Scratching of copper with rough surfaces conducted by diamond tip simulated using molecular dynamics

Li, Jia; Fang, Qihong; Liu, Youwen; Zhang, Liangchi

doi:10.1007/s00170-014-6536-6

Cited by 32 publications

(9 citation statements)

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“…The cutting force was computed from

F \textrm{ } = \textrm{ } \sum_{i = 1}^{N} \underset{j}{\sum} \frac{\partial U \left(\right. r_{i j} \left.\right)}{r_{i j}}

, [ 29–32 ] where

U \left(\right. r_{i j} \left.\right)

is the potential energy, and r ij is the distance.…”

Section: Methodsmentioning

confidence: 99%

“…[ 28 ] The initial workpiece temperature was chosen as 293 K. Meanwhile, the thermostat atoms temperature was kept at 293 K by the velocity‐rescaling approach over the MD‐machining process for achieving heat dissipation. The temperature was derived from

T = \langle\sum_{i = 1}^{N} m_{i} v_{i}^{2}\rangle/ 3 N k_{\text{B}}

, [ 29–32 ] where k B is the Boltzmann constant, N is the number of atoms, m i is the mass of the i th atom, v i is the velocity of the i th atom, and is the statistical averaging over the entire simulation time. The cutting force was computed from

$F \textrm{ } = \textrm{ } \sum_{i = 1}^{N} \underset{j}{\sum} \frac{\partial U \left(\right.

…”

Section: Methodsmentioning

confidence: 99%

“…r ij , [29][30][31][32] where Uðr ij Þ is the potential energy, and r ij is the distance. The scale effect can be effectively reduced by setting the y direction as the periodic boundary condition.…”

Section: Molecular Dynamicsmentioning

confidence: 99%

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Physics‐Informed Multistage Machine Learning Strategy for the Nanomachining‐Induced Plastic Deformation Behavior

Xie

Peng

et al. 2023

Adv Eng Mater

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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.

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“…The cutting force was computed from

F \textrm{ } = \textrm{ } \sum_{i = 1}^{N} \underset{j}{\sum} \frac{\partial U \left(\right. r_{i j} \left.\right)}{r_{i j}}

, [ 29–32 ] where

U \left(\right. r_{i j} \left.\right)

is the potential energy, and r ij is the distance.…”

Section: Methodsmentioning

confidence: 99%

T = \langle\sum_{i = 1}^{N} m_{i} v_{i}^{2}\rangle/ 3 N k_{\text{B}}

$F \textrm{ } = \textrm{ } \sum_{i = 1}^{N} \underset{j}{\sum} \frac{\partial U \left(\right.

…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Physics‐Informed Multistage Machine Learning Strategy for the Nanomachining‐Induced Plastic Deformation Behavior

Xie

Peng

et al. 2023

Adv Eng Mater

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently, this method has been effectively used to solve a wide variety of tasks at the atomic level concerned with single crystals. These studies were most often related to the study of the evolution of plastic deformation in crystals subjected to torsion [19], tension-compression [20][21][22][23][24][25][26], different surface impacts such as nanoindentation [27], scratching [12,[28][29][30], cutting [31][32][33], burnishing [34], etc. A. Dmitriev et al [35] performed simulation of uniaxial compression of FCC metal single crystals along the <001> direction and showed that the evolution of deformation relief and dislocation structure in these crystals is determined by their energetic characteristics, in particular, by the stacking fault energy (SFE).…”

Section: Introductionmentioning

confidence: 99%

Numerical Study and Experimental Validation of Deformation of <111> FCC CuAl Single Crystal Obtained by Additive Manufacturing

Nikonov

Дмитриев

Лычагин

et al. 2021

Metals

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The importance of taking into account directional solidification of grains formed during 3D printing is determined by a substantial influence of their crystallographic orientation on the mechanical properties of a loaded material. This issue is studied in the present study using molecular dynamics simulations. The compression of an FCC single crystal of aluminum bronze was performed along the <111> axis. A Ni single crystal, which is characterized by higher stacking fault energy (SFE) than aluminum bronze, was also considered. It was found that the first dislocations started to move earlier in the material with lower SFE, in which the slip of two Shockley partials was observed. In the case of the material with higher SFE, the slip of a full dislocation occurred via successive splitting of its segments into partial dislocations. Regardless of the SFE value, the deformation was primarily occurred by means of the formation of dislocation complexes involved stair-rod dislocations and partial dislocations on adjacent slip planes. Hardening and softening segments of the calculated stress–strain curve were shown to correspond to the periods of hindering of dislocations at dislocation pileups and dislocation movement between them. The simulation results well agree with the experimental findings.

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“…The following are available online at , Figure S1: Evaluation of simulation box size: (a) the tangential forces for three models, and (b) the friction coefficient at three scratch depths; Figure S2: (a) Tangential force and normal force at the depth of 3.0 r 0 in three samples, and (b) the friction coefficient at different scratch depths. References [ 33 , 55 , 56 ] were cited in Supplementary Materials.…”

mentioning

confidence: 99%

Friction and Wear in Nanoscratching of Single Crystals: Effect of Adhesion and Plasticity

Zeng

2022

Nanomaterials

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Friction and wear are two main tribological behaviors that are quite different for contact surfaces of distinct properties. Conventional studies generally focus on a specific material (e.g., copper or iron) such that the tribological result is not applicable to the other contact systems. In this paper, using a group of virtual materials characterized by coarse-grained potentials, we studied the effect of interfacial adhesion and material plasticity on friction and wear by scratching a rigid tip over an atomic smooth surface. Due to the combined effects of adhesion and plasticity on the nanoscratch process, the following findings are revealed: (1) For shallow contact where interfacial adhesion dominates friction, both friction coefficient and wear rate increase as the adhesion increases to a critical value. For deep contact where plasticity prevails, the variation of friction coefficient and wear rate is limited as the adhesion varies. (2) For weak and strong interfacial adhesions, the friction coefficient exhibits different dependence on the scratch depth, whereas the wear rate becomes higher as the scratch depth increases. (3) As the material hardness increases, both the friction coefficient and wear rate decrease in shallow and deep contacts.

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Scratching of copper with rough surfaces conducted by diamond tip simulated using molecular dynamics

Cited by 32 publications

References 50 publications

Physics‐Informed Multistage Machine Learning Strategy for the Nanomachining‐Induced Plastic Deformation Behavior

Physics‐Informed Multistage Machine Learning Strategy for the Nanomachining‐Induced Plastic Deformation Behavior

Numerical Study and Experimental Validation of Deformation of <111> FCC CuAl Single Crystal Obtained by Additive Manufacturing

Friction and Wear in Nanoscratching of Single Crystals: Effect of Adhesion and Plasticity

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