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Wear assessment of 3–D printed parts of PLA (polylactic acid) using Taguchi design and Artificial Neural Network (ANN) technique
Materials Research Express ( IF 1.8 ) Pub Date : 2020-11-21 , DOI: 10.1088/2053-1591/abc8bd
Meena Pant 1 , Ranganath M Singari 2 , Pawan Kumar Arora 3 , Girija Moona 4 , Harish Kumar 1
Affiliation  

Additive manufacturing (AM) is a rapidly growing technology with promising results and challenges. The aim of this study is to optimize the process parameters of fused deposition modeling (FDM) by exploring the wear performance of Polylactic acid (PLA). In this work, variation of process parameters like layer thickness, orientation and extruder temperature has been investigated. Based on these parameters wear specimen (accordance to ASTM G99) was printed by using FDM. The wear behavior of polymer pin under low sliding speed was investigated. Taguchi Design of experiments by using L9 orthogonal array is applied to optimize the process parameters at which minimum wear rate is obtained and the same has also been investigated by using analysis of variance (ANOVA) and artificial neural network (ANN) technique for rigorous validation / optimization. Results shows that build orientation have major influence on the wear performance of polymer pin. The paper is presented with the display of results, discussion, and conclusions drawn.



中文翻译:

使用Taguchi设计和人工神经网络(ANN)技术评估PLA(聚乳酸)的3D印刷零件的磨损

增材制造(AM)是一项快速发展的技术,具有可喜的结果和挑战。这项研究的目的是通过探索聚乳酸(PLA)的磨损性能来优化熔融沉积建模(FDM)的工艺参数。在这项工作中,已经研究了诸如层厚度,取向和挤出机温度等工艺参数的变化。根据这些参数,使用FDM打印磨损样品(根据ASTM G99)。研究了聚合物销在低滑动速度下的磨损行为。Taguchi使用L 9设计实验正交阵列用于优化获得最小磨损率的工艺参数,并且还通过使用方差分析(ANOVA)和人工神经网络(ANN)技术对严格的验证/优化进行了研究。结果表明,构造取向对聚合物销的磨损性能有重大影响。展示结果,讨论和得出的结论。

更新日期:2020-11-21
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