Keywords

Abstract

3D printing;

Additive manufacturing;

Artificial neural network;

Fused deposition modeling;

Genetic algorithm;

Taguchi philosophy

The purpose of this review is to explore various techniques used in the optimization of process parameters of 3D printing machines for different applications. Fused Deposition Modeling (FDM) is an emerging technology that has been widely used in diverse areas including new product development, mould manufacturing, etc. FDM is the process of depositing the material in a layer-by-layer manner to manufacture the part. FDM provides a lot of flexibility in fabricating a part. Many complex parts can be manufactured by FDM easily which are very difficult to manufacture through conventional manufacturing methods. However, build-in time, manufacturing speed, and mechanical strength of FDM fabricated parts are still challenging and critical. The quality of FDM fabricated parts is affected by various machining parameters, such as air gap, build orientation, infill percentage, raster angle, raster width, layer thickness, etc. The selection of significant process parameters needs to be identified and optimized as per the usage of apart. Many researchers have used different techniques, such as the design of experiment (DOE) technique, response surface method(RSM), genetic algorithm(GA), artificial neural network(ANN), and fuzzy, to optimize the FDM process parameters to improve the desired part quality, such as mechanical properties, and dimensional accuracy. This survey paper attempts to critically review various research articles published on the optimization of process parameters to improve the performance parameters of FDM.

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