Design of a Fused Filament Fabrication (FFF) 3D-printer

Oluwajobi, Akinjide O.; Kolawole, Fidelis

doi:10.4314/njt.v40i2.10

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…During the 3D printing process, various issues [11] can cause failures with the filament, such as the heat bed being too close, residual filament in the nozzle, or blockages in the tube. A monitoring system for additive manufacturing refers to the process of collecting and analyzing data during the 3D printing process to ensure that the final product meets the desired specifications [12]. This system typically includes various components, such as sensors, software, and hardware, that track various aspects of the printing process, including temperature [13], layer thickness, material usage, and build progress [14].…”

Section: Introductionmentioning

confidence: 99%

Utilizing a knowledge-based training algorithm and time-domain extraction for pattern recognition in cylindrical features through vibration and sound signals

Dirhamsyah,

Riza,

Rizal

2023

J. meas. eng.

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This study presents a new solution to address challenges encountered in additive manufacturing, specifically in the context of 3D printing, where failures can occur due to complications associated with the nozzle or filament. The proposed solution in this research involves using a time-domain feature extraction method that leverages sound and vibration patterns. By implementing sensors to capture these signals in a controlled and noise-free environment, and then utilizing a Multi-Layer Perceptron (MLP) model trained accurately to predict upcoming signals and vibrations, proactive anticipation of printing outcomes is facilitated, including potential failures. Simulation results obtained using MATLAB for the MLP showcase the effectiveness of this approach, demonstrating remarkably low error rates. Furthermore, through rigorous data validation, the proposed method's ability to accurately identify sound and vibration signals is confirmed. As a result, the likelihood of failures is significantly reduced, thereby preventing defects in the filament. The implications of this solution hold great promise in substantially enhancing the reliability and efficiency of additive manufacturing processes.

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