Advertisement

Production Engineering

, Volume 13, Issue 6, pp 635–647 | Cite as

A study on the extreme learning machine based prediction of machining times of the cycloidal gears in CNC milling machines

  • Turan Gurgenc
  • Ferhat Ucar
  • Deniz KorkmazEmail author
  • Cihan Ozel
  • Yunus Ortac
Computer Aided Engineering
  • 185 Downloads

Abstract

In this study, the machining times of the cycloidal gears manufactured in a CNC milling machine according to the radial machining method are investigated by considering the design and manufacturing parameters of the gear. According to these parameters, the machining times of the cycloidal gears manufactured in Dyna 4M CNC milling machine are determined. It is observed from the experimental manufacturing process that the CAD/CAM parameters of these gears significantly affect the machining times of the gear. In addition, obtained machining times are modeled using extreme learning machine (ELM) which is one of the computational intelligence (CI) algorithms. The proposed ELM model is also compared with the feed-forward and back-propagation based artificial neural network (ANN) algorithm. When both CI methods are compared in terms of modeling performances for training and test phases, it is found that ELM prediction method is extremely fast, accurate and gives higher performance according to ANN method. As a result, ELM method is verified to be able to be used safely to obtain a prediction model for the manufacturing process in a variety of CNC machines where many experiments are required.

Keywords

Cycloidal gear CNC milling machine Machining times Extreme learning machine Computational intelligence Model prediction 

Notes

Supplementary material

11740_2019_923_MOESM1_ESM.docx (24 kb)
Supplementary material 1 (DOCX 23 kb)
11740_2019_923_MOESM2_ESM.docx (24 kb)
Supplementary material 2 (DOCX 24 kb)

References

  1. 1.
    Tu TBH, Song M (2016) Analysis and prediction cost of manufacturing process based on process mining. Int Conf Ind Eng Manag Sci Appl 2016:1–5Google Scholar
  2. 2.
    Ciurana J, Garcia-Romeu ML, Castro R, Alberti M (2003) A system based on machined volumes to reduce the number of route sheets in process planning. Comput Ind 51:41–50CrossRefGoogle Scholar
  3. 3.
    Benardos P, Vosniakos G (2002) Prediction of surface roughness in CNC face milling using neural networks and Taguchi’s design of experiments. Robot Comput Integr Manuf 18:343–354CrossRefGoogle Scholar
  4. 4.
    Bouzid W (2005) Cutting parameter optimization to minimize production time in high speed turning. J Mater Process Technol 161:388–395CrossRefGoogle Scholar
  5. 5.
    Arezoo B, Ridgway K, Al-Ahmari AMA (2000) Selection of cutting tools and conditions of machining operations using an expert system. Comput Ind 42:43–58CrossRefGoogle Scholar
  6. 6.
    Davim JP (2001) A note on the determination of optimal cutting conditions for surface finish obtained in turning using design of experiments. J Mater Process Technol 116:305–308CrossRefGoogle Scholar
  7. 7.
    Monreal M, Rodriguez CA (2003) Influence of tool path strategy on the cycle time of high-speed milling. Comput Des 35:395–401Google Scholar
  8. 8.
    Maropoulos P, Baker R, Paramor KY (2000) Integration of tool selection with design: part 2: aggregate machining time estimation. J Mater Process Technol 107:135–142CrossRefGoogle Scholar
  9. 9.
    Ou-Yang C, Lin TS (1997) Developing and integrated framework for feature-based early manufacturing cost estimation. J Adv Manuf Technol 13:618–629CrossRefGoogle Scholar
  10. 10.
    Malakooti B, Deviprasad J (1989) An interactive multiple criteria approach for parameter selection in metal cutting. Oper Res 37:805–818CrossRefGoogle Scholar
  11. 11.
    Panwalkar SS, Rajagopalan R (1992) Single-machine sequencing with controllable processing times. Eur J Oper Res 59:298–302CrossRefGoogle Scholar
  12. 12.
    Ozel C (2012) A study on cutting errors in the tooth profiles of the spur gears manufactured in CNC milling machine. Int J Adv Manuf Technol 59:243–251CrossRefGoogle Scholar
  13. 13.
    Ozel C (2011) Research of production times and cutting of the spur gears by end mill in CNC milling machine. Int J Adv Manuf Technol 54:203–213CrossRefGoogle Scholar
  14. 14.
    Ozel C, Ortac Y (2016) A study on the cutting errors of the tooth profiles of the cycloidal gears manufactured in CNC milling machine. Int J Mater Prod Technol 53:42–60CrossRefGoogle Scholar
  15. 15.
    Lai TS (2006) Design and machining of the epicycloid planet gear of cycloid drives. Int J Adv Manuf Technol 28:665–670CrossRefGoogle Scholar
  16. 16.
    Lin KS, Chan KY, Lee JJ (2018) Kinematic error analysis and tolerance allocation of cycloidal gear reducers. Mech Mach Theory 124:73–91CrossRefGoogle Scholar
  17. 17.
    Ay M (2018) Modelling of the hole quality characteristics by Extreme Learning Machine in fiber laser drilling of Ti-6Al-4V. J Manuf Process 36:138–148CrossRefGoogle Scholar
  18. 18.
    Wang KS, Li Z, Braaten J, Yu Q (2015) Interpretation and compensation of backlash error data in machine centers for intelligent predictive maintenance using ANNs. Adv Manuf 3:97–104CrossRefGoogle Scholar
  19. 19.
    Huang G-B, Zhu Q, Siew C et al (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501CrossRefGoogle Scholar
  20. 20.
    Huang G, Bin Huang G, Song S, You K (2015) Trends in extreme learning machines: a review. Neural Netw 61:32–48CrossRefGoogle Scholar
  21. 21.
    Ozel C, Ortac Y, Gurgenc T (2017) Investigation of the manufacturing of cycloidal gears used in oil pumps with the end mill in cnc milling machines. Sci Eng J Fırat Univ 29:97–110Google Scholar
  22. 22.
    Nasiri S, Khosravani MR, Weinberg K (2017) Fracture mechanics and mechanical fault detection by artificial intelligence methods: a review. Eng Fail Anal 81:270–293CrossRefGoogle Scholar
  23. 23.
    Pimenov DY, Bustillo A, Mikolajczyk T (2018) Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth. J Intell Manuf 29:1045–1061CrossRefGoogle Scholar
  24. 24.
    Ahila R, Sadasivam V, Manimala K (2015) An integrated PSO for parameter determination and feature selection of ELM and its application in classification of power system disturbances. Appl Soft Comput J 32:23–37CrossRefGoogle Scholar
  25. 25.
    Ucar F, Alcin O, Dandil B et al (2018) Power quality event detection using a fast extreme learning machine. Energies 11:145CrossRefGoogle Scholar
  26. 26.
    Moore EH (1920) On the reciprocal of the general algebraic matrix. Bull Am Math Soc 26:394–395Google Scholar
  27. 27.
    Penrose R (1955) A generalized inverse for matrices. Math Proc Cambridge Philos Soc 51:406–413CrossRefGoogle Scholar

Copyright information

© German Academic Society for Production Engineering (WGP) 2019

Authors and Affiliations

  1. 1.Faculty of Technology, Department of Automotive EngineeringFirat UniversityElazigTurkey
  2. 2.Faculty of Technology, Department of Electrical and Electronics EngineeringFirat UniversityElazigTurkey
  3. 3.Faculty of Engineering, Department of Mechanical EngineeringFirat UniversityElazigTurkey

Personalised recommendations