Process monitoring in end milling using polar figures

• Autorzy: Goetz Sven , Schraknepper Daniel , Faustini Gabriel , Bergs Thomas

Identyfikatory

DOI

10.36897/jme/119690

• Języki publikacji: EN

Abstrakty

EN

Knowledge of the tool wear state in machining has become an important issue in research and industrial application. Current systems use the spindle power or cutting force as measured variable and refer it to a taught set point. However, this method lacks the ability to adapt to new work piece geometries. A new approach focusses on the tool instead of the work piece, and uses a sensory tool holder with integrated strain gauges. This tool holder provides polar figures whose shapes relate to the engagement conditions and whose area is a function of the tool load. As the tool load increases with tool wear, the area of the polar figures provides information about the tool wear status, and with knowledge about the engagement conditions, the model can be calibrated.

Słowa kluczowe

EN

process monitoring; tool wear; polar figur

• Wydawca: Wydawnictwo Wrocławskiej Rady FSNT NOT
• Czasopismo: Journal of Machine Engineering
• Rocznik: 2020
• Tom: Vol. 20, No. 3
• Strony: 95--105
• Opis fizyczny: Bibliogr. 28 poz., rys., tab.

Twórcy

autor

Goetz Sven

• WZL der RWTH Aachen, Aachen, Germany

autor

Schraknepper Daniel

• WZL der RWTH Aachen, Aachen, Germany

autor

Faustini Gabriel

• WZL der RWTH Aachen, Aachen, Germany

autor

Bergs Thomas

• WZL der RWTH Aachen, Aachen, Germany

Bibliografia

• [1] DIN8580:2003-09: Manufacturing Processes – Terms and Definitions, Division, https://dx.doi.org/10.31030/9500 683.
• [2] KLOCKE F., 2011, Manufacturing Processes, Berlin, Heidelberg, New York, Springer.
• [3] AUGSPURGER T., 2019, Thermal Analysis of the Milling Process, Dissertation RWTH, ISBN: 978-3-86359-676-7.
• [4] BAYRAMOGLU M., DUNGEL Ü., 1998, A Systematic Investigation on the Use of Force Ratios in Tool Condition Monitoring for Turning Operations, Trans. Inst. Meas. Control, 20/2, 92–97.
• [5] DIMLA D.E., 2000, Sensor Signals for Tool Wear Monitoring in Metal Cutting Operations – A Review of Methods, Int. J. Mach. Tool Manuf., 40/8, 1073–1098.
• [6] KETTELER G., 1996, Prozessüberwachung mit Acoustic-Emission beim Messerkopfstirnfräsen, Dissertation, RWTH Aachen.
• [7] YOHANNES B., 2013, Industrielle Prozessüberwachung für die Kleinserienfertigung, Berichte aus dem IFW, 03, PZH Verlag.
• [8] KAUPP M., 2014, Ein Verfahren zur automatischen erzeugung intelligenter prozessüberwachungssysteme, Stuttgart, Fraunhofer Verlag.
• [9] KAEVER M., 2004, Steuerungsintegrierte fertigungsprozess-überwachung bei spanender Bearbeitung, Dissertation, RWTH Aachen, Shaker Verlag.
• [10] GHANI J.A., 2009, New Regression Model and I-Kaz Method for Online Cutting Tool Wear Monitoring, Int. J. of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, 3, 1507–1512.
• [11] GHANI J.A., 2013, The Application of I-Kaz-Based Method for Tool Wear Monitoring Using Cutting Force Signal, Procedia Engineering, 68, 461–468.
• [12] AHMAD M.A.F., NUAWI M.Z., ABDULLAHA S., WAHIDA Z., KARIMB Z., DIRHAMSYAH M., 2015, Development of Tool Wear Machining Monitoring Using Novel Statistical Analysis Method, I-Kaz, Procedia Engineering, 101, 335–362.
• [13] KASIM N.A., 2019, Cutting Power Wear Progression Index via Signal Element Variance, Journal of Mechanical Engineering and Sciences, 13, 4596–4612.
• [14] FUSSEL B., 2015, Real-Time Tool Wear Monitoring in Milling Using a Cutting Condition Independent Method, International Journal of Machine Tools & Manufacture, 89, 1–13.
• [15] CUI Y., 2009, Tool Wear Monitoring for Milling by Tracking Cutting Force Model Coefficients, Proceedings of the North American Manufacturing, Research Institution of SME, 37.
• [16] ZHOU Y., XUE W., 2018, A Multisensor Fusion Method for Tool Condition Monitoring in Milling, Sensors, 18, 3866–3878.
• [17] DENKENA B., 2015, Die vernetzte Produktion – Forschungsergebnisse für die praxis, Final report of the SFB 653 collaborate research center, PZH Verlag Hannover, ISBN: 978-3-95900-045-1.
• [18] SCHMIDT C., 2011, Einflussgrößensensitive simulation und überwachung von fräsprozessen, Berichte aus dem IFW 01/2011, PZH Verlag.
• [19] LITWINSKI K.M., 2011, Sensorisches spannsystem zur überwachung von zerspanprozessen in der einzelteilfertigung, Berichte aus dem IFW, 02/2011, PZH Verlag.
• [20] SHENG H., 2012, Model-Based Tool Condition Monitoring for Ball-Nose End Milling, PhD-Thesis, National University of Singapore.
• [21] ALTINTAS Y., YELLOWLEY I., 1987, The Identification of Radial Width and Axial Depth of Cut in Peripheral Milling, International Journal for Machine Tools and Manufacture, 27/3, 367–381.
• [22] TARN J.H., TOMIZUKA M., 1989, On-Line Monitoring of Tool and Cutting Conditions in Milling, Journal of Engineering for Industry, 111, 206–212.
• [23] KWON W.T., CHOI D., 2002, Radial Immersion Angle Estimation Using Cutting Force and Predetermined Cutting Force Ratio in Face Milling, International Journal of Machine Tools & Manufacture, 42, 1649–1655.
• [24] HWANG J.H., 2003, In-Process Estimation of Radial Immersion Ratio in Face Milling Using Cutting Force, International Journal of Advanced Manufacturing Technology, 22, 313–320.
• [25] CHOI J-G., YANG M-Y., 1999, In-Process Prediction of Cutting Depths in End Milling, International Journal of Machine Tools and Manufacture, 39/5, 705–721.
• [26] BERGS T., GOETZ S., 2019, Estimation of Engagement Conditions Using an ANN Pattern Recognition System on the Base of a Sensory Tool Holder, MM Science Journal, 11, DOI: 10.17973/MMSJ.2019_11_2019086.
• [27] KARPUSCHEWSKI B., Sensoren zur prozessüberwachung beim spanen, Fortschritts-Berichte, 2/581, VDI Verlag, Düsseldorf, ISBN: 3-18-358102-7.
• [28] KIENCKE U., 2008, Signalverarbeitung, Zeit-Frequenz-Analyse und Schätzverfahren, Oldenburg-Verlag, München.