Open AccessOptimization of Fused Deposition Modelling process parameters using Teaching Learning Based Optimization (TLBO) algorithm
Author(s) -
Noor Alam,
Mahfuj Alam,
Shafi Ahmad
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/1149/1/012014
Subject(s) - surface roughness , nozzle , taguchi methods , orthogonal array , materials science , deposition (geology) , fused deposition modeling , algorithm , mechanical engineering , surface finish , process (computing) , engineering drawing , computer science , composite material , engineering , 3d printing , paleontology , sediment , biology , operating system
Fused Deposition Modelling (FDM) is one of the most commonly used Additive Manufacturing (AM) techniques with a wide range of applications in various modern manufacturing industries. It is widely employed to fabricate prototypes where immense surface finish is required. Furthermore, the literature suggests that process parameters such as nozzle temperature (NT), nozzle diameter (ND), and feed rate (FR) have a significant influence on the surface finish achieved in an FDM process. Hence, this work intends to examine the effect of process parameters viz. NT, ND, and FR on the side and top surface roughness of poly-lactic acid (PLA) sample fabricated through FDM process. Experiments are designed as per Taguchi’s L18 orthogonal array and a population-based algorithm identified as Teaching Learning Based Optimization (TLBO) algorithm is used to determine the optimal process parameter settings for optimum side and top surface roughness simultaneously. The results of the study reveal that NT of 493 K, ND of 0.4 mm and FR of 60 mm/s results in optimum side and top surface roughness simultaneously.