PPG Uses AI to Speed Collision Repair Paint Development
PPG Automotive Refinish is using artificial intelligence to accelerate product development for collision repair materials, reducing the time needed to formulate new paints and helping shops cut energy costs.
Nicole Sinclair, collision and allied product director for the Americas at PPG, said the company uses AI to evaluate formulation possibilities and narrow down chemical combinations faster than traditional methods would allow. Lab and real-world testing still validates every product before release.
"It's allowing us to make faster decisions and help with our customer outcomes," Sinclair said. "We're focused all the time on real shop benefits - how can we increase throughput, decrease cycle times, lower costs for our customers?"
Faster Drying, Lower Energy Bills
One example: PPG developed a clearcoat with reduced drying time using AI-assisted formulation. The product bakes in five minutes at 140 degrees Fahrenheit without affecting appearance, or air dries in less than an hour.
A shop performing 1,500 repairs annually would save 18,000 kilowatts per year with this clearcoat, equivalent to 4.5 metric tons of CO2 reduction.
Sinclair said AI expanded the range of formulation possibilities beyond what human chemists could intuitively explore. "The beauty of the AI-enabled development is that it can reduce that manual work, improve consistency, and help our technical experts become quicker at narrowing down the right combination of chemicals."
How PPG Applies the Technology
AI generates formulation outputs that PPG's team then tests extensively in the lab and in actual repair shops. The approach reduced the development timeline to market for the clearcoat.
PPG also uses digital tools to help painters reduce waste and improve color accuracy on the first attempt, cutting time spent on product mixing and dispensing.
Sinclair said the focus remains on solving painter pain points while improving shop profitability through faster cycle times, lower labor costs, and reduced energy consumption. "Our focus will continue to be solving customer pain points, and that is, in particular, our end-user painter, and then translating that into customer profitability through cycle time and labor time and waste reduction."
For professionals working in AI for Product Development, understanding how manufacturers apply these tools to real-world constraints-energy costs, cycle time, quality standards-offers practical insight into AI's role in materials science and manufacturing.
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