Optimization of Fused Deposition Modelling process parameters using Teaching Learning Based Optimization (TLBO) algorithm

Abstract

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.