3D magnetic flux density measurement with reduced sampling and high accuracy using visual localization and adaptive mesh generation

Accurate measurement of magnetic flux density distribution is paramount for various industrial applications, including but not limited to automotive design, electronics, and renewable energy systems. Traditional measurement methods are often labor-intensive and time-consuming, leading to potential inaccuracies and inefficiencies. In response to these challenges, this paper proposes an autonomous method that not only enhances accuracy but also significantly reduces the number of measurements required, thereby saving time and resources. In this study, we specifically apply our proposed method to the 3D-printed axial flux variable reluctance resolver. By employing CAD/CAE tools for magnetic flux density simulations and an adaptive mesh generation approach for measurement point generation, our method achieves a substantial reduction in measurement points by 94.9% compared to traditional methods, while maintaining a high similarity within a margin of 97.7%. This efficiency translates into significant time savings and resource optimization for industrial applications. The ability to maintain low distortion and high accuracy in the magnetic flux density distribution is of utmost importance in these applications, especially when utilizing a 3D-printed axial flux VR resolver. This research presents a viable solution to address these industrial needs, enhancing measurement efficiency by over 94.7%. This improvement leads to quicker and more reliable information on 3D magnetic flux density distribution and quality, ultimately contributing to enhanced productivity and more efficient product development processes.