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The evolution of the dislocation density induced by the nanomachining process dominates the plastic deformation behaviors of materials, thus affecting the mechanical properties significantly. However, a challenging topic related to how to establish an accurate model for predicting the dislocation density based on the limited simulations and experiments arises due to the complicated thermal–mechanical coupling mechanism during the machining process. Herein, a multistage method integrating machine learning, physics, and high‐throughput atomic simulation is proposed to investigate the effect of cutting speed on the dislocation behavior in polycrystal copper. Compared with the traditional one‐step machine learning method, the constraint of physical features effectively improves the accuracy and generalization ability of the model. The results indicate that the dislocation behaviors depend on the competition between the cutting force and temperature. In the low‐cutting speed, the predominated role of the cutting temperature leads to a rapid decline of the dislocation density. In contrast, the dislocation density tends to be stable under a high‐speed cutting process due to the dynamic balance between the effects of the cutting force and temperature. Notably, the proposed strategy provides a new and universal framework to design the machining parameters to obtain high‐quality products.

 

Traditionally, precipitates in a material are thought to serve as obstacles to dislocation glide and cause hardening of the material. This conventional wisdom, however, fails to explain recent discoveries of ultrahigh-strength and large-ductility materials with a high density of nanoscale precipitates, as obstacles to dislocation glide often lead to high stress concentration and even microcracks, a cause of progressive strain localization and the origin of the strength–ductility conflict. Here we reveal that nanoprecipitates provide a unique type of sustainable dislocation sources at sufficiently high stress, and that a dense dispersion of nanoprecipitates simultaneously serve as dislocation sources and obstacles, leading to a sustainable and self-hardening deformation mechanism for enhanced ductility and high strength. The condition to achieve sustainable dislocation nucleation from a nanoprecipitate is governed by the lattice mismatch between the precipitate and matrix, with stress comparable to the recently reported high strength in metals with large amount of nanoscale precipitates. It is also shown that the combination of Orowan’s precipitate hardening model and our critical condition for dislocation nucleation at a nanoprecipitate immediately provides a criterion to select precipitate size and spacing in material design. The findings reported here thus may help establish a foundation for strength–ductility optimization through densely dispersed nanoprecipitates in multiple-element alloy systems.