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

Abstract:

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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.

Info:

Periodical:

Advanced Materials Research (Volumes 156-157)

Edited by:

Jingtao Han, Zhengyi Jiang and Sihai Jiao

Pages:

737-741

DOI:

10.4028/www.scientific.net/AMR.156-157.737

Citation:

J. B. Wang et al., "The Process Parameters Modeling and Experimental Study Based on BP Neural Network for Laser Direct Rapid Forming Metal Parts", Advanced Materials Research, Vols. 156-157, pp. 737-741, 2011

Online since:

October 2010

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Price:

$35.00

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