Cutting force estimation from machine learning and physics-inspired data-driven models utilizing accelerometer measurements
Gregory W. Vogl, Yongzhi Qu, Reese Eischens, Gregory Corson, Tony Schmitz, Andrew Honeycutt, Jaydeep Karandikar, Scott Smith
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.
July 12-14, 2023
Gulf of Naples, IT
17th CIRP Conference on Intelligent Computation in Manufacturing Engineering
, Qu, Y.
, Eischens, R.
, Corson, G.
, Schmitz, T.
, Honeycutt, A.
, Karandikar, J.
and Smith, S.
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 December 10, 2023)