A novel neural network model for rolling linear guide pair optimization design

Song, Chenghao; Du, Weiqi; Li, Shuxin; Han, Junjun

doi:10.5194/ms-17-141-2026

Articles | Volume 17, issue 1

https://doi.org/10.5194/ms-17-141-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/ms-17-141-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 17, issue 1

Research article

|

26 Feb 2026

Research article |

| 26 Feb 2026

A novel neural network model for rolling linear guide pair optimization design

Chenghao Song, Weiqi Du, Shuxin Li, and Junjun Han

Related authors

A Coupled Crystal Plasticity-Phase-Field Damage Model for Integrated CAD/CAE-Based Fatigue Design of Rolling Bearings

Chao Ji, Weiqi Du, Yandong Zhao, and Yibin Wu

Mech. Sci. Discuss., https://doi.org/10.5194/ms-2026-192,https://doi.org/10.5194/ms-2026-192, 2026

Preprint under review for MS

Short summary

Rolling guides are essential parts of precision machines, yet their design often depends on slow and costly simulations. This study presents a computer model that learns from a small number of examples to predict and improve guide performance. Combined with an automated design platform, it enables faster, more accurate, and efficient optimisation of mechanical components.