Posted in | Materials Analysis | Materials Testing

Building Resilience in Lubricant Development with AI

From Apr 30 2026

When dealing with changes in base oil prices, additive limits, or international trade disputes, the only constant is the desire to respond to change quickly and efficiently.

Machine-learning technology enables lubricant manufacturers to estimate the performance of untested alternative raw materials, reducing the number of expensive final tests by 50–80%.

In this webinar, viewers will see a case study that demonstrates how this works.

  • Understand how to handle price shocks
  • Screen untested additives as a substitute for harmful constituents
  • Understand how AI models can help guide raw material stock levels

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