Fuzzy logic and taguchi approach for compressive strength prediction of ABS components fabricated by AM

Abstract

This study investigated the prediction of compressive strength in fused deposition modeling (FDM) printed acrylonitrile butadiene styrene (ABS) samples Taguchi-fuzzy logic (FL) approach. The research examined six critical printing parameters: printing direction (PD), infill density (ID), infill pattern (IP), layer height (LH), printing speed (PS), and nozzle temperature (NT). The Taguchi L27 orthogonal array was utilized to systematically design experiments, ensuring minimal trials while maximizing the amount of obtained data. Analysis of variance (ANOVA) revealed that ID was the most influential parameter with a 37.39% contribution to compressive strength, followed by PS (26.95%) and LH (17.51%). The optimal parameter combination achieved a maximum compressive strength of 55.76 MPa with 90% ID, 100 mu m LH, and 40 mm/s PS. FL model was developed to predict compressive strength, demonstrating superior accuracy with a 3.1% average error rate compared to the Taguchi model's 3.7%. The model's reliability was validated through additional experiments, confirming its effectiveness for predicting compressive strength in FDM-printed ABS components. This research provides a robust methodology for accurately predicting mechanical properties, contributing to advancing additive manufacturing (AM) quality control.