Three-dimensional (3D) printed Sc-modified Al alloy by powder bed fusion (PBF) provides significant strength and ductility without hot tearing during the process. This kind of 3D-printable high specific strength materials exhibits great potential in lightweight applications. Due to the lesser design limitation through the 3D printing process, the degree of lightweight is greatly affected by the specific strength of the materials. Hence, to further improve the mechanical properties of the material through process optimisation or post-treatment is of great importance. Microstructure feature variations due to different processing parameters are well known for traditional processes and materials. This study explores the parameter–microstructure–performance relationship of 3D printed Sc-modified Al alloys from the perspective of melt pool interactions. According to the stress concentration effect and Hall–Petch effect, the mechanical properties of the 3D printed materials vary greatly depending on the difference in defect size, shape and grain size.

We present a novel attribute learning framework named Hypergraph-based Attribute Predictor (HAP). In HAP, a hypergraph is leveraged to depict the attribute relations in the data. Then the attribute prediction problem is casted as a regularized hypergraph cut problem, in which a collection of attribute projections is jointly learnt from the feature space to a hypergraph embedding space aligned with the attributes. The learned projections directly act as attribute classifiers (linear and kernelized). This formulation leads to a very efficient approach. By considering our model as a multi-graph cut task, our framework can flexibly incorporate other available information, in particular class label. We apply our approach to attribute prediction, Zero-shot and N-shot learning tasks. The results on AWA, USAA and CUB databases demonstrate the value of our methods in comparison with the state-of-the-art approaches.