Primary Testing of an Instrumented Tool Holder for Brush Deburring of Milled Workpieces
| 1 | IFT – Institute for Production Engineering and Photonic Technologies,, TU Wien, Austria |
| 2 | System Engineering, My Tool IT GmbH, Austria |
| 3 | Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, United States |
CORRESPONDING AUTHOR
Christoph Ramsauer
IFT – Institute for Production Engineering and Photonic Technologies,, TU Wien, Getreidemarkt 9 / 311, 1060, Wien, Austria
IFT – Institute for Production Engineering and Photonic Technologies,, TU Wien, Getreidemarkt 9 / 311, 1060, Wien, Austria
Submission date: 2022-01-31
Final revision date: 2022-04-11
Acceptance date: 2022-05-04
Online publication date: 2022-05-16
Publication date: 2022-06-28
Journal of Machine Engineering 2022;22(2):99–107
KEYWORDS
TOPICS
ABSTRACT
Brush deburring requires consistent contact pressure between brush and workpiece. Automating adjustments to control contact pressure has proven difficult, as the sensors available in machine tools are usually not suitable to observe the small amplitude signals caused by this low force process. Additionally, both the power consumption and the vibration signal caused by the process strongly depend on the workpiece surface features. This paper describes a test setup using an instrumented tool holder and presents the corresponding measurement results, aiming to quantify the axial feed of the brush. It also discusses the interpretation of different signal components and provides an outlook on the utilization of the data for tool wear estimation.
REFERENCES (19)
1.
BRECHER C., WETZEL A., BERNERS T., EPPLE A., 2019, Increasing Productivity of Cutting Processes by Real-Time Compensation of Tool Deflection Due to Process Forces, Journal of Machine Engineering, 19/1, 16–27.
2.
EBERSPÄCHER P., SCHRAML P., SCHLECHTENDAHL J., VERL A., ABELE E., 2014, A Model- and Signal-Based Power Consumption Monitoring Concept for Energetic Optimization of Machine Tools, Procedia CIRP, 15, 44–49.
3.
LI Y., LIU C., JIAQI H., GAO J., MAROPOULOS P., 2019, A Novel Method for Accurately Monitoring and Predicting Tool Wear Under Varying Cutting Conditions Based on Meta-Learning, CIRP Annals – Manufacturing Technology, 68/1, 487–490.
4.
KUSS A., DRUSTA M., VERL A., 2016, Detection of Workpiece Shape Deviations for Tool Path Adaptation in Robotic Deburring Systems, Procedia CIRP, 57, 545–550.
5.
KULJANIC E., TOTIS G., SORTINO M., 2009, Development of an Intelligent Multisensory Chatter Detection System in Milling, Mechanical Systems and Signal Processing, 23, 1704–1718.
6.
MÖHRING H.-C., NGUYENA Q.P., KUHLMANNA A., LEREZA C., NGUYENA L.T., MISCHA S., 2016, Intelligent Tools for Predictive Process Control, Procedia CIRP, 57, 539–544.
7.
DROSSEL W.G., GEBHARDT S., BUCHT A., KRANZ B., SCHNEIDER J., ETTRICHRÄTZ M., 2018, Performance of a New Piezoceramic Thick Film Sensor for Measurement and Control of Cutting Forces During Milling, CIRP Annals, 67/1, 45–48.
8.
MÖHRING H.-C., LITWINSKI K.M., GÜMMER O., 2010, Process Monitoring with Sensory Machine Tool Components, CIRP Annals – Manufacturing Technology, 59, 383–386.
9.
DUNTSCHEW J., ESCHELBACHER S., SCHLUCHTER I., MÖHRING H.-C., 2021, Discrete Wavelet Tranformation as a Tool for Analysing the Borehole Quality when Drilling Carbon Fiber Reinforced Plastic Aluminium Stack Material, Journal of Machine Engineering, 21/1, 78–88.
10.
DITTRICH M.-A., DENKENA B., BOUJNAH H., UHLICH F., 2019, Autonomous Machining - Recent Advances in Process Planning and Control, Journal of Machine Engineering, 19/1, 28–37.
11.
NEUGEBAUER R., DENKENA B., WEGENER K., 2007, Mechatronic Systems for Machine Tools, Annals of the CIRP, 56/2, 657–686.
12.
BLEICHER F., RAMSAUER C., OSWALD R., LEDER N., SCHÖRGHOFER P., 2020, Method for Determining Edge Chipping in Milling Based on Tool Holder Vibration Measurements, CIRP Annals, 69/1, 101–104.
13.
SCHÖRGHOFER P., PAUKER F., LEDER N., MANGLER J., RAMSAUER C., BLEICHER F., 2019, Using Sensory Tool Holder Data for Optimizing Production Processes, Journal of Machine Engineering, 19/3, 43–55.
14.
UHLMANN E., LAGHMOUCHI A., GEISERT C., HOHWIELER E., 2017, Smart Wireless Sensor Network and Configuration of Algorithms for Condition Monitoring Applications, Journal of Machine Engineering, 17/2, 45–55.
15.
BLEICHER F., et al., 2021, Tooling Systems with Integrated Sensors Enabling Data Based Process Optimization, Journal of Machine Engineering, 21/1, 5–21.
16.
MATHAI G., MELKOTE S., 2012, Effect of Process Parameters on the Rate of Abrasive Assisted Brush Deburring of Microgrooves, International Journal of Machine Tools & Manufacture, 57, 46–54.
17.
AURICH J.C., DORNFELD D., ARRAZOLA P.J., FRANKE V., LEITZ L., MIN S., 2009, Burrs-Analysis, Control and Removal, CIRP Annals – Manufacturing Technology, 58, 519–542.
18.
NIKNAM S.A., SONGMENE V., 2014, Analysis of Friction and Burr Formation in Slot Milling, Procedia CIRP 17, 755–759.
19.
TETI R., JEMIELNIAK K., O’DONNELL G., DORNFELD D., 2010, Advanced Monitoring of Machining Operations, CIRP Annals – Manufacturing Technology, 59, 717–739.
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.