrics via automated machine learning: an application of the CRISP-DM methodology

Abstract

Textile and clothing is an important industry that is currently being transformed by the adoption of the Industry 4.0 concept. In this paper, we use the CRoss-Industry Standard Process for Data Mining (CRISP-DM) methodology to model the textile testing process. Real-world data were collected from a Portuguese textile company. Predicting the outcome of a given textile test is beneficial to the company because it can reduce the number of physical samples that are needed to be produced when designing new fabrics. In particular, we target two important textile regression tasks: the tear strength in warp and weft directions. To better focus on feature engineering and data transformations, we adopt an Automated Machine Learning (AutoML) during the modeling stage of the CRISP-DM. Several iterations of the CRISP-DM methodology were employed, using different data preprocessing procedures (e.g., removal of outliers). The best predictive models were achieved after 2 (for warp) and 3 (for weft) CRISP-DM iterations.