Advertisement

Geometry Characteristics Prediction of Single Track Cladding Deposited by High Power Diode Laser Based on Genetic Algorithm and Neural Network

  • Huaming Liu
  • Xunpeng Qin
  • Song Huang
  • Lei Jin
  • Yongliang Wang
  • Kaiyun Lei
Regular Paper
  • 37 Downloads

Abstract

This paper aims to establish a correlation between the process parameters and geometrical characteristics of the sectional profile of the single track cladding deposited by high power diode laser with rectangle beam spot. By applying the genetic algorithm and back propagation neural network, a nonlinear model for predicting the geometry features of the single track cladding is developed. A full factorial design method is used to conduct the experiments, and the experimental results are chosen randomly as training dataset and testing dataset for the neural network. Three main input variables such as laser power, scanning speed, and powder thickness were considered. The performance of the genetic algorithm and back propagation artificial neural network was compared to that of the standard back propagation neural network. To improve the accuracy of the neural network, one-hidden-layer and double-hidden-layer neural network with different architectures were performed. Further, one-output and multi-output neural network are also trained and tested. The results indicate that, by using genetic algorithm, the prediction accuracy of the neural network is significantly improved. Meanwhile, the double-hidden-neural network has higher prediction accuracy than the one-hidden-layer-neural network, while the one-output-neural network has higher prediction accuracy than the multi-output-neural network.

Keywords

Single track cladding High power diode laser Genetic algorithm Back propagation artificial neural network Full factorial design 

NOMENCLATURE

STC

single track cladding

GA

genetic algorithm

BPNN

back propagation neural network

ANN

artificial neural network

FFD

full factorial design

HPDL

high power diode laser

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Wang, D., Hu, Q., Zheng, Y., Xie, Y., and Zeng, X., “Study on Deposition Rate and Laser Energy Efficiency of Laser-Induction Hybrid Cladding,” Optics & Laser Technology, Vol. 77, pp. 16–22, 2016.CrossRefGoogle Scholar
  2. 2.
    Abioye, T. E., McCartney, D. G., and Clare, A. T., “Laser Cladding of Inconel 625 Wire for Corrosion Protection,” Journal of Materials Processing Technology, Vol. 217, pp. 232–240, 2015.CrossRefGoogle Scholar
  3. 3.
    Akbari, M., Saedodin, S., Panjehpour, A., Hassani, M., Afrand, M., and Torkamany, M. J., “Numerical Simulation and Designing Artificial Neural Network for Estimating Melt Pool Geometry and Temperature Distribution in Laser Welding of Ti6Al4V Alloy,” Optik -International Journal for Light and Electron Optics, Vol. 127, No. 23, pp. 11161–11172, 2016.CrossRefGoogle Scholar
  4. 4.
    Mondal, S., Bandyopadhyay, A., and Pal, P. K., “Application of Artificial Neural Network for the Prediction of Laser Cladding Process Characteristics at Taguchi-Based Optimized Condition,” The International Journal of Advanced Manufacturing Technology, Vol. 70, Nos. 7-12, pp. 2151–2158, 2014.CrossRefGoogle Scholar
  5. 5.
    Liu, N., Yang, H., Li, H., Yan, S., Zhang, H., and Tang, W., “BP Artificial Neural Network Modeling for Accurate Radius Prediction and Application in Incremental In-Plane Bending,” The International Journal of Advanced Manufacturing Technology, Vol. 80, Nos. 5-8, pp. 971–984, 2015.CrossRefGoogle Scholar
  6. 6.
    Acherjee, B., Mondal, S., Tudu, B., and Misra, D., “Application of Artificial Neural Network for Predicting Weld Quality in Laser Transmission Welding of Thermoplastics,” Applied Soft Computing, Vol. 11, No. 2, pp. 2548–2555, 2011.CrossRefGoogle Scholar
  7. 7.
    Nagesh, D. S. and Datta, G. L., “Prediction of Weld Bead Geometry and Penetration in Shielded Metal-Arc Welding Using Artificial Neural Networks,” Journal of Materials Processing Technology, Vol. 123, No. 2, pp. 303–312, 2002.CrossRefGoogle Scholar
  8. 8.
    Muthukrishnan, N. and Davim, J. P., “Optimization of Machining Parameters of Al/SiC-MMC with ANOVA and ANN Analysis,” Journal of Materials Processing Technology Vol. 209, No. 1, pp. 225–232, 2009.CrossRefGoogle Scholar
  9. 9.
    Cevik, A., Kutuk, M. A., Erklig, A., and Guzelbey, I. H., “Neural Network Modeling of Arc Spot Welding,” Journal of Materials Processing Technology, Vol. 202, Nos. 1-3, pp. 137–144, 2008.CrossRefGoogle Scholar
  10. 10.
    Yin, F., Mao, H., Hua, L., Guo, W., and Shu, M,. “Back Propagation Neural Network Modeling for Warpage Prediction and Optimization of Plastic Products during Injection Molding,” Materials & Design, Vol. 32, No. 4, pp. 1844–1850, 2011.CrossRefGoogle Scholar
  11. 11.
    Yildiz, A. R., “Comparison of Evolutionary-Based Optimization Algorithms for Structural Design Optimization,” Engineering Applications of Artificial Intelligence, Vol. 26, No. 1, pp. 327–333, 2013.CrossRefGoogle Scholar
  12. 12.
    Yildiz, A. R. and Saitou, K., “Topology Synthesis of Multicomponent Structural Assemblies in Continuum Domains,” Journal of Mechanical Design, Vol. 133, No. 1, pp. 788–796, 2008.Google Scholar
  13. 13.
    Yildiz, A. R., “A Novel Particle Swarm Optimization Approach for Product Design and Manufacturing,” International Journal of Advanced Manufacturing Technology, Vol. 40, Nos. 5-6, pp. 617–628, 2009.CrossRefGoogle Scholar
  14. 14.
    Yildiz, A. R., “A New Hybrid Artificial Bee Colony Algorithm for Robust Optimal Design and Manufacturing,” Applied Soft Computing Journal, Vol. 13, No. 5, pp. 2906–2912, 2013.CrossRefGoogle Scholar
  15. 15.
    Yin, F., Mao, H., and Hua, L., “A Hybrid of Back Propagation Neural Network and Genetic Algorithm for Optimization of Injection Molding Process Parameters,” Materials & Design, Vol. 32, No. 6, pp. 3457–3464, 2011.CrossRefGoogle Scholar
  16. 16.
    Zhong, Y., Xue, K., and Shi, D., “An Improved Artificial Neural Network for Laser Welding Parameter Selection and Prediction,” The International Journal of Advanced Manufacturing Technology, Vol. 68, Nos. 1-4, pp. 755–762, 2013.CrossRefGoogle Scholar
  17. 17.
    Sathiya, P., Panneerselvam, K., and Soundararajan, R., “Optimal Design for Laser Beam Butt Welding Process Parameter Using Artificial Neural Networks and Genetic Algorithm for Super Austenitic Stainless Steel,” Optics & Laser Technology, Vol. 44, No. 6, pp. 1905–1914, 2012.CrossRefGoogle Scholar
  18. 18.
    Sathiya, P., Panneerselvam, K., and Abdul-Jaleel, M. Y., “Optimization of Laser Welding Process Parameters for Super Austenitic Stainless Steel Using Artificial Neural Networks and Genetic Algorithm,” Materials & Design, Vol. 36, pp. 490–498, 2012.CrossRefGoogle Scholar
  19. 19.
    Wang, X., Zhang, C., Li, P., Wang, K., Zhang, P., and Liu, H., “Modeling and Optimization of Joint Quality for Laser Transmission Joint of Thermoplastic Using an Artificial Neural Network and a Genetic Algorithm,” Optics and Lasers in Engineering, Vol. 50, No. 11, pp. 1522–1532, 2012.CrossRefGoogle Scholar
  20. 20.
    Fu, Z. and Mo, J., “Springback Prediction of High-strength Sheet Metal under Air Bending Forming and Tool Design Based on GABPNN,” International Journal of Advanced Manufacturing Technology, Vol. 53, Nos. 5-8, pp. 473–483, 2011.CrossRefGoogle Scholar
  21. 21.
    Riquelme, A., Rodrigo, P., Escalera-Rodríguez, M. D., and Rams, J., “Analysis and Optimization of Process Parameters in Al-SiCp Laser Cladding,” Optics and Lasers in Engineering, Vol. 78, pp. 165–173, 2016.CrossRefGoogle Scholar
  22. 22.
    Farahmand, P. and Kovacevic, R., “Parametric Study and Multi-Criteria Optimization in Laser Cladding by a High Power Direct Diode Laser,” Lasers in Manufacturing and Materials Processing Vol. 1, Nos. 1-4, pp. 1–20, 2014.CrossRefGoogle Scholar
  23. 23.
    Rumelhart, D. E. and McClelland, J. L., “Parallel Distribution Processing Explorations in the Microstructure of Cognition,” MIT Press, Boston, USA, 1986.Google Scholar
  24. 24.
    Kiani, M. and Yildiz, A. R., “A Comparative Study of Non-Traditional Methods for Vehicle Vrashworthiness and NVH Optimization,” Archives of Computational Methods in Engineering, Vol. 23, No. 4, pp. 723–734, 2016.MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Korean Society for Precision Engineering and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Hubei Key Laboratory of Advanced Technology for Automotive ComponentsWuhan University of TechnologyWuhanChina
  2. 2.Hubei Collaborative Innovation Center for Automotive Components TechnologyWuhan University of TechnologyWuhanChina
  3. 3.School of Automotive EngineeringWuhan University of TechnologyWuhanChina

Personalised recommendations