Prediction of Printing Parameters for Minimal Dimensional Variation in 3D Printed Parts by Using Artificial Intelligence

3D Printing as a manufacturing method has exploded in popularity in recent years. Due to the multiple advantages it offers, additive manufacturing has engulfed the whole manufacturing business. The basic idea behind additive manufacturing is to build a specimen by piling material layer by layer until it takes on the desired shape. Fused Deposition Modelling (FDM), or 3D Printing, is amongst the most used additive manufacturing techniques. The print quality is mostly determined by the raw materials used as well as the print parameters. The major goal of this research was to determine the best printing temperature for a cylindrical shaft with the least amount of dimensional variance. This forecast can then be used to improve print quality. In this study, the FDM 3D Printing technology was used, and the feedstock was Acrylonltrile-Butadlene-Styrene (ABS). The effect of extrusion temperature and build-plate temperature on printed sample dimensional fluctuation was explored, and an Artificial Intelligence prediction model was constructed. Layer Thickness has a considerable impact on printed specimens, according to the data.