A Framework for Predicting 3D Printed Part Defects Using MultiModal Deep Learning on Infrared Images

Fused filament fabrication (FFF) has gained popularity in recent years because it can produce prototypes and functional components with complex geometry. Because of inherent process variability, the components often exhibit defects such as warping, layer delamination, voids, and poor surface finish, as well as issues related to variable material strength and anisotropy. In-situ monitoring (ISM) of the FFF process is a promising technique to predict part performance, which in turn can support accept or reject decisions for printed parts. This paper proposes a framework for incorporating ISM-generated information, with a particular focus on infrared (IR) image analysis for this purpose. IR camera images, in conjunction with numerical features such as infill pattern and extruder nozzle temperature, serve as an input to a multimodal deep learning (MDL) model that predicts the mechanical performance of printed parts. In the framework, convolutional neural nets process image inputs, while a fully connected neural network extracts patterns from numerical process parameters. Furthermore, the proposed approach incorporates an ablation study and Cohort Shapley analysis to identify the most informative monitoring modalities and process parameters. This fusion of modalities enables more accurate and robust prediction of mechanical response than a single-source model. We demonstrate the framework on FFF-printed beams subjected to torque and three point bending tests, and discuss opportunities for future work in vehicle manufacturing and expeditionary sustainment.