Evidence map›Paper›PMID 35160759›Full record

ArticleMaterials (Basel, Switzerland)2022

Investigation of the Effect of Temperature on the Wear Rate and Airborne Noise in Sliding Wear.

Kevin Lontin, Muhammad Khan, Bander Alharbi

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2022. 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

3 authors.

Kevin LontinSchool of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield MK43 0AL, UK.
Muhammad KhanSchool of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield MK43 0AL, UK.ORCID 0000-0001-9028-1288
Bander AlharbiSchool of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield MK43 0AL, UK.ORCID 0000-0001-8629-7957

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

When friction processes occur, wear is generated. The generation of wear also leads to airborne noise. There have been many research studies on wear and its correlation with airborne noise, but most research has focused on experimental aspects, and theoretical models are rare. Furthermore, analytical models do not fully account for the wear and airborne noise generation, especially at an asperitical level. One model was developed that gave a reasonable quantification for the relationship between wear and airborne noise generation at an asperitical level under room temperature. In this paper, the accuracy of the model is assessed at higher temperatures. Two materials were set up on a tribometer (aluminium and iron) at 300 RPM. The samples were tested at two different temperatures (40 and 60 degrees) and two different loads were applied (10 N and 20 N). The model computed the predicted wear and sound pressure, and it was compared with the experimental results. The errors are larger for the wear than when the model was validated at room temperature. However, the increase in the error for the sound pressure was smaller at higher temperatures (approximately 20-30%). This is due to the assumptions that were made in the initial model, which are exacerbated when higher temperatures are applied. For example, flash temperatures were neglected in the original model. However, when initial heat is applied, the effects of flash temperatures could be more significant than when no heat is applied. Further refinements could improve the accuracy of the model to increase its validity in a wider temperature range.

Indexed as

airborne noisefrictionwear

Identifiers

PMID35160759
PMCPMC8836526

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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.