Additive manufacturing of metals by employing continuous wave and short pulse lasers completely changes the way of modern industrial production. But the ultrafast laser has the superiority to short pulse laser and continuous wave laser in additive manufacturing. It has higher peak power, small thermal effect, high machining accuracy and low damage threshold. It can effectively perform additive manufacturing for special materials and improve the mechanical properties of parts. This article reviews the mechanism of the interaction between ultrafast laser and metal materials to rule the manufacturing processes. The current application of ultrafast laser on forming and manufacturing special materials, including refractory metals, transparent materials, composite materials and high thermal conductivity materials are also discussed. Among the review, the shortcomings and challenges of the current experimental methods are discussed as well. Finally, suggestions are provided for the industrial application of ultrashort pulse laser in the field of additive manufacturing in the future.

In this work, Fe₆₄Nb₃B₁₇Si₆Cr₆Ni₄ and Fe₆₀Nb₃B₁₇Si₆Cr₆Ni₄Mo₄ (at. %) coatings were prepared with a high-velocity air fuel spraying method, and the effects of minor Mo addition on the microstructure, glass formation, and corrosion resistance of the coating were studied. It was found that the Mo addition improves the glass-forming ability of the alloy and a fully amorphous structure with a higher compactness was obtained in the Mo-containing coating. The thermal stability of the coating is enhanced by Mo addition and the onset crystallization temperature was increased by 20 K. In addition, the Mo-containing amorphous coating exhibited higher corrosion resistance than the Mo-free coating. The superior corrosion resistance can be attributed to the increased proportion of protective, stable Cr, Nb, and Mo oxides in the passive film and fewer defects of the Mo-containing coating.

High-Velocity Air-Fuel (HVAF) spraying of Fe-based amorphous coatings involves strong nonlinear coupling among multiple process parameters, while practical optimization is severely constrained by limited experimental data and poor model interpretability. To address these challenges, a systematic data-driven optimization framework integrating the Denoising Diffusion Probabilistic Model (DDPM)-based data augmentation with explainable machine learning is proposed. Coating microhardness and hardness uniformity were jointly selected as target properties to capture both performance level and spatial reliability. Three generative models—Generative Adversarial Network (GAN), Variational Autoencoder (VAE), and DDPM—were comparatively evaluated using statistical matching and distribution-consistency metrics, revealing that DDPM most faithfully reproduces the intrinsic statistical characteristics of real HVAF process data. We benchmarked ten representative regression algorithms covering classical statistical learning, ensemble methods, and deep learning paradigms, with GBR demonstrating the highest predictive accuracy and stability. The inclusion of 10% DDPM-generated samples further improved the predictive precision of the GBR model. SHapley Additive exPlanations (SHAP) quantitatively identified spraying distance as the dominant parameter governing coating hardness, while elucidating the coupled effects of multiple parameters on hardness uniformity. By interpolatively expanding the process parameter space, a two-stage screening strategy identified 98 high-performance parameter combinations. Experimental validation confirmed that the optimal parameter set simultaneously achieved higher hardness and improved uniformity compared with the original best condition, resulting in a 13.6% reduction in wear rate.