Grain boundary stability governs hardening and softening in extremely fine nanograined metals

Abstract

Conventional metals become harder with decreasing grain sizes, following the classical Hall-Petch relationship. However, this relationship fails and softening occurs at some grain sizes in the nanometer regime for some alloys. In this study, we discovered that plastic deformation mechanism of extremely fine nanograined metals and their hardness are adjustable through tailoring grain boundary (GB) stability. The electrodeposited nanograined nickel-molybdenum (Ni-Mo) samples become softened for grain sizes below 10 nanometers because of GB-mediated processes. With GB stabilization through relaxation and Mo segregation, ultrahigh hardness is achieved in the nanograined samples with a plastic deformation mechanism dominated by generation of extended partial dislocations. Grain boundary stability provides an alternative dimension, in addition to grain size, for producing novel nanograined metals with extraordinary properties.

Local microstructure and flow stress in deformed metals

“…A further reduction in the structural scale down to or below ~10 nm requires an optimization of thermomechanical treatments and structural parameters. Example systems where such a scale may be reached include pearlitic steel wires [9,11,[20][21][22][23][24][25], Cu alloyed with Fe [26] and Ni alloyed with Mo [27]. An extension of the present research will require a validation of the calculated stress profiles on an extremely fine scale together with mechanical testing at high spatial resolution, for example, in-situ TEM testing [28] and nano-indentation.…”

Nanotwinned and hierarchical nanotwinned metals: a review of experimental, computational and theoretical efforts

“…GB in crystalline materials is a quintessential high-dimensional structure with great complexity. 10 Some new interesting efforts have been made to develop the fundamental understanding of its physical characteristics such as analyzing physical "building blocks" in GBs by machine learning, 10 a three-dimensional polyhedral unit model developed for GB structure, 11 tailoring the stability of GB by Mo segregation 12 and the hydrogen embrittlement of GBs. 13 Although nanocrystallization failed to surmount the common sense that strength and ductility are mutually exclusive, many other efforts have been made to explore strategies for simultaneously improving the strength and ductility of NC metals by designing the tunable nanostructures of materials in recent years.…”

Deep Learning-Based Image Segmentation for Al-La Alloy Microscopic Images

“…According to this as well as Figure 6, this could be rectified by the output mask of the previous mask and the last mask. Therefore, we propose a symmetric rectification method to recover the under-segmentation errors caused by contaminations, as described in Equation (2).…”

“…The quantitative analysis of microstructures is essential in the control the properties and performances of metals or alloys [1,2]. An important step in this process is microscopic image processing [3] which is used to extract significant information in a microstructure [4].…”

“…First, the microstructure of many samples is highly complex [5]. For example, there are hundreds of substructures with different shapes and sizes (e.g., "dendritic structure" in Al-La alloy [6], "grains" in polycrystalline iron [2], or "cells" in biomaterials [7], etc.) which must be accurately segmented in each image.…”