Evidence map›Paper›PMID 38930218›Full record

ArticleMaterials (Basel, Switzerland)2024

Use of Heat-Applied Coatings to Reduce Wear on Agricultural Machinery Components.

Dawid Romek, Jarosław Selech, Dariusz Ulbrich

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

3 authors.

Dawid RomekFaculty of Civil and Transport Engineering, Poznan University of Technology, 60-965 Poznan, Poland.ORCID 0000-0002-2658-1670
Jarosław SelechInstitute of Mechanical Science, Vilnius Gediminas Technical University, Plytinės Str. 25, LT-10105 Vilnius, Lithuania.ORCID 0000-0002-2656-3800
Dariusz UlbrichFaculty of Civil and Transport Engineering, Poznan University of Technology, 60-965 Poznan, Poland.ORCID 0000-0002-4154-1167

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article presents the effect of the conditions of abrasive compounds on the wear of samples made by different methods. The 28MnB5 steel was used, which is intended for agricultural components, to which two arc and laser coatings were applied. The study included the analysis of microstructure, microhardness, roughness, and tribological experiments on a dedicated stand. The arc coating was found to significantly improve the tribological properties compared to the samples without the coating. Varied wear results were obtained for the laser coating depending on the parameters of the abrasive compound. Studies of the surface roughness of the samples showed that the concentration and pH of the abrasives have a significant effect on the changes in the surface parameters after the tribological tests. The results of the tribological experiments indicated that wear resistance for some of the abrasive mass conditions was improved by the application of heat-applied coatings. In addition, it was found that the power consumption on the stand was the highest for abrasive mass conditions of a 10% moisture content and a pH of 10. For these test conditions, the mass loss was four times higher than for the parameter with W0% and pH7. The energy consumption of the stand was 60 kWh lower for this variant than for the parameter with W10% and pH10. The results of the study have important practical applications that can help in the selection of materials for agricultural machinery components, depending on the abrasive mass conditions.

Indexed as

abrasive wearenergyfrictionmoisturepaddingpHsoiltribology

Identifiers

PMID38930218
PMCPMC11205101

What OpenQuestion holds

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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.