3D打印模型特征细节自适应分层优化方法及试验研究

Adaptive slicing optimization method and experimental investigation on feature details of 3D printing models

  • 摘要: 针对3D打印自适应分层技术无法较好地保留模型特征细节的问题,提出3D打印模型特征细节自适应分层优化方法(model details adaptive slice method, MDASM),该方法基于三角面片法向量的自适应分层方法(normal vector of triangular face adaptive slice method, NTASM)来确定不存在模型特征细节的分层厚度。首先,设定角度变化阈值与切平面相邻高度三角面片法向量的角度变化进行比较,识别是否存在垂直尖角特征,并确定垂直尖角特征分层厚度。其次,设定面积变化阈值与切平面封闭轮廓曲线面积进行比较,识别是否存在平行尖角特征,并确定平行尖角特征分层厚度。最后,取3种分层厚度的最小值作为分层厚度。Matlab仿真表明,相较于NTASM,MDASM模型成形精度提高了24.2%。熔融沉积成形(fused deposition modeling, FDM)结果表明,相较于NTASM,MDASM模型成形效率降低了21.8%,表面粗糙度减小了14.57%。仿真和FDM试验研究表明,MDASM在保证模型成形效率的前提下,能够更好地保留模型特征细节,具有更好的模型成形精度和实用性。

     

    Abstract: For the problem that 3D printing adaptive layering techniques do not preserve the details of model features well, the 3D printing model details adaptive slice method (MDASM) is proposed. The thickness of the slice without model feature details is determined by the method that based on normal vector of triangular face slices adaptive slice method (NTASM) to determine. Firstly, the angle change threshold is set and compared with the angle change of the normal vectors of the adjacent height triangular face slices in the tangent plane, so as to discriminate the existence of vertical sharp corner features, and determine the thickness of the vertical sharp corner feature layering. Secondly, the area change threshold is set and compared with the area of the closed contour curve of the tangent plane to determine whether there is a parallel cusp feature and determine the layering thickness of the parallel cusp feature. Finally, the smallest value of the three layering thicknesses is taken as the layering thickness. Matlab simulation shows that the model molding accuracy of MDASM is improved by 24.2% compared with NTASM. Fused deposition modeling (FDM) test results show that the model molding efficiency of MDASM is reduced by 21.8% and the value of surface roughness is reduced by 14.57% compared with NTASM. The simulation and FDM test studies show that MDASM can better retain the model feature details under the premise of ensuring the model molding efficiency, and has better model molding accuracy and practicality.

     

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