NIST - National Institute of Standards and Technology
11/13/2023 | Press release | Distributed by Public on 11/14/2023 03:07
Cutting force estimation from machine learning and physics-inspired data-driven models utilizing accelerometer measurements
Published
November 13, 2023
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
Pub Type
Conferences
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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 November 14, 2023)