Title: ANN model for the prediction of density in Selective Laser Sintering

Authors: Rong-Ji Wang, Jianbing Li, Fenghua Wang, Xinhua Li, Qingding Wu

Addresses: College of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, 410004, PR China. ' College of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, 410004, PR China. ' College of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, 410004, PR China. ' College of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, 410004, PR China. ' College of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, 410004, PR China

Abstract: The effects of process parameters on density of the part prepared by Selective Laser Sintering (SLS) were modelled, using an Artificial Neural Network (ANN) with a feed forward topology and a back propagation algorithm. The inputs of the ANN are the process parameters, including layer thickness, hatch spacing, laser power, scanning speed, temperature of working environment, interval time and scanning mode. The output of the ANN is the density. The experimental investigation results show that the ANN model may be used to analyse the relationship between the process parameters and the density of the SLS part quantitatively. [Received 12 September 2007; Revised 14 March 2009; Accepted 24 March 2009]

Keywords: SLS; selective laser sintering; process parameters; ANNs; artificial neural networks; density; rapid prototyping.

DOI: 10.1504/IJMR.2009.026579

International Journal of Manufacturing Research, 2009 Vol.4 No.3, pp.362 - 373

Available online: 19 Jun 2009 *

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