Smart Adaptive CNC Machining - State of the Art

Abstract:

Condition monitoring is becoming popular in industry because of its efficient role in detecting potential failures. The use of condition monitoring techniques will generally improve plant production availability and reduce downtime cost. A reliable adaptive control system can prevent downtime of the machine or avoid unwanted conditions such as chatter vibration, excessive tool wear by allowing the optimum utilization of the tool life. To ensure the quality of machining products, reduce the machining costs and increase the machining efficiency, it is necessary to adjust the machining parameters in real time. A survey of actual researches is presented in this paper in purpose to define new directions of improvement of adaptive control towards smart machining systems.

• Periodical: Applied Mechanics and Materials (Volume 657)
• Pages: 859-863
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.657.859
• Online since: October 2014
• Authors: Anton Mircea Vasiloni*, Mircea Viorel Drăgoi
• Keywords: Adaptive Control, Smart Machining

References

[1] C. Wang, S.B.C. Ghani, K. Cheng, R. Rakovski, Adaptive smart machining based on using constant cutting force and a smart cutting tool, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. Volume 227, Issue 2 (2013).

DOI: 10.1177/0954405412466233

[2] K. Dohyun, J. Doyoung, Fuzzy-logic control of cutting forces in CNC milling processes using motor currents as indirect force sensors, Precision Engineering 35 (2011) 143-152.

DOI: 10.1016/j.precisioneng.2010.09.001

[3] U. Zuperl, F. Cus, M. Milfelner, Fuzzy control strategy for an adaptive force control in end-milling, Journal of Materials Processing Technology 164–165 (2005) 1472-1478.

DOI: 10.1016/j.jmatprotec.2005.02.143

[4] F. Cus, U. Zuperl Zuperl, E. Kiker, M. MIlfelner, Adaptive controller design for federate maximization of machining process, Journal of Achievements in Materials and Manufacturing Engineering. Volume 17, Issue 1-2 (2006) 237-240.

[5] K. Jurrens, J. Soons, R. Ivester, Smart Machining Research at the National Institute of Standards and Technology, Manufacturing Metrology Division, National Institute of Standards and Technology, available at: http: /www. mel. nist. gov/div822/staff/ivester/ SLIM2003_SmartMachining. pdf, accessed: 21. 03. (2013).

[6] S. Atluru, A. Deshpande, S. Huang, J.P. Snyder, Smart machine supervisory system: concept, definition and application, available at: http: /www. home. amit-deshpande. com/mysite/Papers/MFPT2008. pdf, accessed: 21. 03. (2013).

[7] L. Deshayes, L. Welsch, A. Donmez, R. Ivester, D. Gilsinn, R. Rhorer, E. Whitenton, F. Potra, Smart Machining Systems: Issues and Research Trends, CIRP Life Cycle Engineering Seminar, Grenoble, France, 2005, pp.363-380.

DOI: 10.1007/1-4020-4617-0_25

[8] B. Robert, R. Jerard, M. Xu, B. Fussell, Cutting Power Model-Sensor Integration for Tool Condition Monitoring, available at: http: /www. unh. edu/dml/, accessed: 21. 03. (2013).

[9] J.B. Hentza, V.K. Nguyen, W. Maederb, D. Panaresec, J.W. Gunninkd, A. Gontarze, P. Stavropoulosf, K. Hamiltong, J.Y. Hascoëth, An enabling digital foundation towards smart machining, Procedia CIRP. Volume 12 (2013) 240 – 245.

DOI: 10.1016/j.procir.2013.09.042

[10] U. Zuperl, F. Cus, M. Reibenschuh, Neural control strategy of constant cutting force system in end milling, Robotics and Computer-Integrated Manufacturing, Volume 27 (2011) 485–493.

DOI: 10.1016/j.rcim.2010.10.001

[11] S. Ibaraki, T. Shimizu, A long-term control scheme of cutting forces to regulate tool life in end millingprocesses, Precision Engineering, Volume 34 (2010) 675–682.

DOI: 10.1016/j.precisioneng.2010.05.001

[12] K.P. Karunakaran, R. Shringi, A solid model-based off-line adaptive controller for feed rate scheduling for milling process, Journal of materials processing technology, Volume 204 (2008) 384–396.

DOI: 10.1016/j.jmatprotec.2007.11.092

[13] A. Matsubara, S. Ibaraki, Monitoring and Control of Cutting Forces in Machining Processes: A Review, Monitoring and Control of Cutting Forces in Machining Processes, 2009, available at: http: /www. fujipress. jp/finder/xslt. php?mode=present&inputfile=IJATE000300040010. xml, accessed: 21. 03. (2013).

DOI: 10.20965/ijat.2009.p0445

[14] U. Zuperl, F. Cus, M. Reibenschuh, Modeling and adaptive force control of milling by using artificial techniques, Journal of Intelligent Manufacturing. Volume 23 (2012) 1805–1815.

DOI: 10.1007/s10845-010-0487-z

[15] P. Stavropoulos, P. Chantzis, D. Doukas. C. Papacharalampopoulos, A. Chryssolouris, G. Monitoring and control of manufacturing processes: A review, Procedia CIRP, Volume 8 (2013) 421–425.

DOI: 10.1016/j.procir.2013.06.127

[16] B. Denkenaa, F. Flötera, Adaptive Cutting Force Control on a Milling Machine with Hybrid Axis Configuration, Procedia CIRP. 4 (2012) 109-114.

DOI: 10.1016/j.procir.2012.10.020

[17] C.K. Schuyler, M. Xu, R.B. Jerard, B.K. Fussell, Cutting power model-sensor integration for a smart machining system  (Conference Paper), Transactions of the North American Manufacturing Research Institute of SME, Volume 34 (2006) 47-54.

[18] C. C. H Mat, Y. Altintas, Direct Adaptive Cutting Force Control of Milling Processes, Automatica. Vol. 26. No. 5 (1990) 899-902.

DOI: 10.1016/0005-1098(90)90006-4

[19] S. Huang, K. Kiong, T. Geok, S. Hong, Y.S. Wong, Cutting force control of milling machine, Mechatronics. Volume 17 (2007) 533–541.

DOI: 10.1016/j.mechatronics.2007.07.005

[20] F. Ridwan, X. Xu, Advanced CNC system with in-process feed-rate optimization, Robotics and Computer-Integrated Manufacturing. Volume 29 (2013) 12–20.

DOI: 10.1016/j.rcim.2012.04.008

[21] EC FP7 FoFdation Project, Foundation for sustainable factory of the future, available at http: /www. fofdation-project. eu/ accessed: 21. 03. (2013).