NTN intros machine learning to boost design
NTN announced that it has integrated machine learning technology into its automated calculation system used for designing 3rd‑generation hub bearings, marking the first use of this approach in the bearing industry.
By incorporating AI into the company’s ABICS design platform, NTN said it has reduced performance evaluation analysis to less than one-tenth of the conventional time while enabling automatic proposals for design dimensions that meet required specifications. This advancement significantly reduces design workload and supports shorter development cycles for customers.
As model-based development continues to expand across the automotive sector, NTN said its enhancements to ABICS build on improvements introduced in 2022, when the system first reduced design person-hours by approximately 80 per cent. Traditionally, FEM analysis required multiple rounds of redesign and verification due to the complex geometry of 3rd‑generation hub bearings. With AI-assisted prediction, NTN noted that certain FEM results can now be generated much faster, and the system can automatically suggest revised dimensions if initial targets are not met.
The technology combines a simulation model using Lasso Regression to identify essential data with Bayesian Optimization to determine ideal solutions across a wide range. NTN said it plans to automate all FEM analyses within ABICS by fiscal year 2029, aiming to reduce design hours by more than 90 per cent. The company added that it will continue advancing digital tools such as CAE and AI to enhance research, development, and product performance.
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