Metal 3D printing holds great promise for future digitalized manufacturing. However, the intricate interplay between laser and metal powders poses a significant challenge for conventional trial-and-error optimization. Meanwhile, the “optimized” yet fixed parameters largely limit possible extensions to new designs and materials. Herein, we report a high throughput design coupled with machine learning (ML) guidance to eliminate the notorious cracks and porosities in metal 3D printing for improved corrosion resistance and overall performance. The high throughput methodologies are mostly on obtaining the printed samples and their structural and physical properties, while ML is used for data analysis by model building for prediction (optimization), and understanding. For 316L stainless steel, we concurrently printed 54 samples with different parameters and subjected them to parallel tests to generate an extensive dataset for ML analysis. An ensemble learning model outperformed the other five single learners while Bayesian active learning recommended optimal parameters that could reduce porosity from 0.57% to below 0.1%. Accordingly, the ML-recommended samples showed higher tensile strength (609.28 MPa) and elongation (50.67%), superior anti-corrosion (I_corr = 4.17 × 10⁻⁸ A·cm⁻²), and stable alkaline oxygen evolution for >100 hours (at 500 mA·cm⁻²). Remarkably, through the correlation analysis of printing parameters and targeted properties, we find that the influence of hardness on corrosion resistance is second only to porosity. We then expedited optimization in AlSi7Mg using the learned knowledge and feed hardness and relative density, thus demonstrating the method’s general extensibility and efficiency. Our strategy can significantly accelerate the optimization of metal 3D printing and facilitate adaptable design to accommodate diverse materials and requirements.

NiTi alloys have drawn significant attentions in biomedical and aerospace fields due to their unique shape memory effect (SME), superelasticity (SE), damping characteristics, high corrosion resistance, and good biocompatibility. Because of the unsatisfying processabilities and manufacturing requirements of complex NiTi components, additive manufacturing technology, especially laser powder bed fusion (LPBF), is appropriate for fabricating NiTi products. This paper comprehensively summarizes recent research on the NiTi alloys fabricated by LPBF, including printability, microstructural characteristics, phase transformation behaviors, lattice structures, and applications. Process parameters and microstructural features mainly influence the printability of LPBF-processed NiTi alloys. The phase transformation behaviors between austenite and martensite phases, phase transformation temperatures, and an overview of the influencing factors are summarized in this paper. This paper provides a comprehensive review of the mechanical properties with unique strain-stress responses, which comprise tensile mechanical properties, thermomechanical properties (e.g. critical stress to induce martensitic transformation, thermo-recoverable strain, and SE strain), damping properties and hardness. Moreover, several common structures (e.g. a negative Poisson’s ratio structure and a diamond-like structure) are considered, and the corresponding studies are summarized. It illustrates the various fields of application, including biological scaffolds, shock absorbers, and driving devices. In the end, the paper concludes with the main achievements from the recent studies and puts forward the limitations and development tendencies in the future.