Fused Filament Fabrication (FFF) is an additive manufacturing technique commonly used in industry to produce complicated structures sustainably. Although promising, the technology frequently suffers from defects, including warp deformation compromising the structural integrity of the component and, in extreme cases, the printer itself. To avoid the adverse effects of warp deformation, this thesis explores the implementation of deep neural networks to form a closed-loop in-process monitoring architecture using Convolutional Neural Networks (CNN) capable of pausing a printer once a warp is detected. Any neural network, including CNNs, depend on their hyperparameters. Hyperparameters can either be optimized using a manual or an automated approach. A manual approach, although easier to program, is often time-consuming, inaccurate and computationally inefficient, necessitating an automated approach. To evaluate this statement, classification models were optimized through both approaches and tested in a laboratory scaled manufacturing environment. The automated approach utilized a Bayesianbased optimizer yielding a mean accuracy of 100% significantly higher than 36% achieved by the other approach.
Implementation of Convolutional Neural Networks for Warp Detection in 3D Printed Components manufactured via Fused Filament Fabrication: A Bayesian-Based Automated Approach
