Parametric Influence of Process Parameters on the Wear Rate of 3D Printed Polylactic Acid Specimens

Indian Journal of Pure & Applied Physics

article

Manohar Singh¹  Pushpendra S Bharti¹ 

[1] U S I C T, Guru Gobind Singh Indraprastha University;Galgotias College of Engineering & Technology

关键词: Additive manufacturing (AM);    Fused deposition modeling (FDM);    Artificial neural network (ANN);    Article;   

来源: National Institute of Science Communication and Information Resources

【 摘 要 】

Fused deposition modeling (FDM) is a 3D printing technique that prints thermoplastic layer by layer. Various parameters affect the properties of the final printed object. The exact identification of variation in the properties of the printed object is still a very popular issue among the researchers. In the present work, an effort has been made to identify the parametric influence of layer thickness, infill density, print speed and extruder temperature on the wear behavior of the printed specimens. The specimens of polylactic acid (PLA) have been printed using Fused Deposition Modeling (FDM). The combinations of input parameters during the fabrication have been considered as per the Taguchi L16 Orthogonal Array. Moreover, to identify the parametric influence on wear, mathematical modeling has been done using regression and artificial neural networks. The results show that the average percentage variation in predicted experimental values for regression and ANN models are, 5.04% and 1.94%, respectively. Moreover, for minimum wear layer thickness should be kept between 0.28- 0.34mm. Similarly, infill density, print speed, and extruder temperature should be between 70-72, 125-175mm/s and 195-202 degree, respectively.

【 授权许可】

Unknown