An enhanced neural network-based method for predicting step error in a five-axis finishing machining

Ye, Te; Liu, Wei; Zhang, Jiaping; Chen, Jiawei; Yu, Jingyang; Zhao, Yuhang; Zhang, Ziyu

doi:10.5194/ms-17-747-2026

Articles | Volume 17, issue 2

https://doi.org/10.5194/ms-17-747-2026

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

https://doi.org/10.5194/ms-17-747-2026

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

Articles | Volume 17, issue 2

Research article

|

20 Jul 2026

Research article |

| 20 Jul 2026

An enhanced neural network-based method for predicting step error in a five-axis finishing machining

Te Ye, Wei Liu, Jiaping Zhang, Jiawei Chen, Jingyang Yu, Yuhang Zhao, and Ziyu Zhang

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Short summary

Traditional mathematical methods for calculating machining errors before production are often time-consuming. To solve this problem, we developed a data-driven intelligent method that learns from existing data to realize real-time error prediction. Our method achieves over 99 % prediction accuracy and runs significantly faster than conventional approaches, helping factories manufacture high-precision, high-quality parts more efficiently.