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Cutting force estimation from machine learning and physics-inspired data-driven models utilizing accelerometer measurements

Published

Author(s)

Gregory W. Vogl, Yongzhi Qu, Reese Eischens, Gregory Corson, Tony Schmitz, Andrew Honeycutt, Jaydeep Karandikar, Scott Smith

Abstract

Monitoring cutting forces for process control may be challenging because force measurements typically require invasive instrumentation. To remedy this situation, two new methods were recently developed to estimate cutting forces in real time based on the use of on-machine accelerometer measurements. One method uses machine learning, while another uses a physics-inspired data-driven approach, to generate a model that estimates cutting forces from on-machine accelerations. The estimated forces from both approaches were compared against cutting force data collected during various milling operations on several machine tools. The results reveal the advantages and disadvantages of each model to estimate real-time cutting forces.
Proceedings Title
Procedia CIRP
Conference Dates
July 12-14, 2023
Conference Location
Gulf of Naples, IT
Conference Title
17th CIRP Conference on Intelligent Computation in Manufacturing Engineering

Keywords

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

Citation

Vogl, G. , Qu, Y. , Eischens, R. , Corson, G. , Schmitz, T. , Honeycutt, A. , Karandikar, J. and Smith, S. (2023), Cutting force estimation from machine learning and physics-inspired data-driven models utilizing accelerometer measurements, Procedia CIRP, Gulf of Naples, IT, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=956179 (Accessed June 13, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created November 13, 2023