Articles | Volume 17, issue 1
https://doi.org/10.5194/ms-17-141-2026
https://doi.org/10.5194/ms-17-141-2026
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

Cited articles

Cheng, S. W.: Analysis of non-uniform load distribution and stiffness for a preloaded roller linear motion guide, Mech. Mach. Theory, 164, 104407, https://doi.org/10.1016/j.mechmachtheory.2021.104407, 2021. 
Fallah, A., Mokhtari, A., and Ozdaglar, A.: On the convergence theory of gradient-based model-agnostic meta-learning algorithms, arXiv [preprint], https://doi.org/10.48550/arXiv.1908.10400, 2020. 
Finn, C., Abbeel, P., and Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks, in: Proceedings of the 34th International Conference on Machine Learning, edited by: Precup, D. and Teh, Y. W., PMLR, 70, 1126–1135, https://doi.org/10.48550/arXiv.1703.03400, 2017. 
Fishwick, P. A.: Neural network models in simulation: a comparison with traditional modeling approaches, in: Proceedings of the 21st conference on Winter simulation, ACM, New York, NY, USA, 702–709, https://doi.org/10.1145/76738.76828, 1989. 
Gill, P. E., Murray, W., and Saunders, M. A.: SNOPT: An SQP algorithm for large-scale constrained optimization, SIAM Rev., 47, 99–131, https://doi.org/10.1137/S0036144504446096, 2005. 
Download
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.
Share