Ivan Scansani Gregori
Ivan Roger Scansani Gregori holds a Ph.D. in Engineering and is an Engineering Manager at ZF. He has more than thirty years of experience working with friction materials, tribology, and signal analysis, applying multivariate statistics and machine learning to real-world problems in clutch and transmission systems. He has also joined his son’s platform to help create high-quality content based on his expertise.
Multivariate exploratory analysis of eight process variables identified two that correlate with burst resistance — one positively, one negatively — and process changes based on that finding raised both burst resistance and reliability.
Design of experiments and Weibull analysis are used to correlate friction facing wear on a bench test with vehicle mileage, making it possible to estimate field durability from bench results.
The original presentation of a bench test procedure built to contain the full range of vehicle operating conditions, so that friction facing durability can be studied without depending on uncontrolled field data.
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Books
1Clutch facings live at the intersection of driver feel, safety, and durability—and this book shows how to engineer them with data, not guesswork. Across nine studies, laboratory torque signals, finite element models, sub-scale benches, and statistical experiments are used to predict judder, burst resistance, and wear long before a vehicle hits the road. It is a practical blueprint for anyone who wants faster material development, fewer field surprises, and test methods that actually correlate with the car.
Sep 14, 2026
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