A review on optimization of process parameters of fused deposition modeling

Abstract

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.

Process parameter modelling and optimization techniques applied to fused deposition modelling: A review

“…This method is extensively employed in AM research too to explore the impact of process parameters on additively manufactured components using this method. It integrates statistical and mathematical techniques to optimize performance traits, revealing effects with fewer experiments and focusing on controlling signal factors while mitigating noise factors that contribute to unpredictability [28].…”

“…The experimental units are displayed to all conceivable combinations of the levels over all variables. This approach facilitates the investigation of how each component influences the variables and the interactions among the factors affecting them [28].…”

Assessment of the mechanical properties of PC/ABS blends for functional prototyping by FFF 3D printing

“…Print speed is often set to high values to increase productivity but may negatively impact the tensile strength of printed parts. To better understand the effects of other print parameters, such as layer height and printing temperature, it is important to apply experimental matrices and statistical models that assimilate the most relevant parameters and the effect of their interactions (Suniya and Verma, 2023).…”