Analysis of Dimensional Accuracy of Fused Filament Fabrication Parts Using Genetic Algorithm and Taguchi Analysis

Chohan, J. S.; Kumar, Raman; Singh, Sandeep

doi:10.1007/978-3-031-04301-7_7

Cited by 1 publication

(1 citation statement)

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“…Specifically, it played a pivotal role in optimizing a myriad of process parameters. These encompassed layer thickness [66][67][68][69], orientation angle [66,67,69], raster angle [66,67,69], raster width [66,67,69], printing temperature [68], infill pattern [68], slice thickness [70], road width [70], liquefier temperature [70], and air gap [67,69,70]. The overarching aim was to minimize dimensional variability [67], reduce build time [71] , enhance accuracy [71], refine surface roughness [70], and meticulously control porosity [70] in FDM parts.…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

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

Batu,

Lemu,

Shimels

et al. 2023

IOP Conf. Ser.: Mater. Sci. Eng.

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Manufacturing is the foundation of any industrialized country and involves making products from raw materials using various processes. Additive manufacturing (AM) was originally created as a method for swift prototyping, allowing the visualization, testing, and validation of a design prior to final production for end-users. FDM is the most commonly used additive manufacturing process for constructing products and prototypes. It encompasses numerous process parameters that impact the quality of manufactured products. Properly selecting these process parameters is crucial for producing products at a lower cost while enhancing mechanical properties, build time, and part quality, among other factors. Therefore, in the past, researchers have optimized the process parameters to achieve the desired product outcomes. In the present study, we provide an overview of FDM process parameters and review various design optimization methods. We present several experimental designs, such as the Taguchi method, response surface methodology, and design of experiments, as well as computational approaches like artificial intelligence, and machine learning.

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