Abstract
Selective laser sintering (SLS), one of rapid prototy** technologies, employs laser beam to selectively fuse fully powder into a solid object layer by layer. However, density prediction of SLS parts using finite elements analysis (FEA) having been reported, heavily depends on the precision of the FEA model. An Artificial neural network (ANN) approach presented in this paper has been developed for density prediction of SLS parts. Two-layer supervised neural networks are used, and the inputs to the neural network are known SLS process parameters such as laser power, scan speed, scan spacing and layer thickness. Orthogonal experimental method is employed for collection of experimental training and test sets. The construction of network is also investigated. Comparison of predicted and experimental data has confirmed the accuracy of the ANN approach.
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© 2004 Springer-Verlag Berlin Heidelberg
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Shen, X., Yao, J., Wang, Y., Yang, J. (2004). Density Prediction of Selective Laser Sintering Parts Based on Artificial Neural Network. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_133
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DOI: https://doi.org/10.1007/978-3-540-28648-6_133
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22843-1
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