NTN Enhancing Through AI Integration
Key Highlights
- NTN has introduced AI-based machine learning into its bearing design process, marking a first in the industry.
- The new system predicts FEM analysis results in less than one-tenth of traditional time, significantly speeding up development.
- Automatic design dimension suggestions help ensure specifications are met, reducing manual effort.
- NTN plans to automate all FEM analyses and provide optimal design proposals by FY2029, aiming for over 90% reduction in design person-hours.
The NTN Corporation has introduced machine learning technology based on AI into its automated calculation system. This system has been used in the design of 3rd-generation hub bearings that support the rotation of automobile tires. This represents the first use of this method in the bearing industry.
By introducing AI technology into “ABICS,” NTN has achieved high-speed prediction of certain FEM analysis tasks in less than one-tenth of the time. Furthermore, if the results do not meet the required specifications, the system automatically suggests appropriate design dimensions.
NTN is the first company in the bearing industry to introduce machine learning technology using Lasso Regression with Bayesian Optimization into its design process.
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 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 it to promptly offer high-performance, high-quality products to its customers.