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Estimating high precision hole diameters of aerospace alloys using artificial intelligence systems: a comparative analysis of different techniques

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Abstract

Monitoring metal removal in machining processes has proved to be essential for companies seeking a high level of excellence in the quality of their products and processes, contributing to improved resource allocation and reduced wastage due to nonconforming parts. Multisensory approaches have been employed to monitor these processes, aiming to use signals to train artificial intelligence systems to perform the task of indicating nonconformities in the tools or the product being manufactured. In this study, three artificial intelligence systems were used to estimate diameter of holes produced in sandwich plates—Ti6Al4V alloy was mounted in AA 2024-T3 alloy—and cutting conditions were selected to simulate a common aircraft fuselage manufacturing process. A multilayer perceptron artificial neural network (MLP ANN), an adaptive neuro-fuzzy inference system (ANFIS) and a radial basis function (RBF) neural network were trained to estimate the diameter of machined holes. The multisensory approach includes an acoustic emission sensor, accelerometer, dynamometer and an electric power sensor. The optimum configuration for each artificial intelligence system was determined based on algorithms designed to examine the influence of each system’s signals and specific parameters on the final result of the estimate. The results indicated the MLP ANN was more robust in withstanding data variation. The ANFIS system and RBF network showed markedly varying results in response to variations in the obtained data during training, suggesting these systems should always be trained with the dataset presented in the same order. A satisfactory response between the multisensory approach and MLP network was observed. The vertical component of force, along the z axis, was the only parameter able to present valid results for all the artificial intelligence systems analysed.

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References

  1. Kamen EW (1999) Industrial controls and manufacturing. Academic Press, San Diego

    Google Scholar 

  2. Huang SH, Zhang HC (1994) Artificial neural networks in manufacturing: concepts, applications, and perspectives. IEEE Trans Compon Packag Manuf Technol Part A 17(2):212–228

    Article  Google Scholar 

  3. Matsushima M, Kawai N, Fujie H, Yasuda K (2010) Visual inspection of soldering joints by neural network with multi-angle view and principal component analysis. In: Shirase K, Aoyagi S (eds) Service robotics and mechatronics. Springer, London, pp 329–334

    Google Scholar 

  4. Aguiar PR, Paula WCF, Bianchi EC, Ulson JAC, Cruz CED (2010) Analysis of forecasting capabilities of ground surfaces valuation using artificial neural networks. J Braz Soc Mech Sci Eng 32(2):146–153

    Google Scholar 

  5. Cruz CED, Aguiar PR, Machado AR, Bianchi EC, Contrucci JG, Castro Neto F (2013) Monitoring in precision metal drilling process using multi-sensors and neural network. Int J Adv Manuf Technol 66:151–158

    Article  Google Scholar 

  6. Rangwala S, Dornfeld. DA (1987) Integration of sensors via neural networks for detection of tool wear states intelligent and integrated manufacturing analysis and synthesis. In: Winter Meeting of the ASME, Boston, pp 109–120

  7. Yang X, Kumehara H, Zhang W (2009) Back propagation wavelet neural network based prediction of drill wear from thrust force and cutting torque signals. Comput Inf Sci 2(3):75–86

    Google Scholar 

  8. Li X, Tso SK (1999) Drill wear monitoring based on current signals. Wear 231(2):172–178

    Article  Google Scholar 

  9. Abu-Mahfouz I (2003) Drilling wear detection and classification using vibration signals and artificial neural network. Int J Mach Tools Manuf 43(7):707–720

    Article  Google Scholar 

  10. Kandilli I, Sonmez M, Ertunc HM, Cakir B (2007) Online monitoring of tool wear in drilling and milling by multi-sensor neural network fusion. In: International conference on mechatronics and automation. IEEE, pp 1388–1394

  11. Youssef HA, El-Hofy H (2008) Machining technology: machine tools and operations. CRC Press, Boca Raton

    Book  Google Scholar 

  12. Monostori L, Váncza J, Kumara SRT (2006) Agent-based systems for manufacturing. CIRP Ann Manuf Technol 55(2):697–720

    Article  Google Scholar 

  13. Liang SY, Hecker RL, Landers RG (2004) Machining process monitoring and control: the state-of-the-art. J Manuf Sci Eng 126(2):297

    Article  Google Scholar 

  14. Davim JP (2008) Machining: fundamentals and recent advances, 2nd edn. Springer, London

    Google Scholar 

  15. Haykin S (1999) Neural networks: a comprehensive foundation, 2nd edn. Pearson Prentice Hall, Delhi

    MATH  Google Scholar 

  16. Wilamowski BM, Irwin JD (2011) The industrial electronics handbook: intelligent systems. CRC Press, Boca Raton

    Google Scholar 

  17. Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    Article  MathSciNet  MATH  Google Scholar 

  18. Zadeh LA (1994) Fuzzy logic, neural networks, and soft computing. Commun ACM 37(3):77–84

    Article  MathSciNet  Google Scholar 

  19. Dote Y, Ovaska SJ (2001) Industrial applications of soft computing: a review. Proc IEEE 89(9):1243–1265

    Article  Google Scholar 

  20. Chinnam RB, Ding J, May GS (2000) Intelligent quality controllers for on-line parameter design. IEEE Trans Semicond Manuf 13(4):481–491

    Article  Google Scholar 

  21. Hermann G (2003) Application of neural network based sensor fusion in drill monitoring. In: 1st Slovakian-Hungarian joint simposium on applied machine intelligence. Budapest polytechnic, annals of conference, pp 1–14

  22. Bustillo A, Correa M, Reñones A (2011) A virtual sensor for online fault detection of multitooth-tools. Sensors 11:2773–2795

    Article  Google Scholar 

  23. Abellan-Nebot JV, Romero Subirón F (2009) A review of machining monitoring systems based on artificial intelligence process models. Int J Adv Manuf Technol 47(1–4):237–257

    Google Scholar 

  24. Bustillo A, Correa M (2012) Using artificial intelligence to predict surface roughness in deep drilling of steel components. J Intell Manuf 23(5):1893–1902

    Article  Google Scholar 

  25. Ferreiro S, Sierra B, Irigoien I, Gorritxategi N (2011) Data mining for quality control: burr detection in the drilling process. Comput Ind Eng 60:801–810

    Article  Google Scholar 

  26. Grzenda M, Bustillo A, Zawistowski P (2012) A soft computing system using intelligent imputation strategies for roughness prediction in deep drilling. J Intell Manuf 23:1733–1743

    Article  Google Scholar 

  27. Wu X, Liu J (2009) A new early stopping algorithm for improving neural network generalization. In: Proceedings of the second international conference on intelligent computation technology and automation. IEEE, vol 1, pp 15–18

  28. Brazilian Association of Technical Standards (1995) ABNT NBR 6158: System of limits and fits—Procedure. Brazilian Standard. Rio de Janeiro, 79 [IN PORTUGUESE: ASSOCIAÇÃO BRASILEIRA DE NORMAS TÉCNICAS—ABNT NBR 6158: Sistema de tolerâncias e ajustes]. Rio de Janeiro, 1995, p 79

  29. Trent EM, Wright PK (2000) Metal cutting, 4th edn. Butterworth-Heinemann, Woburn, p 245

    Google Scholar 

  30. Bustillo A, Díez-Pastor JP, Quintana G, Osorio CG (2001) Avoiding neural network fine tuning by using ensemble learning: application to ball-end milling operations. Int J Adv Manuf Technol 57:521–532

    Article  Google Scholar 

  31. Vaxevanidis NM, Kechagias JD, Fountas NA, Manolakos DE (2013) Three component cutting force system modelling and optimization in turning of AISI D6 tool steel using design of experiments and neural network. In: Proceedings of the World Congress on Engineering 2013 vol I, WCE 2013, London, UK

  32. Noori-Khajavi A, Komanduri R (1993) On multisensor approach to drill wear monitoring. Ann CIRP 42(1):71–74

    Article  Google Scholar 

  33. Patten JA, Jacob J, Bhattacharya B, Grevstad A, Fang N, Marsh ER (2007) Numerical simulations and cutting experiments on single point diamond machining of semiconductors and ceramics, semiconductor machining at the micro-nano scale, chapter 2. Transw Res Netw Kerala India 2:1–36

    Google Scholar 

  34. Nabhani F (2001) Wear mechanisms of ultra-hard cutting tools materials. J Mater Process Technol 115:402–412

    Article  Google Scholar 

  35. Da Silva RB, Da Silva MB, Sales WF, Ezugwu EO, Machado AR (2016) Advances in the turning of titanium alloys with carbide and superabrasive cutting tools. Adv Mater Res Trans Tech Publ Switz 1135:234–254

    Google Scholar 

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Authors and Affiliations

  1. Faculty of Engineering, UNESP, Paulista State University, Av. Eng. Luiz Edmundo C. Coube 14-01, Bauru, SP, 17033-360, Brazil

    P. R. Aguiar, T. M. Gerônimo, M. N. Franchin & E. C. Bianchi

  2. School of Mechanical Engineering, UFU, Federal University of Uberlandia, Av. Joao Naves de Avila, 2121, Bloco 1-M, Uberlandia, MG, 38400-902, Brazil

    R. B. Da Silva

Authors
  1. P. R. Aguiar
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  2. R. B. Da Silva
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  3. T. M. Gerônimo
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  4. M. N. Franchin
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  5. E. C. Bianchi
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Correspondence to R. B. Da Silva.

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Technical Editor: Márcio Bacci da Silva.

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Aguiar, P.R., Da Silva, R.B., Gerônimo, T.M. et al. Estimating high precision hole diameters of aerospace alloys using artificial intelligence systems: a comparative analysis of different techniques. J Braz. Soc. Mech. Sci. Eng. 39, 127–153 (2017). https://doi.org/10.1007/s40430-016-0525-7

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  • DOI: https://doi.org/10.1007/s40430-016-0525-7

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