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

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Total article views: 588 (including HTML, PDF, and XML)

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HTML: 420
PDF: 140
XML: 28
Total: 588
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Views and downloads (calculated since 26 Feb 2026)

Month	HTML	PDF	XML	Total
Feb 2026	29	14	2	45
Mar 2026	76	23	6	105
Apr 2026	100	32	3	135
May 2026	100	16	7	123
Jun 2026	12	5	3	20
Jul 2026	27	21	0	48
Aug 2026	22	8	0	30
Sep 2026	54	21	7	82

Cumulative views and downloads (calculated since 26 Feb 2026)

Month	HTML	PDF	XML	Total
Feb 2026	29	14	2	45
Mar 2026	76	23	6	105
Apr 2026	100	32	3	135
May 2026	100	16	7	123
Jun 2026	12	5	3	20
Jul 2026	27	21	0	48
Aug 2026	22	8	0	30
Sep 2026	54	21	7	82

Latest update: 27 Sep 2026

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.