Investigation of dynamic elastic deformation of parts processed by fused deposition modeling additive manufacturing
Investigation of dynamic elastic deformation of parts processed by fused deposition modeling additive manufacturing
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DOI：
10.14743/apem2016.3.223
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发表时间：
2016-09-01
影响因子：
3.6
通讯作者：
Bhowmik, Jahar L.
中科院分区：
文献类型：
--
作者：
Mohamed, Omar A.;Masood, Syed H.;Bhowmik, Jahar L.
熔融沉积建模（FDM）已被认为是一种有效的技术，可以直接从数字计算机辅助设计（CAD）模型中逐层制造三维零件。虽然增材制造技术已成为一项非常重要的制造工艺，但由于许多加工参数影响了零件的性能，因此在动态和循环条件下，增材制造技术仍未被广泛接受。本研究的目的是通过检测单个和交互FDM工艺参数如何影响动态和循环条件下的制成品性能来表征FDM制造零件。实验采用分数因子设计和人工神经网络（ANN）进行。采用方差分析（ANOVA）方法研究了各参数对动态弹性模量的影响。在此基础上，确定了最佳工艺参数，并进行了验证实验。结果表明，人工神经网络和分数阶乘模型都提供了良好的预测质量，但在数据拟合和估计能力方面，人工神经网络显示出经过适当训练的人工神经网络在捕捉系统的非线性关系方面优于分数阶乘模型。(C) 2016，马里博尔大学PEI。版权所有。
Fused deposition modeling (FDM) has been recognized as an effective technology to manufacture 3D dimensional parts directly from a digital computer aided design (CAD) model in a layer-by-layer style. Although it has become a significantly important manufacturing process, but it is still not well accepted additive manufacturing technology for load-carrying parts under dynamic and cyclic conditions due to many processing parameters affecting the part properties. The purpose of this study is to characterize the FDM manufactured parts by detecting how the individual and interactive FDM process parameters will influence the performance of manufactured products under dynamic and cyclic conditions. Experiments were conducted through fractional factorial design and artificial neural network (ANN). Effect of each parameter on the dynamic modulus of elasticity was investigated using analysis of variance (ANOVA) technique. Furthermore, optimal processing parameters were determined and validated by conducting verification experiment. The results showed that both ANN and fractional factorial models provided good quality predictions, yet the ANN showed the superiority of a properly trained ANN in capturing the nonlinear relationship of the system over fractional factorial for both data fitting and estimation capabilities. (C) 2016 PEI, University of Maribor. All rights reserved.