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Article History

Received: 17 June 2025

Accepted: 2 January 2026

First Online: 23 January 2026

<!--Emphasis Type='Bold' removed-->Declarations

:

: The authors declare that they have no conflicts of interest.

: This manuscript is an original work and has not been submitted to or published in any other journal. All authors have significantly contributed to the research and have agreed to the submission of the manuscript in its current form. No part of this work has been plagiarized, and proper citations have been provided for all referenced materials. There is no data fabrication, falsification, or inappropriate manipulation involved. This study does not involve any experiments on human participants or animals. No personally identifiable data was used. Any software, tools, or datasets used in this study are either open-source, properly licensed, or used with permission. All authors agree to abide by the ethical responsibilities and publishing standards set by Springer Nature. The authors are also prepared to provide original data or supporting documentation upon request to validate the findings presented in this paper.The previous study (Hsieh et al., ) focused on the application of force sensor sensors on a small-scale stage, aiming to evaluate the feasibility of such sensing elements. The scope and findings of that study have been clearly cited and described in the Introduction section of this manuscript. In contrast, this manuscript applies force sensor sensors to a machine tool and utilizes a different signal processing system to carry out entirely new data collection. None of the data used in this study overlaps with that of the previous research. The key innovation of this manuscript lies in the introduction of various machine learning methods to identify the most suitable AI model for predicting the horizontal accuracy of CNC machine tools. The proposed models have been validated via experimental testing, confirming both the feasibility and the performance specifications of the approach.

: Parts of this manuscript are based on the co-author Yuchen Chien’s master’s thesis, which is currently under embargo; this manuscript has been substantially revised and extended for journal submission.