Evidence map›Paper›PMID 33530323›Full record

ArticleMicromachines2021

Experimental Study on Ultrasonic Vibration-Assisted WECDM of Glass Microstructures with a High Aspect Ratio.

Yan Chen, Xu Feng, Gongming Xin

Abstract read
In one paragraph

Article in Micromachines, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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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

3 authors.

Yan ChenSchool of Energy and Power Engineering, Shandong University, Jinan 250061, China.
Xu FengSchool of Energy and Power Engineering, Shandong University, Jinan 250061, China.
Gongming XinSchool of Energy and Power Engineering, Shandong University, Jinan 250061, China.

Funding

Fundamental Research Funds of Shandong University 2018JC037Shandong Provincial Key Research and Development Program 2019JZZY010454Shandong Provincial Natural Science Foundation ZR2019MEE024Shenzhen Basic Research Program JCYJ20190807092801669
6 · The paper itself

Abstract

With the rapid development of micro-electro-mechanical systems (MEMSs), the demand for glass microstructure is increasing. For the purpose of achieving high quality and stable machining of glass microstructures with a high aspect ratio, ultrasonic vibration is applied into the micro-wire electrochemical discharge machining (WECDM), which is proposed as ultrasonic vibration-assisted WECDM with a micro helical electrode. Firstly, the formation of a gas film on the surface of the helical electrode in WECDM machining is simulated, meaning the thickness of the gas film can be reduced by adding suitable ultrasonic amplitude, thus reducing the critical voltage, then the machining localization and stability were enhanced. Then, the micro helical electrode with a diameter of 100 μm is used to carry out sets of experiments that study the influence of ultrasonic amplitude, machining voltage, duty factor, pulse frequency, and feed rate on the slit width. The experimental results show that the machining stability and quality are significantly improved by adding suitable ultrasonic amplitude. When the amplitude was 5.25 μm, the average slit width was reduced to 128.63 μm with a decrease of 20.78%. Finally, with the optimized machining parameters, micro planar coil structure and microcantilever structure with a high aspect ratio were fabricated successfully on the glass plate. It is proved that ultrasonic vibration-assisted WECDM with the micro helical electrode method can meet the requirements of high aspect ratio microstructure machining for hard and brittle materials.

Indexed as

glass microstructureshelical electrodehigh aspect ratioultrasonic vibrationWECDM

Identifiers

PMID33530323
PMCPMC7912486

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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.