To achieve an effective design of additively manufacturable Ni superalloys with decent service performance, a hybrid computational design model has been developed, where the strategy to tailor local elemental segregations was integrated within a scheme of minimizing the cracking susceptibility. More specifically, the phase boundary of primary NbC / γ matrix was introduced into the design routine to tune the spatial distribution of critical solutes at an atomic scale, thereby inhibiting the formation of borides and segregation-induced cracking. Based on the output of the design, new grades of Ni superalloy have been developed with excellent additive manufacturability, as confirmed by the robustness of printing parameters in fabricating low-defect-density samples. The capability of the phase boundaries to evenly distribute boron atoms was validated experimentally, and the cracking induced by uncontrolled boron segregation at grain boundaries was effectively prevented. The newly designed alloys showed good tensile properties and decent oxidation resistance at different service temperatures, which are comparable to those of conventionally produced superalloys. The finding that phase boundaries can be employed to prevent undesirable clustering of boron atoms can be extended to manipulate the distributions of other critical elements, which provides a new path for designing novel Ni superalloys with balanced printability and mechanical properties.

Battery fault diagnosis is crucial for stable, reliable, and safe operation of electric vehicles, especially the thermal runaway early warning. Developing methods for early failure detection and reducing safety risks from failing high energy lithium-ion batteries has become a major challenge for industry. In this article, a real-time early fault diagnosis scheme for lithium-ion batteries is proposed. By applying both the discrete Fréchet distance and local outlier factor to the voltage and temperature data of the battery cell/module that measured in real time, the battery cell that will have thermal runaway is detected before thermal runaway happens. Compared with the widely used single parameter based diagnosis approach, the proposed one considerably improve the reliability of the fault diagnosis and reduce the false diagnosis rate. The effectiveness of the proposed method is validated with the operational data from electric vehicles with/without thermal runaway in daily use.