Investigation for Impact of Process Parameters on Mechanical Properties of Fused Filament Fabrication Components
Abstract
The purpose of this article is to look at a variety of tactics used in different industries to optimize the operating parameters of 3D printing systems. Fused Deposition Modeling (FDM), one of the most well-known methods, has drawn a lot of interest because of its broad range of applications in fields including die-making and prototype development. FDM creates three-dimensional objects by layering materials one after the other. Because of its great versatility, the technique makes it possible to produce complex geometries that would be challenging to accomplish with conventional manufacturing techniques. However, FDM still has drawbacks with regard to printing speed, production time, and the structural soundness of the printed parts. The quality of the finished product is directly impacted by a number of variables, including the distance between layers, orientation during printing, percentage of internal fill, deposition angle, path width, and layer depth. Determining and modifying the most important factors in accordance with the particular needs of the item being produced is therefore crucial. To tackle these challenges, numerous researchers have explored advanced optimization tools like experimental design approaches, surface response modeling, evolutionary algorithms, neural network models, and fuzzy logic systems. Many academics have investigated cutting-edge optimization tools such as fuzzy logic systems, evolutionary algorithms, surface response modeling, experimental design approaches, and neural network models in order to address these issues. Strength, accuracy, and dependability are some of the important product attributes that are improved by using these instruments. Objective of this work is to present a thorough analysis of the body of research on enhancing FDM results via efficient process parameter adjustment.
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