Monitoring of the average cutting forces from controller signals using artificial neural networks

• Autorzy: Bugdayci Nevzat Bircan , Wegener Konrad , Postel Martin

Identyfikatory

DOI

10.36897/jme/154801

• Języki publikacji: EN

Abstrakty

EN

A new approach is presented to monitor the average cutting forces that are used for the calculation of the average cutting coefficients through neural networks using available controller signals. The cutting forces and the relevant controller signals are measured using a dynamometer and commercially available software supplied by the controller manufacturer in the calibration stage. Then a neural network is trained, which treats these controller signals as inputs and the cutting forces as the outputs. Finally, the average cutting forces for a new milling operation are predicted using the trained neural network without using a dynamometer. The proposed approach is validated using an experimental study, where a good match between predictions and measured forces is achieved. It is also shown that cutting coefficients can be calibrated and stability lobe diagrams can be generated using this method.

Słowa kluczowe

EN

milling; cutting force monitoring; artificial neural network

• Wydawca: Wydawnictwo Wrocławskiej Rady FSNT NOT
• Czasopismo: Journal of Machine Engineering
• Rocznik: 2022
• Tom: Vol. 22, No. 4
• Strony: 54--70
• Opis fizyczny: Bibliogr. 13 poz., rys., tab.

Twórcy

autor

Bugdayci Nevzat Bircan

• Institute of Machine Tools and Manufacturing, ETH Zurich, Switzerland

autor

Wegener Konrad

• Institute of Machine Tools and Manufacturing, ETH Zurich, Switzerland

autor

Postel Martin

• Institute of Machine Tools and Manufacturing, ETH Zurich, Switzerland

Bibliografia

• [1] ALTINTAS Y., 1992, Prediction of Cutting Forces and Tool Breakage in Milling from Feed Drive Current Measurements, Journal of Engineering for Industry, 114/4, 386–392.
• [2] PRICKETT P.W., JOHNS C., 1999, An Overview of Approaches to End Milling Tool Monitoring, International Journal of Machine Tools and Manufacture, 39/1, 105–122.
• [3] NOURI M., FUSSELL B.K., ZINITI B.L., LINDER E., 2015, Real-Time Tool Wear Monitoring in Milling Using a Cutting Condition Independent Method, International Journal of Machine Tools and Manufacture, 89, 1–13.
• [4] ALTINTAS Y., 2012, Manufacturing Automation: Metal Cutting Mechanics, Machine Tool Vibrations, and CNC Design, Cambridge University press.
• [5] EYNIAN M., 2019, In-Process Identification of Modal Parameters Using Dimensionless Relationships in Milling Chatter, International Journal of Machine Tools and Manufacture, 143, 49–62.
• [6] KARAGUZEL U. and BUDAK, E., 2018, Investigating Effects of Milling Conditions on Cutting Temperatures Through Analytical and Experimental Methods, Journal of Materials Processing Technology, 262, 532–540.
• [7] ALTINTAS Y., KERSTING P., BIERMANN D., BUDAK E., DENKENA B., LAZOGLU I., 2014, Virtual Process Systems for Part Machining Operations, CIRP Annals, 63/2, 585–605.
• [8] KOIKE R., OHNISHI K., AOYAMA T., 2016, A Sensorless Approach for Tool Fracture Detection in Milling by Integrating Multi-Axial Servo Information, CIRP Annals, 65/1, 385–388.
• [9] ALTINTAS Y., ASLAN D., 2017, Integration of Virtual and Online Machining Process Control and Monitoring, CIRP Annals, 66/1, 349–352.
• [10] POSTEL M., ASLAN D., WEGENER K. ALTINTAS Y., 2019, Monitoring of Vibrations and Cutting Forces with Spindle Mounted Vibration Sensors, CIRP Annals, 68, 413–416.
• [11] ZHONG R.Y., XU X., KLOTZ E., NEWMAN S.T., 2017, Intelligent Manufacturing in the Context of Industry 4.0: A review, Engineering, 3/5, 616–630.
• [12] WEGENER K., GITTLER T., WEISS L., 2018, Dawn of New Machining Concepts: Compensated, Intelligent, Bioinspired, Procedia CIRP, 77, 1–17.
• [13] LIU X., MAO X., HE Y., LIU H., FAN W., LI B., 2016, A New Approach to Identify the Ball Screw Wear Based on Feed Motor Current, Proceedings of the International Conference on Artificial Intelligence and Robotics and the International Conference on Automation, Control and Robotics Engineering, 1–5.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).