In additive manufacturing, such as Selective Laser Melting (SLM), identifying fabrication defects poses a significant challenge. Existing identification algorithms often struggle to meet the precision requirements for defect detection. To accurately identify small-scale defects in SLM, this paper proposes a deep learning model based on the original YOLOv5 network architecture for enhanced defect identification. Specifically, we integrate a small target identification layer into the network to improve the recognition of minute anomalies like keyholes. Additionally, a similarity attention module (SimAM) is introduced to enhance the model’s sensitivity to channel and spatial features, facilitating the identification of dense target regions. Furthermore, the SPD-Conv module is employed to reduce information loss within the network and enhance the model’s identification rate. During the testing phase, a set of sample photos is randomly selected to evaluate the efficacy of the proposed model, utilizing training and test sets derived from a pre-existing defect database. The model’s performance in multi-category recognition is measured using the average accuracy metric. Test results demonstrate that the improved YOLOv5 model achieves a mean average precision (mAP) of 89.8%, surpassing the mAP of the original YOLOv5 network by 1.7% and outperforming other identification networks in terms of accuracy. Notably, the improved YOLOv5 model exhibits superior capability in identifying small-sized defects.

Conventionally, fused deposition modeling (FDM) 3D printing allows for multiple color printing, but it is limited to only various monochromatic colors. Consequently, the effect of progressive color transition cannot be reflected. To produce the progressive 3-D color printing effect, the only solution is to implement stereolithography technology, which is particularly expensive. Therefore, the aim of this paper is to develop a color mixing mechanism to be incorporated into an FDM 3D printer, which is relatively inexpensive. The underlying idea is to pre-mix the color so that the FDM 3D printer can produce a progressive color printing effect. Three conceptual color mixing mechanisms are designed, i.e., a triangular stirring shaft, a rectangular spoiler stirring shaft, and a spiral blade stirring shaft. The mixing process is modeled based on the non-Newtonian fluid theory, in which the Carreau model is used to simulate the motion of pseudoplastic fluids in FDM 3D printing under forced mixing. The resulting mixing ratio produced by all the designs is computed, which inspires the integrated design of rectangular spoiler stirring shaft and the spiral blade string shaft. Subsequently, the axial velocity of the mixed-color fluid, which increases from inlet to outlet, is verified. The integrated design is then fabricated and incorporated into the FDM 3D printer, and the progressive color printing effect is practically demonstrated.