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Tool flank wear prediction in CNC turning of 7075 AL alloy SiC composite

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Abstract

Flank wear occurs on the relief face of the tool and the life of a tool used in a machining process depends upon the amount of flank wear; so predicting of flank wear is an important requirement for higher productivity and product quality. In the present work, the effects of feed, depth of cut and cutting speed on flank wear of tungsten carbide and polycrystalline diamond (PCD) inserts in CNC turning of 7075 AL alloy with 10 wt% SiC composite are studied; also artificial neural network (ANN) and co-active neuro fuzzy inference system (CANFIS) are used to predict the flank wear of tungsten carbide and PCD inserts. The feed, depth of cut and cutting speed are selected as the input variables and artificial neural network and co-active neuro fuzzy inference system model are designed with two output variables. The comparison between the results of the presented models shows that the artificial neural network with the average relative prediction error of 1.03% for flank wear values of tungsten carbide inserts and 1.7% for flank wear values of PCD inserts is more accurate and can be utilized effectively for the prediction of flank wear in CNC turning of 7075 AL alloy SiC composite. It is also found that the tungsten carbide insert flank wear can be predicted with less error than PCD flank wear insert using ANN. With Regard to the effect of the cutting parameters on the flank wear, it is found that the increase of the feed, depth of cut and cutting speed increases the flank wear. Also the feed and depth of cut are the most effective parameters on the flank wear and the cutting speed has lesser effect.

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References

  1. Bhushan RK, Kumar S, Das S (2010) Effect of machining parameters on surface roughness and tool wear for 7075 Al alloy SiC composite. Int J Adv Manuf Technol. doi:10.1007/s00170-010-2529-2

  2. Palanisamy P, Rajendran I, Shanmugasundaram S (2008) Prediction of tool wears using regression and ANN models in end-milling operation. Int J Adv Manuf Technol 37:29–41

    Article  Google Scholar 

  3. Sharma VS, Sharma SK, Sharma AK (2008) Cutting tool wear estimation for turning. J Intell Manuf 19:99–108

    Article  Google Scholar 

  4. Chandrasekaran M, Muralidhar M, Murali Krishna C, Dixit US (2009) Application of soft computing techniques in machining performance prediction and optimization: a literature review. Int J Adv Manuf Technol. doi:10.1007/s00170-009-2104-x

  5. Chen JC, Chen JC (2005) An artificial-neural-networks-based in-process tool wears prediction system in milling operations. Int J Adv Manuf Technol 25:427–434

    Article  Google Scholar 

  6. Jemielniak K, Kwiatkowski L, Wrzosek P (1998) Diagnosis of tool wear based on cutting forces and acoustic emission measures as inputs to a neural network. J Intell Manuf 9:447–455

    Article  Google Scholar 

  7. Seeman M, Ganesan G, Karthikeyan R, Velayudham A (2009) Study on tool wear and surface roughness in machining of particulate aluminum metal matrix composite-response surface methodology approach. Int J Adv Manuf Technol. doi:10.1007/s00170-009-2297-z

  8. Haber RE, Alique A, Alique JR (2002) Tool wear prediction in milling using neural networks. ICANN 2002, LNCS 2415, pp 807–812

  9. Dong J, Subrahmanyam KVR, Wong YS, Hong GS, Mohanty AR (2006) Bayesian-inference-based neural networks for tool wear estimation. Int J Adv Manuf Technol 30:797–807

    Article  Google Scholar 

  10. Ming L, Xiaohong Y, Shuzi Y (1999) Tool wear length estimation with a self learning fuzzy inference algorithm in finish milling. Int J Adv Manuf Technol 15:537–545

    Article  Google Scholar 

  11. Kumanan S, Jesuthanam CP, Ashok Kumar R (2008) Application of multiple regression and adaptive neuro fuzzy inference system for the prediction of surface roughness. Int J Adv Manuf Technol 35:778–788

    Article  Google Scholar 

  12. Dweiri F, Al-Jarrah M, Al-Wedyan H (2003) Fuzzy surface roughness modelling of CNC down milling of Alumic-79. J Mater Process Technol 133:266–275

    Article  Google Scholar 

  13. Caydas U, Hascalik A, Ekici S (2009) An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM. Exp Sys with Appl 36:6135–6139

    Article  Google Scholar 

  14. Mat darus IZ, Tokhi MO (2005) Soft computing-based active vibration control of a flexible structure. Eng Appl Artif Intel 18:93–114

    Article  Google Scholar 

  15. Gölo˘glu C, Arslan Y (2009) Zigzag machining surface roughness modelling using evolutionary approach. J Intell Manuf 20:203–210

    Article  Google Scholar 

  16. Tsai KM, Wang PJ (2001) Predictions on surface finish in electrical discharge machining based upon neural network models. Int J Mach Tools Manuf 41:1385–1403

    Article  Google Scholar 

  17. Wong JT, Chen KH, Su CT (2008) Designing a system for a process parameter determined through modified PSO and fuzzy neural network. PAKDD 2008, LNAI 5012, pp 785–794

  18. Tsai KM, Wang PJ (2001) Comparisons of neural network models on material removal rate in electrical discharge machining. J Mater Process Technol 117:111–124

    Article  Google Scholar 

  19. Li X, Guan X, Li Y (2004) A hybrid radial basis function neural network for dimensional error prediction in end milling. ISNN 2004, LNCS 3174, pp 743–748

  20. Uros Z, Frank C, Edi K (2009) Adaptive network based inference system for estimation of flank wear in end-milling. J Mater Process Technol 209:1504–1511

    Article  Google Scholar 

  21. Balazinski M, Czogala E, Jemielniak K, Leski J (2005) Tool condition monitoring using artificial intelligence methods. Eng Appl Artif Intell 15:73–80

    Article  Google Scholar 

  22. Gajate1 A, Haber RE, Alique JR, Vega PI (2009) Transductive-weighted neuro-fuzzy inference system for tool wear prediction in a turning process. HAIS 2009, LNAI 5572, pp 113–120

  23. Kuo RJ (2000) Multi-sensor integration for on-line tool wears estimation through artificial neural networks and fuzzy neural network. Eng Appl Artif Intell 3:49–261

    Google Scholar 

  24. Achiche S, Balazinski M, Baron L, Jemielniak K (2004) Tool wears monitoring using genetically-generated fuzzy knowledge bases. Eng Appl Artif Intell 15:303–314

    Article  Google Scholar 

  25. Hardalac F et al (2004) The examination of the effects of obesity on a number of arteries and body mass index by using expert systems. J Med Syst 28:129–142

    Article  Google Scholar 

  26. Dinh NQ, Afzulpurkar NV (2007) Neuro-fuzzy MIMO nonlinear control for ceramic roller kiln. Simu Model Prac Theo 15:1239–1258

    Article  Google Scholar 

  27. Saemi M, Ahmadi M (2008) Integration of genetic algorithm and a coactive neuro-fuzzy inference system for permeability prediction from well logs data. Transp Porous Med 71:273–288

    Article  Google Scholar 

  28. Mintz R, Young BR, Svrcek WY (2005) Fuzzy logic modeling of surface ozone concentrations. Comp Chem Eng 29:2049–2059

    Article  Google Scholar 

  29. Camps-Valls G et al (2003) Support vector machines for crop classification using hyperspectral data. IbPRIA 2003, LNCS 2652, pp 134–141

  30. Chen J, Roberts C, Weston P (2008) Fault detection and diagnosis for railway track circuits using neuro-fuzzy systems. Cont Eng Prac 16:585–596

    Article  Google Scholar 

  31. Aytek A (2009) Co-active neurofuzzy inference system for evapotranspiration modeling. Soft Comput 13:691–700

    Article  Google Scholar 

  32. Singh TN, Verma AK, Sharma PK (2007) A neuro-genetic approach for prediction of time dependent deformational characteristic of rock and its sensitivity analysis. Geotech Geol Eng 25:395–407

    Article  Google Scholar 

  33. Antony J (2003) Design of experiments for engineers and scientists. ISBN: 0750647094. Elsevier Science & Technology Books

  34. Lippman RP (1987) An introduction to computing with neural nets. IEEE ASSP Mag 4:4–22

    Article  Google Scholar 

  35. Anderson D, McNeil G (1992) Artificial neural network technology. Kaman Sciences Corporation

  36. Klimasauskas CC (1991) Neural computing. Neural–Ware, Pittsburgh

    Google Scholar 

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Authors and Affiliations

  1. Manufacturing Engineering Division, Department of Engineering and High-Tech, Iran University of Industries and Mines, Tehran, Iran

    Saeed Zare Chavoshi

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  1. Saeed Zare Chavoshi
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Zare Chavoshi, S. Tool flank wear prediction in CNC turning of 7075 AL alloy SiC composite. Prod. Eng. Res. Devel. 5, 37–47 (2011). https://doi.org/10.1007/s11740-010-0282-x

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