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Review Article

A review on optimization of process parameters of fused deposition modeling

Naveen Kumar Suniya, Arvind Kumar Verma

Department of Production and Industrial Engineering, MBM University, Jodhpur, India

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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LATEST News

01/01/2024 Best Paper Award: The winners of 2023 Best Paper Award of Research on Engineering Structures and Materials (RESM) are announced. More information can be found at Author Awards section.


01/01/2024 Most Cited Paper Award: The winners of 2023 Most Cited Paper Award of Research on Engineering Structures and Materials (RESM) are announced. More information can be found at Author Awards section.


01/01/2024 Reviewer Awards: The winners of 2023 reviewer awards of Research on Engineering Structures and Materials (RESM) are announced. More information can be found at Reviewer Awards section.


8/12/2023 Special Issue: Embark on a journey of innovation with the journal of Research on Engineering Structures and Materials as we unveil a compelling opportunity for contributors in our upcoming special issue, "Design, Analysis, and Manufacturing of Composite Vehicle Structures." Led by distinguished Guest Editors Liubov Gavva and Oleg Mitrofanov from Moscow Aviation Institute. For more info see the link.


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LATEST AWARDS


2023 Reviewer Awards:

Please, visit Reviewer Awards section for the winners of the 2022 RESM reviewer awards.



2023 Best Paper Award:

The paper authored by Ferzan Fidan, Naim Aslan, Mümin Mehmet Koç entitled as “Morpho-structural and compressive mechanical properties of graphene oxide reinforced hydroxyapatite scaffolds for bone tissue applications” is awarded.



2023 Most Cited Paper Award:

The paper authored by Ercan Işık, Ehsan Harirchian, Hüseyin Bilgin, Kirti Jadhav entitled as “The effect of material strength and discontinuity in RC structures according to different site-specific design spectra" is awarded.


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