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Search Publications by: Gregory W. Vogl (Fed)

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Displaying 1 - 25 of 47

State Space Neural Network with Nonlinear Physics for Mechanical System Modeling

February 19, 2025
Author(s)
Reese Eischens, Tao Li, Gregory Vogl, Yi Cai, Yongzhi Qu
Dynamic modeling of mechanical systems is important for the monitoring, diagnostics, control, and prediction of system behaviors. Modeling dynamic system is one of the emerging tasks in scientific machine learning. Neural networks have been used to learn

Machine Tool Thermal Error Measurement and Prediction via Wireless Microscope

October 1, 2024
Author(s)
Zongze Li, Gregory Vogl, Edward Kinzel, Botond Santa, Robert Landers
A novel method is proposed to measure the thermal errors of a three-axis machine tool by taking images of unique custom-designed fiducials attached to a worktable using a wireless microscope mounted to the spindle. Multiple fiducials are applied for the

Robust feature design for early detection of ball screw degradation

October 1, 2024
Author(s)
Xu Han, Marcella Miller, Gregory Vogl, Guanyu Chen, Xiaodong Jia
The ball screw is a critical device for precision linear motion control that has widespread applications in industrial robots, computer numerical control (CNC) machines, and high-precision leveling systems, among others. Because high-precision positioning

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

November 13, 2023
Author(s)
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
Author(s)
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
Author(s)
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
Author(s)
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
Author(s)
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
Author(s)
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
Author(s)
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
Author(s)
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