Open AccessOptimization Of Process Parameters In 3d Printing-Fused Deposition Modeling Using Taguchi Method
Author(s) -
M. Sumalatha,
J. N. Malleswara Rao,
B. Narayana Reddy*
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1112/1/012009
Subject(s) - fused deposition modeling , taguchi methods , design of experiments , 3d printing , infill , aerospace , deposition (geology) , process (computing) , surface roughness , rapid prototyping , mechanical engineering , automotive industry , layer (electronics) , orthogonal array , process variable , engineering drawing , process engineering , computer science , materials science , engineering , composite material , structural engineering , mathematics , statistics , paleontology , sediment , biology , operating system , aerospace engineering
3D printing or Rapid Prototyping (RP) or Additive Manufacturing (AM) is an innovative manufacturingmethod that is mostly used for prototypes in various Industries such as Aerospace, Defense, Medical, Automotive, etc. One of the worthy and widely used 3 D printing techniques is Fused Deposition Modeling (FDM), which involves adding melted material layer by layer. FDM has several benefits over other manufacturing methods. While setting the 3 D printing options, we have to take several parameters into account, such as speed, layer thickness and infill density, etc. The present experimental work illustrates the performance of ABS built parts fabricated by AION 500 make FDM machine. The quality of parts produced by FDM process mainly depends on the selection of process parameters. The present research work consists of process parameters such as layer thickness, infill density and speed of deposition, etc. This study aims to find effects of process parameters on different performance parameters i.e., mechanical considerations (impact strength), build time, surface roughness in a systematic manner with less number of experimental runs. Taguchi design of experiment (DOE) approach has been used to save cost and time of experimentation. Statistical significance of process parameters is analyzed using analysis of variance (ANOVA). The significant parameters for optimum results and optimal parameter setting has been suggested using S/N ratio.