CAJ | 학술논문

选择性激光烧结的烧结件质量预测是一个多变量、非线性的问题,采用传统的方法很难得到满意的结果.采用BP神经网络模型,在数值模拟取样的基础上,建立了烧结件质量的神经网络预测模型.该模型确定了工艺参数激光功率P、扫描速度v和预热温度T 0与烧结宽度和烧结深度的关系.其预测结果与数值模拟结果相一致,说明该神经网络模型能定量地反映出工艺参数与烧结件质量之间的关系,据此可合理选择加工工艺参数.
선택성격광소결적소결건질량예측시일개다변량、비선성적문제,채용전통적방법흔난득도만의적결과.채용BP신경망락모형,재수치모의취양적기출상,건립료소결건질량적신경망락예측모형.해모형학정료공예삼수격광공솔P、소묘속도v화예열온도T 0여소결관도화소결심도적관계.기예측결과여수치모의결과상일치,설명해신경망락모형능정량지반영출공예삼수여소결건질량지간적관계,거차가합리선택가공공예삼수.
The quality prediction of sintered parts was a multi-variable and nonlinear problem in selective laser sintering (SLS), which was difficult to obtain satisfactory results by the traditional methods. By adopting BP neural network model,a neural network prediction model for the quality of SLS part was established based on the sampling of numerical simulation,which was used to deter-mine the relation between processing parameters including the laser power,scanning speed and preheating temperature,and quality at-tributes including sintering width and sintering depth. The prediction results are in consistent with the numerical simulation result, which show that the neural network model might be used to reflect the relationship quantitatively between process parameters and quality of SLS parts. So it is a valuable guide to select appropriate process parameters.