The role of Artificial Intelligence and Machine Learning in the paints and coatings industry
Artificial Intelligence (AI) and Machine Learning (ML) are spearheading a revolution across various industries, reshaping the way businesses operate and deliver value. The increasing sophistication of AI/ML simulation models and algorithms offers countless applications in healthcare, finance, manufacturing, logistics, and beyond; facilitating automation, predictive analytics and maintenance, resource efficiency, and supply chain optimization.
The dynamic field of paints and coatings is no exception, with AI/ML projected to grow by 40% annually in the construction sector over the next decade. Innovators in paint R&D are harnessing these technologies to improve upon existing capabilities, streamline production, enhance quality control, reduce costs, and optimize formulations.
Traditional methods for paint production are defined by time-consuming and wasteful trial and error. When navigating extensive datasets, AI/ML algorithms have the power to characterize raw material properties, predict how they will behave under certain conditions, uncover patterns and interrelations before they are physically made in the laboratory, and even create new chemical compositions.
This is made possible by “training” the algorithms to identify performance-related attributes, which allows them to simulate thousands of different coating compositions, detect optimal combinations of materials, and recommend starting point formulations or resin recipes. As an algorithm learns and grows with new information, its predictive capabilities improve.
For instance, AI/ML algorithms can diagnose and prevent flaws in existing products with remarkable accuracy when provided with extensive image datasets and expert input. This feedback is enabling innovators in paint R&D to expand the production frontier in terms of reflectivity, wear-resistance, adhesiveness, anti-corrosion, chemical safety, seal-cleaning, self-lubricating, and other innovative attributes, all while increasing product quality and consistency, and reducing waste.
At Pirta, incorporating state-of-the-art approaches to our product development is a foundational aspect of our ethos. Pirta has teamed up with experts from the Department of Materials Science & Engineering at the University of Utah to build a probabilistic AI/ML active learning framework model focused on highly reflective materials design and discovery using our extensive cooling paint lab sample database curated over more than two years of continuous R&D.
The insights we are hoping to gather with this effort will allow us to drive faster and more effective iterative paint development as the predictive power of our AI/ML model improves. This in turn will differentiate Pirta from competitors and enable us to expand our portfolio of superior-quality products that meet growing regulatory and sustainability requirements. As the role of AI and ML in our daily lives grows, Pirta remains dedicated to driving innovation and setting new standards for what is possible in the dynamic world of paints and coatings.
