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Design and optimization of 3D printing process parameters: A review

Radius: Journal of Science and Technology

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Volume 3, Issue 2, Article Number: 262003 (2026)

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Ankit Singh1,* ORCID logo | Vikas Patel1 | Deepak Pal1

1Department of Mechanical Engineering, Axis Institute of Technology and Management (AITM), Kanpur – 209402, U.P. (India)

*Corresponding Author: ankit108106@gmail.com

Received: 12 June 2026 | Revised: 27 June 2026

Accepted: 09 July 2026 | Published Online: 16 July 2026

DOI: https://doi.org/10.5281/zenodo.21298613

© 2026 The Authors, under a Creative Commons license, Published by Scholarly Publication

Abstract

The Additive Manufacturing (AM) technology and more specifically the Fused Deposition Modelling (FDM), is well known for the possibility to produce complex components with high design freedom in order to obtain outputs with reduced material waste and at competitive costs. The process parameters, like build orientation, infill density, bed temperature, nozzle temperature, layer thickness and print speed, however, have a significant effect on the quality and performance of FDM-printed parts. So, it is very important to choose the best suited process parameters in order to enhance the mechanical properties, surface quality, dimensional accuracy and manufacturing efficiency. The present review article critically explores the new studies reported to date on optimization of the process parameters for FDM process, covering statistical method namely Taguchi method, Response Surface Methodology (RSM), Grey Relational Analysis (GRA) and Design of Experiments (DOE) among various Artificial Intelligence (AI) and Machine Learning (ML) techniques such as Artificial Neural Networks (ANNs) and Particle Swarm Optimization (PSO). This review provides a comparative analysis of statistical and AI-based optimization techniques for FDM process parameter optimization and identifies current research gaps and future directions. The review concludes that hybrid optimization methods combining statistical process with AI–based method are more accurate and more optimized, and could be the answer to next-generation additive manufacturing systems.

Keywords

3D Printing, Additive Manufacturing, FDM, RSM, ANN, Surface Roughness

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Cite This Article

A. Singh, V. Patel, and D. Pal, “Design and optimization of 3D printing process parameters: A review,” Radius: Journal of Science and Technology 3(2) (2026) 262003. https://doi.org/10.5281/zenodo.21298613

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