Sahu, J.K.Sahu, R.K.Katiyar, J.K.Kiran, P.S.2026-02-042023Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 2023, , , pp. -9544089https://doi.org/10.1177/09544089231206800https://idr.nitk.ac.in/handle/123456789/22099The fused deposition modelling (FDM) technique has been adopted in the present work to fabricate Acrylonitrile Butadiene Styrene (ABS) plastic components using input variables like layer thickness, orientation, infill rate and the number of shells. The dimensional geometrical changes of FDM printed built parts, that is, length, width and thickness variation, are optimised individually. Still, a confounding effect is found in the optimisation of individual responses. To overcome this issue, this study uses a fuzzy inference system as an artificially intelligent system. A fuzzy inference system transformed all the responses into one response, known as a multi-response performance index (MPI), and optimised the MPI coupled with Taguchi philosophy. The optimal parameter combination of FDM-printed ABS components for the slightest variation in length, width, and thickness was found to be layer thickness: 200 µm, orientation: 0°, infill rate: 20%,and the number of shells: 3. The significant parametric effect on the responses of FDM components was studied using analysis of variance, and the results reveal that orientation is the most influential parameter trailed by infill rate and the number of shells. © IMechE 2023.ABS resinsFused Deposition ModelingFuzzy inferenceFuzzy systemsInfill drillingIntelligent systemsStyreneAcrylonitrile-butadiene-styreneAnalyse of varianceArtificial smart systemGeometry accuracyLayer thicknessMulti-response performance indexMultiresponsePerformance indicesResponse performanceSmart SystemDeposition