Advancing Commercial Coatings: A Novel Approach of Machine Learning Solutions to Sustainable Manufacturing

Manuscript submitted July 2, 2025; accepted July 31, 2025; published October 24 2025

Abstract—Traditional technology for making commercial coatings is limited in terms of efficiency and environmentally sustainability. Emerging Machine Learning (ML) and artificial intelligence (AI) technologies have the potential to transform the coatings industry through data-driven design, forecasting, and optimization of coating properties and processes. In this article, a brief overview of ML applications in protein-resistant, damping, ferroalloy, TiO₂, and epoxy-based coating design for net-zero carbon goals and sustainable production is presented. The major ML methods like neural networks and regression models are highlighted in property prediction, design optimization, and market analysis. The review concentrates on the transition from empirical and thermodynamic models to intelligent, green manufacturing for the substitution of traditional practices with novel, eco-friendly technologies.

keywords—ferroalloy coatings, damping coatings, protein-resistant coatings, predictive modeling, machine learning, artificial intelligence, sustainable manufacturing.

Cite: Harshit Mittal,"Advancing Commercial Coatings: A Novel Approach of Machine Learning Solutions to Sustainable Manufacturing," Journal of Advances in Artificial Intelligence, vol. 3, no. 4, pp. 248-253, 2025. doi: 10.18178/JAAI.2025.3.4.248-253

Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).