Leveraging AI to accelerate complex calculations and automatically propose optimal design dimensions NTN Corporation (hereafter, NTN) has introduced machine learning*1 technology based on AI (Artificial Intelligence) into its automated calculation system, which has been utilized in the design of the 3rd generation hub bearings that support the rotation of automobile tires. This is the first method in the bearing industry*2. By doing so, NTN has significantly accelerated the time required for performance evaluation analysis to less than one-tenth of the conventional time, while also enabling the automatic design of dimensions that meet requirements. This helps reduce design workload and shorten customers' development periods. Machine learning: A method that learns patterns from data to perform prediction and classification According to our internal study of publicly available sources, including academic publications, as of December 2025 In automotive parts design, the introduction of model-based development (MBD), which simulates performance on computers to create higher-quality products more quickly, is progressing. In 2022, NTN introduced the automated calculation system ¡°ABICS,¡± which performs a series of design processes of the 3rd generation hub bearings to reduce design person-hours by approximately 80% compared to conventional methods and contribute to shortening development time for our customers.
In FEM analysis*3 to verify whether the design meets customer requirements, if the requirements were not satisfied, redesign followed by another FEM analysis was required. However, 3rd generation hub bearings required advanced calculations for FEM analysis due to its complex shape, which integrates the bearing and peripheral components such as bolts. FEM Analysis: A computational method for numerically analyzing physical phenomena such as stress concentration and deformation on a computer by dividing complex shapes or structures into fine elements By introducing the AI technology into ¡°ABICS,¡± we have achieved high-speed prediction of certain FEM analysis tasks in less than one-tenth of the conventional time. Furthermore, if the results do not meet the required specifications, the system automatically suggests appropriate design dimensions. By combining a simulation model using Lasso Regression*4 which seclects only the necessary data from a large dataset to predict analysis results based on input design dimensions with Bayesian Optimization*5 which is an algorithm for efficiently obtaining optimal solutions, we achieve high-precision predictions and dimensional proposals across a wide range. NTN is the first company within the bearing industry to introduce machine learning technology using Lasso Regression with Bayesian Optimization into its design process. Lasso Regression: A type of regression method (a general approach for predicting numerical outcomes from given inputs) that selects important variables and builds a predictive model Bayesian Optimization: An algorithm for efficiently finding the optimal solution with as few trials as possible in a short time by selecting the next trial point based on the results of previous trials NTN aims to leverage the newly introduced AI technology to enable automatic prediction of all FEM analyses implemented in ABICS and provide optimal design proposals by FY2029. Once all these functions are implemented, design person-hours are expected to be reduced by more than 90% compared to before the introduction of ABICS. NTN will also continue to utilize digital technologies such as CAE and AI to improve the efficiency and sophistication of research and development operations, enabling us to promptly offer high-performance, high-quality products to our customers. In addition, we will actively work on developing digital talent capable of utilizing these technologies.
