Spindle performance monitoring via accelerometer measurements in data-driven models

Published

February 12, 2026

Author(s)

Gregory Vogl, M. Wahidur Rahman, Yongzhi Qu

Abstract

The future of manufacturing depends on transitioning traditional machines into intelligent machine tools that can monitor and control themselves. As the spindle is an essential component of machine tools, the performance of machine tool spindles should be tracked for quality control. For example, smart spindles could be equipped with accelerometers for monitoring the spindle performance via models that relate the measured accelerations to the spindle error motions. Various data-driven models were created that estimate spindle-related displacements from on-machine accelerations. The estimated displacements were compared, revealing the advantages and disadvantages of each model to monitor the spindle performance.

Proceedings Title

Proceedings of the 18th CIRP Conference on Intelligent Computation in Manufacturing Engineering

Volume

138

Conference Dates

July 10-12, 2024

Conference Location

Naples, IT

Conference Title

18th CIRP Conference on Intelligent Computation in Manufacturing Engineering

Pub Type

Conferences

Download Paper

https://doi.org/10.1016/j.procir.2026.01.035

Local Download

Keywords

Smart manufacturing, Industry 4.0, Data-driven dynamics, Frequency response function, Machine tool, Modeling, Dynamics, Machining processes, Sensing, Monitoring, Diagnostics, Machine learning, Artificial intelligence

Citation

Vogl, G. , Rahman, M. and Qu, Y. (2026), Spindle performance monitoring via accelerometer measurements in data-driven models, Proceedings of the 18th CIRP Conference on Intelligent Computation in Manufacturing Engineering, Naples, IT, [online], https://doi.org/10.1016/j.procir.2026.01.035, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=957966 (Accessed May 14, 2026)

Additional citation formats