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Understanding the mechanical properties of printed parts requires detailed analysis of their microstructures. While experiments are reliable for capturing as-printed microstructures, they are often costly and time-consuming. Alternatively, numerical simulations have become valuable tools to reduce the reliance on trial-and-error experimentation, powered by recent advances in high-performance computing such as GPUs for acceleration. However, a key question remains of effectively combining high-fidelity, yet expensive, experiment data with relatively lower-fidelity, yet massive, simulation data to achieve a coherent prediction of microstructures under various manufacturing process conditions. In this study, we leverage the power of deep generative models and propose a sim-to-real framework based on denoising diffusion probabilistic model (DDPM) to generate realistic microstructures for additively manufactured products. Specifically, we pre-train a DDPM on a large dataset of phase-field (PF) simulated microstructures. This model is then fine-tuned or distilled using a relatively small set of microstructure images obtained from electron backscatter diffraction (EBSD) experiments, with the goal of enhancing the authenticity of the generated images. The microstructures generated by our sim-to-real diffusion models show strong agreement with experimental results, evaluated using both machine learning and physics-based metrics. In particular, the sim-to-real diffusion models show accurate prediction under unseen experimental manufacturing process conditions (but covered by simulation data), demonstrating their excellent generalization ability.<\/jats:p>","DOI":"10.1007\/s00366-026-02290-9","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T11:54:51Z","timestamp":1773748491000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Sim-to-real diffusion models for microstructure prediction in metal additive manufacturing"],"prefix":"10.1007","volume":"42","author":[{"given":"Ziyuan","family":"Xie","sequence":"first","affiliation":[]},{"given":"Zichuan","family":"Fu","sequence":"additional","affiliation":[]},{"given":"Jingchi","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Kaihao","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Tianchen","family":"Zeng","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Shuheng","family":"Liao","sequence":"additional","affiliation":[]},{"given":"Xiang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Tianju","family":"Xue","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,3,17]]},"reference":[{"key":"2290_CR1","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1016\/j.engstruct.2018.11.045","volume":"180","author":"C Buchanan","year":"2019","unstructured":"Buchanan C, Gardner L (2019) Metal 3d printing in construction: A review of methods, research, applications, opportunities and challenges. 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