Evidence map›Paper›PMID 38893955›Full record

ArticleMaterials (Basel, Switzerland)2024

Material Removal Mechanism of SiC Ceramic by Porous Diamond Grinding Wheel Using Discrete Element Simulation.

Zhaoqin Zhang, Jiaxuan Xu, Yejun Zhu, Zhongxing Zhang, Weiqi Zeng

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Zhaoqin ZhangCollege of Engineering, Nanjing Agricultural University, Nanjing 210031, China.
Jiaxuan XuCollege of Engineering, Nanjing Agricultural University, Nanjing 210031, China.
Yejun ZhuCollege of Engineering, Nanjing Agricultural University, Nanjing 210031, China.
Zhongxing ZhangCollege of Engineering, Nanjing Agricultural University, Nanjing 210031, China.
Weiqi ZengCollege of Engineering, Nanjing Agricultural University, Nanjing 210031, China.

Funding

National Natural Science Foundation of China 52305474Natural Science Foundation of Jiangsu Province BK20210407
6 · The paper itself

Abstract

SiC ceramics are typically hard and brittle materials. Serious surface/subsurface damage occurs during the grinding process due to the poor self-sharpening ability of monocrystalline diamond grits. Nevertheless, recent findings have demonstrated that porous diamond grits can achieve high-efficiency and low-damage machining. However, research on the removal mechanism of porous diamond grit while grinding SiC ceramic materials is still in the bottleneck stage. A discrete element simulation model of the porous diamond grit while grinding SiC ceramics was established to optimize the grinding parameters (e.g., grinding wheel speed, undeformed chip thickness) and pore parameters (e.g., cutting edge density) of the porous diamond grit. The influence of these above parameters on the removal and damage of SiC ceramics was explored from a microscopic perspective, comparing with monocrystalline diamond grit. The results show that porous diamond grits cause less damage to SiC ceramics and have better grinding performance than monocrystalline diamond grits. In addition, the optimal cutting edge density and undeformed chip thickness should be controlled at 1-3 and 1-2 um, respectively, and the grinding wheel speed should be greater than 80 m/s. The research results lay a scientific foundation for the efficient and low-damage grinding of hard and brittle materials represented by SiC ceramics, exhibiting theoretical significance and practical value.

Indexed as

DEMgrindingporous diamondself-sharpeningSiC

Identifiers

PMID38893955
PMCPMC11173562

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.