Predicting the phase precipitation of multicomponent alloys, especially the Ni-base superalloys, is a difficult task. In this work, we introduced a dependable and efficient way to establish the relationship between composition and detrimental phases in Ni-base superalloys, by integrating high throughput experiments and machine learning algorithms. 8371 sets of data about composition and phase information were obtained rapidly, and analyzed by machine learning to establish a high-confidence phase prediction model. Compared with the traditional methods, the proposed approach has remarkable advantage in acquiring and analyzing the experimental data, which can also be applied to other multicomponent alloys. IMPACT STATEMENT By integrating the high throughput experiments and machine learning algorithms, it is hopeful to facilitate the design of new Ni-base superalloys, and even other multicomponent alloys.
Equal-channel angular pressing (ECAP) was performed on a Mg (6 wt %) Zn alloy at temperatures from 160 to 240 °C and the microstructures and mechanical properties were studied using optical microscopy, X-ray diffraction, scanning electron microscopy, transmission electron microscopy, and an electronic universal testing machine. The results showed that ECAP was effective for grain refinement and a bi-modal grain structure formed at low temperatures, which was stable during ECAP from 160 to 200 °C. MgZn2 phase and Mg4Zn7 phase were generated during the ECAP process. The mechanical properties remarkably increased after two repetitions of ECAP. However, the strengths could not be further improved by increasing the plastic deformation, but decreased when ECAP was performed between 200 and 240 °C. The mechanical properties of the ECAP Mg-6Zn alloy was determined by a combination of grain refinement strengthening, precipitation hardening, and texture softening.
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