Advanced SearchSearch Tips
Optimization of Build Parameters in SLS Process
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Optimization of Build Parameters in SLS Process
Heo, Seong-Min; O, Do-Geun; Choe, Gyeong-Hyeon; Lee, Seok-Hui;
  PDF(new window)
RP(Rapid Prototyping) technology is gaining its popularity in building a prototype in all industries. SLS(Slective Laser Sintering) is one of RP technologies, which is focused on tooling processes as well as three dimension solid model. There are several factors, the length and the cross-sectional area of a part, that have an effect on build setup in SLS process. In this paper, the computation on geometrical relationship is used to slice STL file and to estimate these factors. Based on these values, the build setup parameters such as the heating temperature, the laser power, and the powder cartridge feed rate are determined by neural network approaches. The test results show that the computation time is saved and the neural network approach is able to apply to get the optimal parameters of build process within an acceptable error rate.
RP;SLS;Build Parameter;STL File;Slicing;Neural Network;
 Cited by
Turner I. Y., Thompson D. C, Wood K. L. and Crawford R. H., 1998, 'Characterization of Surface Fault Patterns with Application to a Layered Manufacturing Process,' Journal of Manufacturing Systems, Vol. 17, No. 1, pp. 23-36

Sabourin E., 1996, 'Adaptive High-Precision Exterior, High-Speed Interior, Layered Manufacturing,' M.S. Thesis, Virginia Polytechnic Institute and State Univ., pp. 3-27

Dolenc A. and Makeya I., 1994, 'Slicing Procedures for Layered Manufacturing Techniques,' Computer Aided Design, Vol. 26, NO. 2, pp. 119-126 crossref(new window)

Suh Y. and Wozny M. J., 1994, 'Adaptive Slicing of Solid Freeform Fabrication Proce- sses,' Solid Freeform Fabrication Symposium Proceedings, University of Texas at Austin, pp. 404-411

강신민, 1996, 미적분학, 경문사, 서울, pp. 1024-1028

Choi K. H., 1996, 'Neural Networks Approach to the Determination of the Machining Parameters,' KSME Journal, Vol. 10, No. 4, pp. 389-395

Burke L. and Kamal S., 1995, 'Neural Networks and the Part Family/Machine Group Formation Problem in Cellular Manufacturing : A Framework Using Fuzzy ART,' Journal of Manufacturing Systems, Vol. 14, No. 3, pp. 148-159