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Additive Manufacturing (AM) has transformed manufacturing by enabling the fabrication
of complicated geometries with less material waste which is particularly beneficial in a
variety of industries, including aerospace, automotive, healthcare, and consumer goods.
Fused Deposition Modeling (FDM) is a prominent approach in the realm of AM, attributed
to its economic practicality, user-friendly execution, and material flexibility. The persistent
of warpage in FDM poses a critical challenge in FDM affecting dimensional accuracy,
fitment and mechanical reliability of components. Despite advancements, a lack of
comprehensive studies on the combined effects of key process parameters limits the ability
to predict and mitigate the effect of warpage. This research aims at providing approaches
to rectify warpage in FDM-printed parts a priority based on process improvements. This
research investigates three critical printing parameters i.e., raster pattern, bed temperature,
and infill density. Samples are printed with various printing conditions and warpage is
measured followed by validation using FEA analysis. The initially developed predictive
model gained relatively high precision with overall variance of value being less than 10%
in accordance with experimental tests. FEA simulations show that the FCC raster pattern
decreases warping by 40% than the grid raster pattern. Similarly, 80ºC bed temperature
decreases warpage by 30% comparing with 40ºC. Infill density of 90% decreases warpage
by 20% more than 10% infill density. Optimizing key process parameters in FDM can
significantly reduce warpage, improving dimensional accuracy and mechanical reliability
in precision-critical applications.