Geometry Characteristics Prediction of Single Track Cladding Deposited by High Power Diode Laser Based on Genetic Algorithm and Neural Network
- 37 Downloads
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.
KeywordsSingle track cladding High power diode laser Genetic algorithm Back propagation artificial neural network Full factorial design
single track cladding
back propagation neural network
artificial neural network
full factorial design
high power diode laser
Unable to display preview. Download preview PDF.
- 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.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.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
- 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
- 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
- 23.Rumelhart, D. E. and McClelland, J. L., “Parallel Distribution Processing Explorations in the Microstructure of Cognition,” MIT Press, Boston, USA, 1986.Google Scholar