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Optimization Study on Technical Process of Selective Laser Sintering Based on Simulation

Author: DongQu
Tutor: WangChuanYang
School: Suzhou University
Course: Mechanical and Electronic Engineering
Keywords: Selective Laser Sintering BP neural network Genetic Algorithms Polystyrene Shrinkage The amount of deformation Tensile strength
CLC: TG665
Type: Master's thesis
Year: 2010
Downloads: 61
Quote: 0
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Abstract


Selective laser sintering (Selective Laser Sintering, SLS) is a past two to three years to develop the Rapid Prototyping (Rapid Prototyping, RP) technology and concurrent engineering, and an important part of reverse engineering. With the the SLS technology of development, put forward higher requirements on the the SLS specimens accuracy with strength. Expand the SLS process and the accuracy and strength of the specimen of great theoretical and practical significance. On the basis of a large number of experimental studies, the application of neural network and genetic algorithm SLS process parameters simulation and optimization studies. This work include: First, the SLS process sintered polystyrene (PS) material test research, considering sintered in the sintering process forming stable case, select the laser power, scan speed, scan spacing, single-layer thickness sintering shrinkage of the specimen under different process parameters and the foundation temperature as the main process parameters affect the sintered products and the amount of deformation and tensile strength were extracted for the neural network model and genetic algorithm simulation the test data. Secondly, the sintering process parameters as input and as an output, shrinkage, deformation, tensile strength, respectively, established the shrinkage BP neural network model, the amount of deformation BP neural network model and the tensile strength of the BP neural network model, combined with the test data network model training and testing, and application of the modified model simulation results show that the neural network model can establish a quantitative relationship between the SLS processing parameters and specimen deformation amount of shrinkage and tensile strength. Application of genetic algorithms and combined with the established BP neural network model to optimize the process parameters, The optimum combination of parameters, ie the foundation temperature of 94 ° C, 14W of laser power, scanning speed 1700mm / s, single-layer thickness of 0.16mm, scanning spacing of 0.13mm. The test results show that the use of genetic algorithms to search the optimal solution of the parameters of the SLS process is effective. Finally, the combination of optimized combinations of process parameters, using BP neural network model simulation analysis of the combined effect of a single process parameter changes, and the two process parameters when sintering quality. The predictive capabilities of neural networks is applied to the sintered mass of the test piece by the impact of the process parameters were evaluated, the results show that: the impact of laser power on the shrinkage, followed by the scanning pitch, and the impact of a single-layer thickness of the smallest; deformation amount by the laser power and laser power on the tensile strength of the foundation temperature affect the scanning speed with minimal impact on the amount of deformation; maximum and minimum temperatures foundation.

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CLC: > Industrial Technology > Metallurgy and Metal Craft > Metal cutting and machine tools > Special machine tools and processing > Light processing equipment and processing
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