Case Study: Leading Critical Safety System Manufacturing Company improves brake pad design efficiency with Quantiphi AI solution

Determining Key factor affecting friction affecting characteristics and assisting in brake pad friction material design

Quantiphi worked with a Leading Critical Safety System Manufacturing Company, a manufacturer of critical safety systems such as brake pads and shock absorption technology. The customer’s R&D team spent significant time and cost testing brake pad materials to determine friction levels, but faced challenges including limited training data, 2,000+ input features, complex data structures, and the need to align model outputs with business understanding.

Quantiphi built an automated AI solution using Google Cloud services and machine learning methods such as gradient boosted decision trees and hyperparameter tuning to identify key features and predict brake pad friction coefficients. The result was a web-based interface for chemical formulators to submit material inputs and receive predicted friction values, achieving over 80% model accuracy and enabling an estimated 80% reduction in design iterations.