Developing Predictive Mathematical Model for Optimizing Coating Weight Variation in Galvalume Production: A Case Study of a Metal Industry

Abstract

Variations in coating weight for galvanized steel sheets can result in notable differences between batches. Such variations may cause various issues, such as diminished corrosion resistance, lower mechanical strength, and visual defects, which can ultimately drive up costs, lead to customer dissatisfaction, and pose safety risks. Even with attempts to manage elements like air knife pressure and line speed, coating weight inconsistencies remain challenging. The research focuses on developing a predictive mathematical model designed to optimize variations in coating weight during Galvalume production. The critical parameters influencing coating weight variation were identified and analysed using a systematic literature review, primary data collection and process observation. The findings reveal that substrate thickness, air knife pressure, line speed, bath composition, bath temperature, nozzle-to-strip distance and immersion time significantly affect coating weight. By applying regression analysis and optimization techniques such as Response Surface Methodology (RSM), the study provides a comprehensive understanding and practical solutions for achieving consistent coating weights. As a result, a model that integrates these factors was developed to forecast coating weight, and the predictive model can be used by industry practitioners to optimize production processes, reduce material wastage and ensure high-quality outputs in hot dip galvanization operations.