Optimization of 3D Printing Parameters: A Review Paper

Abstract

Additive Manufacturing (AM), commonly known as 3D printing, has transformed modern manufacturing by enabling complex geometries, reduced material waste, and rapid prototyping. However, the quality of printed components is highly dependent on process parameters such as layer height, printing speed, extrusion temperature, infill density, and build orientation. Improper selection of these parameters leads to defects like poor surface finish, weak mechanical strength, and dimensional inaccuracies. This review paper summarizes recent research on optimization techniques used in 3D printing processes, including statistical methods, machine learning approaches, and hybrid optimization techniques. The study highlights the significance of parameter optimization in improving mechanical properties, surface quality, and process efficiency. Furthermore, challenges and future research directions related to smart additive manufacturing and Industry 4.0 integration are discussed.

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