Selective laser sintering （SLS） is a new process to prepare the polystyrene （PS）/Al2O3 nanocomposites. In this paper, with different laser power and other processing parameters unchanged, the morphology, density a...Selective laser sintering （SLS） is a new process to prepare the polystyrene （PS）/Al2O3 nanocomposites. In this paper, with different laser power and other processing parameters unchanged, the morphology, density and mechanical properties of the sintered specimens were investigated. It was found that nano-sized inorganic particles are uniformly located in the PS matrix and the maximum density of the sintered specimens with pure PS powder reaches 1.07 g/cm^3, higher than 1.04 g/cm^3 that of the sintered specimens with mixture powder. Due to strengthening and toughness of the nano-sized Al2O3 inorganic particles, the maximum notched impact strength and tensile strength of the sintered part mixed with nano-sized inorganic particles are improved greatly from 7.5 to 12.1 kJ/m^2 and from 6.5 to 31.2 MPa, respectively, under the same sintering condition.展开更多
In the present work,a study is made to investigate the effects of process parameters,namely,laser power,scanning speed,hatch spacing, layer thickness and powder temperature, on the tensile strength for selective laser...In the present work,a study is made to investigate the effects of process parameters,namely,laser power,scanning speed,hatch spacing, layer thickness and powder temperature, on the tensile strength for selective laser sintering( SLS) of polystyrene( PS). Artificial neural network( ANN) methodology is employed to develop mathematical relationships between the process parameters and the output variable of the sintering strength. Experimental data are used to train and test the network. The present neural network model is applied to predicting the experimental outcome as a function of input parameters within a specified range. Predicted sintering strength using the trained back propagation( BP) network model showed quite a good agreement with measured ones. The results showed that the networks had high processing speed,the abilities of error-correcting and self-organizing. ANN models had favorable performance and proved to be an applicable tool for predicting sintering strength SLS of PS.展开更多
文摘Selective laser sintering （SLS） is a new process to prepare the polystyrene （PS）/Al2O3 nanocomposites. In this paper, with different laser power and other processing parameters unchanged, the morphology, density and mechanical properties of the sintered specimens were investigated. It was found that nano-sized inorganic particles are uniformly located in the PS matrix and the maximum density of the sintered specimens with pure PS powder reaches 1.07 g/cm^3, higher than 1.04 g/cm^3 that of the sintered specimens with mixture powder. Due to strengthening and toughness of the nano-sized Al2O3 inorganic particles, the maximum notched impact strength and tensile strength of the sintered part mixed with nano-sized inorganic particles are improved greatly from 7.5 to 12.1 kJ/m^2 and from 6.5 to 31.2 MPa, respectively, under the same sintering condition.
基金National Natural Science Foundation of China(No.51475315)Innovative Project on the Integration of Industry,Education and Research of Jiangsu Province,China(No.BY2014059-10)
文摘In the present work,a study is made to investigate the effects of process parameters,namely,laser power,scanning speed,hatch spacing, layer thickness and powder temperature, on the tensile strength for selective laser sintering( SLS) of polystyrene( PS). Artificial neural network( ANN) methodology is employed to develop mathematical relationships between the process parameters and the output variable of the sintering strength. Experimental data are used to train and test the network. The present neural network model is applied to predicting the experimental outcome as a function of input parameters within a specified range. Predicted sintering strength using the trained back propagation( BP) network model showed quite a good agreement with measured ones. The results showed that the networks had high processing speed,the abilities of error-correcting and self-organizing. ANN models had favorable performance and proved to be an applicable tool for predicting sintering strength SLS of PS.
