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The Process Parameters Modeling and Experimental Study Based on BP Neural Network for Laser Direct Rapid Forming Metal Parts

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Abstract:

Discussed in detail using BP neural network to establish the quantitative relationship model between the process parameters and components density on the laser direct rapid forming (LDRF) metal parts, in which input of single-pass sintering model is: laser power (P), scanning speed (V ) and powder feeding rate (G), performance indicators to measure the width of the sintered layer (W) and height (H); input of multi-pass multi-sintering model is: P、V、G、scan spacing (D) and layer thick ( ), the performance measure for the density of sintered parts，And neural network simulation results and experimental results are analyzed and compared. The results show that using BP neural network model can quantitative analyze the effect on sintering process parameters and the sintering performance, the model for the optimization of LDRF process parameters has built the foundation.

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Advanced Manufacturing Technology, ICAMMP 2010 View Preview

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Periodical:

Advanced Materials Research (Volumes 156-157)

Pages:

737-741

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.156-157.737

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Online since:

October 2010

Authors:

Jian Bin Wang, Ji Shu Yin, Bing Huang Chen

Keywords:

Laser Direct Rapid Forming, Metal Part, Modeling, Neural Network (NN), Process Parameter

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© 2011 Trans Tech Publications Ltd. All Rights Reserved

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