Alin-Daniel RIZEA, Daniel-Constantin ANGHEL, Cristina-Florena BANICA


This research study presents an exploration into the accuracy of threads produced using the Fused Deposition Modeling (FDM) process. In traditional manufacturing of helical threads adhering to established standards, deformations can occur. These deviations in dimensions often result in departures from radial tolerances and changes in thread shapes. The primary objective of this research is to investigate and characterize these dimensional inaccuracies using image analysis. Additionally, the study aims to address these imperfections partially by implementing a strategy to adjust thread profiles. To achieve this goal, a total of 9 specimens were manufactured using the ZORTRAX M-200 3D printer, varying in flank inclination values and layer thicknesses. These values were selected based on a Taguchi plan. Z-ABS material was utilized in the printing process. The collected data underwent analysis using Minitab software and the Artificial Neural Networks Toolbox within MATLAB, enabling corrective measures to be applied to the printed thread profiles. As an innovative aspect of this study, the paper introduces the utilization of Artificial Neural Networks for analysis and the potential to make necessary adjustments in order to attain profiles that closely align with the desired outcomes.

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