Evidence map›Paper›PMID 40047667›Full record

ArticleMicromachines2025

Development of a Next-Generation Cooling Channel Technology with High Cooling Efficiency by Roughing Cooling Channels Using a Combination of Laser Machining and Embossing Techniques.

Chil-Chyuan Kuo, Geng-Feng Lin, Armaan Farooqui, Song-Hua Huang, Shih-Feng Tseng

Abstract read
In one paragraph

Article in Micromachines, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Chil-Chyuan KuoDepartment of Mechanical Engineering, Ming Chi University of Technology No. 84, Gungjuan Road, New Taipei City 24301, Taiwan.ORCID 0000-0003-0519-4126
Geng-Feng LinDepartment of Mechanical Engineering, Ming Chi University of Technology No. 84, Gungjuan Road, New Taipei City 24301, Taiwan.
Armaan FarooquiDepartment of Mechanical Engineering, Ming Chi University of Technology No. 84, Gungjuan Road, New Taipei City 24301, Taiwan.
Song-Hua HuangLi-Yin Technology Co., Ltd., No. 37, Lane 151, Section 1, Zhongxing Road, Wugu District, New Taipei City 248012, Taiwan.
Shih-Feng TsengDepartment of Mechanical Engineering, National Taipei University of Technology, No. 1, Section 3, Zhongxiao E. Road, Da'an District, Taipei City 106344, Taiwan.ORCID 0000-0002-9630-1961

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study investigates the development of a rapid wax injection tooling with enhanced heat dissipation performance using aluminum-filled epoxy resin molds and cooling channel roughening technology. Experimental evaluations were conducted on cooling channels with eleven surface roughness variations, revealing that a maximum roughness of 71.9 µm achieved an 81.48% improvement in cooling efficiency compared to smooth channels. The optimal coolant discharge rate was determined to be 2 L/min. The heat dissipation time for wax patterns was significantly reduced, enabling a cooling time reduction of approximately 12 s per product. For a production scale of 100,000 units, this equates to a time savings of about 13 days. Empirical equations were established for estimating heat dissipation time and pressure drop, with a high coefficient of determination. This research provides a valuable contribution to the mold and dies manufacturing industry, offering practical solutions for sustainable and efficient production processes.

Indexed as

aluminum-filled epoxy resin moldscooling efficiencyheat dissipationsurface roughness

Identifiers

PMID40047667
PMCPMC11857225

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.