Adaptive network based inference system for estimation of flank wear in end-milling

Journal of Materials Processing Technology

Volumn 209, Issue 3, 2009, Pages 1504-1511

  Uros, Zuperl ^a   Franc, Cus ^a   Edi, Kiker ^a  

^a UNIVERSITY OF MARIBOR   (Slovenia)

Author keywords

ANFIS; End milling; Estimation; Flank wear

Indexed keywords

CUTTING; FUZZY SYSTEMS; LINGUISTICS; NEURAL NETWORKS;

ADAPTIVE NETWORKS; ANFIS; CUTTING FORCE SIGNALS; END-MILLING; FLANK WEAR; FORCE SIGNALS; INFERENCE SYSTEMS; LINGUISTIC MODELS; MACHINING EXPERIMENTS; MILLING CONDITIONS; MILLING PROCESSES; RELIABLE METHODS; TOOL WEARS;

MILLING (MACHINING);

EID: 57949102921     PISSN: 09240136     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jmatprotec.2008.04.002     Document Type: Article

References (20)

1. Achiche S., Balazinski M., Baron L., and Jemielniak K. Tool wear monitoring using genetically-generated fuzzy knowledge bases. Eng. Appl. Artif. Intell. 15 (2004) 303-314
2. Balazinski M., Czogala E., Jemielniak K., and Leski J. Tool condition monitoring using artificial intelligence methods. Eng. Appl. Artif. Intell. 15 (2005) 73-80
3. Bhattacharyya P., Sengupta D., and Mukhopadhyay S. Cutting force-based real-time estimation of tool wear in face milling using a combination of signal processing techniques. Mech. Syst. Signal Process. 21 (2007) 2665-2683
4. Chen J.C., and Susanto V. Fuzzy logic based in-process tool-wear monitoring system in face milling operations. Int. J. Adv. Manuf. Technol. 21 (2003) 1433-3015
5. Chien W.T., and Tsai C.S. The investigation on the prediction of tool wear and the determination of optimum cutting conditions in machining 17-4PH stainless steel. J. Mater. Process. Technol. 140 (2003) 340-345
6. Cus F., and Balic J. Selection of cutting conditions and tool flow in flexible manufacturing system. Int. J. Manuf. Sci. Technol. 2 (2000) 101-106
7. Elanayar S., and Shin Y. Robust tool wear estimation via radial basis function neural networks. J. Dyn. Syst., Meas. Control 117 (2001) 459-467
8. Fu P., and Hope A.D. Intelligent classification of cutting tool wear states. Adv. Neural Netw. 39 (2006) 1611-3349
9. Ghosh N., Ravi Y.B., Patra A., Mukhopadhyay S., Paul S., Mohanty A.R., and Chattopadhyay A.B. Estimation of tool wear during CNC milling using neural network-based sensor fusion. Mech. Syst. Signal Process. 21 (2007) 466-479
10. Haber R.E., and Alique A. Intelligent process supervision for predicting tool wear in machining processes. Mechatronics 13 (2003) 825-849
11. Iqbal A., He N., Dar N.U., and Li L. Comparison of fuzzy expert system based strategies of offline and online estimation of flank wear in hard milling process. Expert Syst. Appl. 33 (2007) 61-66
12. Jang J.R., and Sun C. Functional equivalence between radial basis function and fuzzy inference systems. IEEE Trans. Neural Netw. 4 (1993) 56-159
13. Kuo R.J. Multi-sensor integration for on-line tool wear estimation through artificial neural networks and fuzzy neural network. Eng. Appl. Artif. Intell. 3 (2000) 49-261
14. Leski J., and Czogala E. A new artificial network based inference system with moving consequents in if-then rules and selected applications. FuzzySets Syst. 108 (2001) 289-297
15. Ming L., Xiaohong Y., and Shuzi Y. Tool wear length estimation with a self-learning fuzzy inference algorithm in finish milling. Int. J. Adv. Manuf. Technol. 15 (2001) 1433-1445
16. Morgan G., Cheng R.Q., Altintas Y., and Ridgway K. An expert troubleshooting system for the milling process. Int. J. Mach. Tools Manuf. 47 (2007) 1417-1425
17. Ozcelik B., and Bayramoglu M. The statistical modeling of surface roughness in high-speed flat end milling. Int. J. Mach. Tools Manuf. 46 (2006) 1395-1402
18. Özel T., and Nadgir A. Prediction of flank wear by using back propagation neural network modeling when cutting hardened H-13 steel with chamfered and honed CBN tools. Int. J. Mach. Tools Manuf. 42 (2002) 87-97
19. Sokolowski A. On some aspects of fuzzy logic application in machine monitoring and diagnostics. Eng. Appl. Artif. Intell. 17 (2004) 429-437
20. Zuperl U., Cus F., Mursec B., and Ploj T. A generalized neural network model of ball-end milling force system. J. Mater. Process. Technol. 175 (2006) 98-108