Simulation tests of adaptive control strategies for CNC machine tools

• Autorzy: Stryczek Roman , Szczepka Wojciech

Identyfikatory

DOI

10.5604/01.3001.0013.2225

• Języki publikacji: EN

Abstrakty

EN

The issue addressed in the article concerns the current needs and possibilities of computer-aided design of adaptive control strategies in machining processes. A simulative method of selecting the adaptive feed control strategy while rough turning materials difficult to machine, effective and inexpensive in its implementation, based on controlling the load placed on the machine's drives, has been presented. The results of a number of virtual tests of the proposed feed control strategy have been included, while paying particular attention to the stability of the machining process during moments of sudden change in the machining allowance. The obtained results meet the accepted quality indicators of the control algorithm. At the same time, the experiences collected by the author while conducting the tests confirmed the complexity of the issue and the resulting necessity to implement a comprehensive simulation testing program.

Słowa kluczowe

EN

adaptive control; processing simulation; virtual reality; intelligent machining

• Wydawca: Wydawnictwo Wrocławskiej Rady FSNT NOT
• Czasopismo: Journal of Machine Engineering
• Rocznik: 2019
• Tom: Vol. 19, No. 2
• Strony: 73--82
• Opis fizyczny: Bibliogr. 16 poz., rys.

Twórcy

autor

Stryczek Roman

• University of Bielsko-Biala, Faculty of Mechanical Engineering and Computer Science, Bielsko-Biala, Poland

autor

Szczepka Wojciech

• Siemens sp. z o. o., Industry Automation and Drive Technologies, Katowice, Poland

Bibliografia

• [1] HASSAN M., SADEK A., ATTIA M.H., THOMSON V., 2018, Intelligent Machining: real-time tool condition monitoring and intelligent adaptive control systems, Journal of Machine Engineering, 18/1, 5–17.
• [2] GRZESIK W., NIESŁONY P., BARTOSZCZUK M., 2009, Modelling of the Cutting Process Analytical and Simulation Methods, Advances in Manufacturing Science and Technology, 33/1, 5–29.
• [3] NIESŁONY P., GRZESIK W., CHUDY R., HABRAT W., 2015, Meshing strategies in FEM simulation of the machining process, Archives of Civil and Mechanical Engineering, 15, 62–70.
• [4] KILIC Z.M., ALTINTAS Y., 2007, Generalized mechanics and dynamics of metal cutting operations for unified simulations, International Journal of Machine Tools & Manufacture, 104, 1–13.
• [5] BRITZ R., MAIER T., SCHWARZ F., ULBRICH H., ZAEH M.F., 2013, Modelling and Simulation-Based Optimization of a Turning, Process Machine Interactions, 361–379.
• [6] TETI R., 2015, Advanced IT Methods of Signal Processing and Decision Making for Zero Defect Manufacturing in Machining, Procedia CIRP, 28, 3–15, doi: 10.1016/j.procir.2015.04.003.
• [7] TETI E., JEMIELNIAK K., O’DONNELL G., DORNFELD D., 2010, Advanced monitoring of machining operations, CIRP Annals – Manufacturing Technology, 59, 717–739.
• [8] ORABY S., ALASKARI A., 2016, Adaptive Control Program for Rough Turning Machining Process, 6th Int'l Conference on Advances in Engineering Sciences and Applied Mathematics (ICAESAM’2016), Kuala Lumpur 18-23, doi.org/10.15242/IIE.E1216006.
• [9] ABELLAN J.V., ROMERO F., SILLER H.R., ESTRUCH A., VILA C., 2008, Adaptive Control Optimization of Cutting Parameters for High Quality Machining Operations based on Neural Networks and Search Algorithms, Advances in Robotics, Automation and Control, Book edited by: Arámburo J. and Ramírez Treviño A., InTech, Vienna, 1–20, doi: 10.5772/5539.
• [10] CHÁVEZ-GARCIA H., CASTILLO-VILLAR K.K., 2018, Simulation-based model for the optimization of machining parameters in a metal-cutting operation, Simulation Modelling Practice and Theory, 68, 204–221.
• [11] ALTINTAS Y., ASLAN D., 2017, Integration of Virtual and On-line Machining Process Control and Monitoring, CIRP Annals – Manufacturing Technology, 66, 349–352.
• [12] LI J.G., ZHAO H., YAO Y.X., LIU C.Q., 2008, Off-line optimization on NC machining based on virtual machining, International Journal of Advanced Manufacturing Technology, 36, 908–917, doi.10.1007/s00170-006-0915-6.
• [13] LAN T.S., 2007, Virtual CNC Machining and Implementation of optimum MRR with Tool Life Control, Journal of Marine Science and Technology, 15/3, 201–209.
• [14] STRYCZEK R., SZCZEPKA W., 2016, Process factors of impact on OEE for lathes for machining of wheelset. Journal of Machine Engineering, 16/3, 126–140.
• [15] ROTAVA J., LOHTANDER M., 2018, Fuzzy feed rate and cutting speed optimization in turning, The International Journal of Advanced Manufacturing Technology, 99/9–12, 2081–2092, doi:10.1007/s00170-018-1845-9.
• [16] LIAN R.J., LIN B.F., HUANG J.H., 2005, A grey prediction fuzzy controller for constant cutting force in turning, International Journal of Machine Tools & Manufacture, 45, 1047–1056.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).