AI & Machine Learning Technology Efficient Design Engineering with AI

For AI and machine learning technologies to deliver tangible results in development environments, a comprehensive understanding of the challenges from an engineering perspective (domain knowledge) is essential. ESTECH leverages its extensive consulting experience and expertise to support the application of AI technology in our clients' development environments.
We don't just provide simple surrogate models; we identify the specific phases in the design and development process where the AI model will be used and the required outputs, providing the best possible model.
For example, we offer methods such as assigning system performance to component characteristics (Target Cascade Down) and systems that allow designers to perform performance evaluations on behalf of CAE engineers.

In product development with diverse performance requirements and specifications, the use of MBD has become widespread as a technique to satisfy multiple performance targets in an integrated manner while solving issues from an early stage. However, when performance can only be evaluated after the 3D shape is determined—such as for vibration/noise and strength/stiffness problems—teams face challenges that are difficult to front-load.
To address this, we propose applying AI and machine learning methods as an approach that allocates target performance to component characteristics and enables trial-and-error on trade-offs with other performance targets at a development stage before the 3D shape is finalized.
This is achieved by modeling the relationship between component characteristics and system characteristics based on data. In addition, by searching for and classifying multiple solutions that meet the performance targets, we can extract design proposals that are the most efficient and have minimal impact on other performance targets.