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Search Publications by: Gregory W. Vogl (Fed)

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Displaying 1 - 25 of 43

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

November 13, 2023
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

Vision-based thermal drift monitoring method for machine tools

April 15, 2023
Gregory W. Vogl, Ainsley Rexford, Zongze Li, Robert Landers, Edward Kinzel, Alkan Donmez, Joe Chalfoun
A method is presented to measure machine tool thermal drift for error compensation. A wireless microscope within a tool holder in the spindle is used to capture videos of image targets attached to the worktable. For each target, one video is captured

Ball Screw Health Monitoring with Inertial Sensors

September 30, 2022
Vibhor Pandhare, Marcella Miller, Gregory W. Vogl, Jay Lee
In industrial applications, the mechanical wear on ball screw components can lead to a loss of positioning accuracy that reduces the operational reliability and reproducibility of production systems. Existing monitoring solutions are impractical for real

Real-time estimation of cutting forces via physics-inspired data-driven model

May 25, 2022
Gregory W. Vogl, Dominique Regli, Gregory Corson
A method is presented to estimate the cutting forces in real time within machine tools for any spindle speed, force profile, tool type, and cutting conditions. Before cutting, a metrology suite and instrumented tool holder are used to induce magnetic

Influence of bearing ball recirculation on error motions of linear axes

January 10, 2021
Gregory W. Vogl, Kyle F. Shreve, Alkan Donmez
For positioning systems utilizing linear guides and trucks with recirculating balls, a method is presented that uses the measured total error motions and the measured phase of ball loops within trucks to determine the influence of each ball loop on the

Bearing Metrics for Health Monitoring of Machine Tool Linear Axes

June 13, 2019
Gregory W. Vogl, Brian C. Galfond, Jordan Jameson
Diagnostics and prognostics of rotating machinery ball bearings is quite mature with an abundance of available methods and algorithms. However, extending these algorithms to other ball bearing applications is challenging and may not yield usable results

Root-cause analysis of wear-induced error motion changes of machine tool linear axes

May 23, 2019
Gregory W. Vogl, Jordan Jameson, Andreas Archenti, Karoly Szipka, M A. Donmez
Manufacturers need online methods that give updated information of system capabilities to know and predict the performance of their machine tools. Use of an inertial measurement unit (IMU) is attractive for on-machine condition monitoring, so methods based

Identification of machine tool squareness errors via inertial measurements

May 14, 2019
Karoly Szipka, Andreas Archenti, Gregory W. Vogl, Alkan Donmez
The accuracy of multi-axis machine tools is affected to a large extent by the behaviour of the system's axes and their error sources. In this paper, a novel methodology using circular inertial measurements quantifies changes in squareness between two axes

Identification of machine tool geometric performance using on-machine inertial measurements

June 6, 2017
Gregory W. Vogl, Radu Pavel, Andreas Archenti, Thomas J. Winnard, Matlock M. Mennu, Brian A. Weiss, Alkan Donmez
Machine tools degrade during operations, yet accurately detecting degradation of machine components such as linear axes is typically a manual and time-consuming process. Thus, manufacturers need automated and efficient methods to diagnose the condition of