Skip to main content

Advertisement

SpringerLink
Log in

Selection of optimum cutting condition of cobalt-based superalloy with GONNS

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Machining of new superalloys is challenging. Automated software environments for determining the optimal cutting conditions after reviewing a set of experimental results are very beneficial to obtain the desired surface quality and to use the machine tools effectively. The genetically optimized neural network system (GONNS) is proposed for the selection of optimal cutting conditions from the experimental data with minimal operator involvement. Genetic algorithm (GA) obtains the optimal operational condition by using the neural networks. A feed-forward backpropagation-type neural network was trained to represent the relationship between surface roughness, cutting force, and machining parameters of face-milling operation. Training data were collected at the symmetric and asymmetric milling operations by using different cutting speeds (V c), feed rates (f), and depth of cuts (a p) without using coolant. The surface roughness (Raasymt, Rasymt) and cutting force (Fxasymt, Fyasymt, Fzasymt, Fxsymt, Fysymt, Fzsymt) were measured for each cutting condition. The surface roughness estimation accuracy of the neural network was better for the asymmetric milling operation with 0.4% and 5% for training and testing data, respectively. For the symmetric milling operations, slightly higher estimation errors were observed around 0.5% and 7% for the training and testing. One parameter was optimized by using the GONNS while all the other parameters, including the cutting forces and the surface roughness, were kept in the desired range.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Agarwal SC, Ocken H (1990) The microstructure and galling wear of a laser-melted cobalt-base hardfacing alloy. Wear 140:223–233

    Article  Google Scholar 

  2. Crook P (1993) Properties and selection: non-ferrous alloys and special-purpose materials 2 (10). Metals handbook, ASM Int., USA, 446

  3. Murray JD, McAlister AJ (1984) Bulletin in alloy phase diagrams, 5:90

  4. Aykut Ş (2005) The investigation of effects of machinability on chip removal parameters for face milling of cobalt-based superalloy steels. Marmara University Institute of Science & Technology, Ph.D. thesis

  5. Kuzucu V, Ceylan M, Celik H, Aksoy İ (1997) Microstructure and phase analyses of Stellite plus 6 wt.% Mo alloy. J Mater Process Technol 69:257–263

    Article  Google Scholar 

  6. Mohamed KE, Gad MMA, Nassef AE, El-Sayed AW (1999) Localized behavior of powder metallurgy processed cobalt-based alloy Stellite 6 in chloride environments. Z Met kd 90:195–201

    Google Scholar 

  7. Ozcelik B, Bayramoglu M (2006) The statistical modeling of surface roughness in high-speed flat end milling. Int J Mach Tools Manuf 46:1395–1402

    Article  Google Scholar 

  8. Diniz AE, Filho JC (1999) Influence of the relative positions of tool and workpiece on tool life, tool wear and surface in the face milling process. Wear 232:67–75

    Article  Google Scholar 

  9. Altıntaş Y (2000) Manufacturing automation. Cambridge University Press, New York, pp 5–6

    Google Scholar 

  10. Bezce CE, Elbestawi MA (2002) A chip formation based analytic force model for oblique cutting. Int J Mach Tools Manuf 42:529–538

    Article  Google Scholar 

  11. Peigne G, Paris H, Brissaud D, Gouskov A (2004) Impact of the cutting dynamics of small radial immersion milling operations on machined surface roughness. Int J Mach Tools Manuf 44:1133–1142

    Article  Google Scholar 

  12. Franco P, Estrems M, Fuara F (2004) Influence of radial and axial run outs on surface roughness in face milling with round insert cutting tools. Int J Mach Tools Manuf 44:1555–1565

    Article  Google Scholar 

  13. Kishawy HA, Dumitrescu M, Ng EG, Elbestawi MA (2005) Effect of coolant strategy on tool performance, chip morphology and surface quality during high-speed machining of A356 aluminum alloy. Int J Mach Tools Manuf 45:219–227

    Article  Google Scholar 

  14. de Souza M, Jr SWF, Santosc SC, Machadod AR (2005) Performance of single Si3N4 and mixed Si3N4CPCBN wiper cutting tools applied to high speed face milling of cast iron. Int J Mach Tools Manuf 45:335–344

    Article  Google Scholar 

  15. Ertakin YM, Kwon Y, Tseng TL (2003) Identification of common sensory features for the control of CNC milling operations under varying cutting conditions. Int J Mach Tools Manuf 43:897–904

    Article  Google Scholar 

  16. Fuht KH, Wu CF (1995) A proposed statistical model for surface quality prediction in end-milling of Al alloy. Int J Mach Tools Manuf 35:1187–1200

    Article  Google Scholar 

  17. Dabade UA, Joshi SS, Ramakrishnan N (2003) Analysis of surface roughness and cross-sectional area while machining with self propelled round inserts milling cutter. J Mater Process Technol 132:305–312

    Article  Google Scholar 

  18. Saglam H, Unuvar A (2003) Tool condition monitoring in milling based on cutting forces by a neural network. Int J Prod Res 41:1519–1532

    Article  Google Scholar 

  19. Benardos PG, Vosniakos GC (2003) Predicting surface roughness in machining: a review. Int J Mach Tools Manuf 43:833–844

    Article  Google Scholar 

  20. Balic J, Korosec M (2002) Intelligent tool path generation for milling of free surfaces using neural networks. Int J Mach Tools Manuf 42:1171–1179

    Article  Google Scholar 

  21. Ozcelik B, Oktem H, Kurtaran H (2005) Optimum surface roughness in end milling Inconel 718 by coupling neural network model and genetic algorithm. Int J Adv Manuf Technol 27:234–241

    Article  Google Scholar 

  22. El-Wahab AI, Kishawy HA (2000) A new method to improve the surface quality during CNC machining. Int J Prod Res 38:3711–3723

    Article  MATH  Google Scholar 

  23. Baek DK, Ko TJ, Kim HS (1997) A dynamic surface roughness model for face milling. Precis Eng 20:171–178

    Article  Google Scholar 

  24. Amiolemhen PE, Ibhadode AOA (2004) Application of genetic algorithms—determination of the optimal machining parameters in the conversion of a cylindrical bar stock into a continuous finished profile. Int J Mach Tools Manuf 44(12/13):1403–1412

    Article  Google Scholar 

  25. Tansel IN, Bao WY, Arkan TT, Kropas-Hughes CV (2002) Genetic algorithm versus neural network in health monitoring applications. Intelligent Engineering Systems Through Artificial Neural Networks 12:255–260

    Google Scholar 

  26. Tansel IN, Bao WY, Reen NS, Kropas-Hughes CV (2005) Genetic tool monitor (GTM) for micro-end-milling operations. Int J Mach Tools Manuf 45:293–299

    Article  Google Scholar 

  27. Cus F, Balic J (2003) Optimization of cutting process by GA approach. Robot Comput-Integr Manuf 19(1–2):113–121

    Article  Google Scholar 

  28. Davim JP, Anto´nio CAC (2001) Optimisation of cutting conditions in machining of aluminium matrix composites using a numerical and experimental model. J Mater Process Technol 112(1):78–82

    Article  Google Scholar 

  29. Khan Z, Prasad B, Singh T (1997) Machining condition optimization by genetic algorithms and simulated annealing. Comput Oper Res 24(7):647–657

    Article  MATH  Google Scholar 

  30. Brezocnik M, Kovacic M (2003) Integrated genetic programming and genetic algorithm approach to predict surface roughness. Mater Manuf Process 18(3):475–491

    Article  Google Scholar 

  31. Chien WT, Tsai CS (2003) 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(1/3):340–345

    Article  Google Scholar 

  32. Tansel IN, Ozcelik B, Baoa WY, Chena P, Rincona D, Yanga SY, Yenilmez A (2006) Selection of optimal cutting conditions by using GONNS. Int J Mach Tools Manuf 46:26–35

    Article  Google Scholar 

  33. Yang SY, Tansel IN, Kropas-Hughes CV (2003) Selection of optimal material and operating conditions in composite manufacturing. Part I: computational tool. Int J Mach Tools Manuf 43:169–173

    Article  Google Scholar 

  34. Yang SY, Girivasan V, Singh NR, Tansel IN, Kropas-Hughes CV (2003) Selection of optimal material and operating conditions in composite manufacturing. Part II: complexity, representation of characteristics and decision making. Int J Mach Tools Manuf 43:175–184

    Article  Google Scholar 

  35. Tansel I N, Yang S Y, Shu C, Bao W Y, Mahendrakar N, Dagli C H, Akay M, Buczak A L, Ersoy O, Fernandez (Eds.) B 1999 Introduction to genetically optimized neural network systems (GONNS). Smart engineering systems: neural networks, fuzzy logic, evolutionary programming, data mining, and rough sets, ASME, New York, 331–336

  36. ISO 8688-1 (1989) Tool life testing in milling, part I, face milling, 1st edition

  37. Lou MS, Chen JC, Li CM (1999) Surface roughness prediction technique for CNC end milling. J Ind Technol 15(1):1–6

    Google Scholar 

  38. Yang JL, Chen JC (2001) A systematic approach for identifying optimum surface roughness performance in end-milling operations. J Ind Technol 17(2):1–8

    Google Scholar 

  39. Bağcı E, Aykut Ş (2006) A study of Taguchi optimization method for identifying optimum surface roughness in CNC face milling of cobalt-based alloy (stellite 6). Int J Adv Manuf Technol 29:940–947

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Engineering and Architecture, Department of Mechanical Engineering, Bitlis Eren University, 1300, Bitlis, Turkey

    Şeref Aykut

  2. Mechanical Department, Technical Education Faculty, Marmara University, Goztepe, Istanbul, Turkey

    Mustafa Demetgul

  3. Mechanical Engineering Department, Florida International University, 10555 West Flagler Street, Miami, FL, 33174, USA

    Ibrahim N. Tansel

Authors
  1. Şeref Aykut
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mustafa Demetgul
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ibrahim N. Tansel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Şeref Aykut.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Aykut, Ş., Demetgul, M. & Tansel, I.N. Selection of optimum cutting condition of cobalt-based superalloy with GONNS. Int J Adv Manuf Technol 46, 957–967 (2010). https://doi.org/10.1007/s00170-009-2165-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-009-2165-x

Keywords

Search

Navigation