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

Download Paper

Local Download

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 December 30, 2024)