| description abstract | Despite the rapid adoption of deep learning models in additive manufacturing (AM), significant quality assurance challenges continue to persist. This is further emphasized by the limited availability of sample objects for monitoring AM-fabricated builds. Thus, this study advances an emerging diffusion generative model, i.e., the denoising diffusion implicit model (DDIM), for layer-wise image augmentation and monitoring in AM. The generative model can be used to generate potential layer-wise variations, which can be further studied to understand their causation and prevent their occurrence. The proposed models integrate two proposed kernel-based distance metrics into the DDIM framework for effective layer-wise AM image augmentation. These newly proposed metrics include a modified version of the kernel inception distance (m-KID) as well as an integration of m-KID and the inception score (IS) termed KID-IS. These novel integrations demonstrate great potential for maintaining both similarity and consistency in AM layer-wise image augmentation, while simultaneously exploring possible unobserved process variations. In the case study, six different cases based on both metal-based and polymer-based fused filament fabrication (FFF) are examined. The results indicate that both the proposed DDIM/m-KID and DDIM/KID-IS models outperform the four benchmark methods, including the popular denoising diffusion probabilistic models (DDPMs), and three other generative adversarial networks (GANs). Overall, DDIM/KID-IS emerges as the best-performing model with an average KID score of 0.840, m-KID score of 0.1185, peak signal-to-noise ratio (PSNR) of 18.150, and structural similarity index measure (SSIM) of 0.173, which demonstrated strong capabilities in generating potential AM process variations in terms of layer-wise images. | |
