Surface textures have been of great interest in the tribology community with nearly 1500 papers published on this topic in the past two decades. With pursuit of low emissions and environmental sustainability, the application of surface texturing to mechanical systems to lower friction and control wear is attracting increasing attention. There is no doubt that certain textured surface can have a beneficial effect on tribological performance but it is widely agreed that the optimization of textures should be carried out based on specific applications requirements. The purpose of this review article is to summarize the current state of the art in surface texturing applied to mechanical applications (cutting tools, piston-ring & cylinder liners, sealing and journal bearings) from the following aspects: application requirements, numerical/ experimental testing and validations, and tribological performance of textured surfaces (wear and friction), as well as limitations in texture designs according to certain applications. Patterns/grooves in micron-scale are the most typical shapes been studied, and benefits of partial texturing are applicable for most of these mechanical applications. Friction reduction of up to 34.5% in cutting tools, 82% in piston-ring & cylinder-liner, 65% in seals and 18% in journal bearings have been observed by experimental tests. Based on primary evidence from the literature, the last section provides general suggestions on current gaps in understanding and modelling and suggests future research directions.

https://iopscience.iop.org/article/10.1088/2051-672X/abb6d0/pdf

Tribological performance of surface texturing in mechanical applications—a review

Building trust in science and evidence-based decision-making depends heavily on the credibility of studies and their findings. Researchers employ many different study designs that vary in their risk of bias to evaluate the true effect of interventions or impacts. Here, we empirically quantify, on a large scale, the prevalence of different study designs and the magnitude of bias in their estimates. Randomised designs and controlled observational designs with pre-intervention sampling were used by just 23% of intervention studies in biodiversity conservation, and 36% of intervention studies in social science. We demonstrate, through pairwise within-study comparisons across 49 environmental datasets, that these types of designs usually give less biased estimates than simpler observational designs. We propose a model-based approach to combine study estimates that may suffer from different levels of study design bias, discuss the implications for evidence synthesis, and how to facilitate the use of more credible study designs.

/pdf/quantifying-and-addressing-the-prevalence-and-bias-of-study-2hfj55yqor.pdf

Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences

Contact Mechanics and Wear of Rail/Wheel Systems

Ball-on-flat linear reciprocating tests: Critical assessment of wear volume determination methods and suggested improvements for ASTM D7755 standard

Functionalization of graphene-based materials: Effective approach for enhancement of tribological performance as lubricant additives

An analysis of the quality of experimental design and reliability of results in tribology research

There are two options for testing the performance of rail vehicles and the application of material to the wheel/rail interface to influence friction, and therefore braking performance, in a low adh...

https://journals.sagepub.com/doi/pdf/10.1177/0954409720973135

Field trials of a methodology for locomotive brake testing to assess friction enhancement in the wheel/rail interface using a representative leaf layer:

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Peter Krier

Author Tools

Chat about Author

Papers

An analysis of the quality of experimental design and reliability of results in tribology research

Field trials of a methodology for locomotive brake testing to assess friction enhancement in the wheel/rail interface using a representative leaf layer: