Cargando…
Analysis of High-Speed Milling Surface Topography and Prediction of Wear Resistance
Surface topography parameters are an important factor affecting the wear resistance of parts, and topography parameters are affected by process parameters in order to explore the influence law of process parameters on surface topography parameters and to find the quantitative relationship between mi...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8911380/ https://www.ncbi.nlm.nih.gov/pubmed/35268949 http://dx.doi.org/10.3390/ma15051707 |
_version_ | 1784666788037918720 |
---|---|
author | Zhang, Wei Li, Kangning Wang, Weiran Wang, Ben Zhang, Lei |
author_facet | Zhang, Wei Li, Kangning Wang, Weiran Wang, Ben Zhang, Lei |
author_sort | Zhang, Wei |
collection | PubMed |
description | Surface topography parameters are an important factor affecting the wear resistance of parts, and topography parameters are affected by process parameters in order to explore the influence law of process parameters on surface topography parameters and to find the quantitative relationship between milling surface topography parameters and wear resistance. Firstly, this paper took the surface after high-speed milling as the research object, established the residual height model of the milled surface based on static machining parameters, and analyzed the relationship between the residual height of the surface and the machining parameters. Secondly, a high-speed milling experiment was designed to explore the influence law of processing parameters on surface topography and analyzed the influence law of processing parameters on specific topography parameters; Finally, a friction and wear experiment was designed. Based on the BP neural network, the wear resistance of the milled surface in terms of wear amount and friction coefficient was predicted. Through experimental verification, the maximum error of the prediction model was 16.39%, and the minimum was 6.18%. |
format | Online Article Text |
id | pubmed-8911380 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-89113802022-03-11 Analysis of High-Speed Milling Surface Topography and Prediction of Wear Resistance Zhang, Wei Li, Kangning Wang, Weiran Wang, Ben Zhang, Lei Materials (Basel) Article Surface topography parameters are an important factor affecting the wear resistance of parts, and topography parameters are affected by process parameters in order to explore the influence law of process parameters on surface topography parameters and to find the quantitative relationship between milling surface topography parameters and wear resistance. Firstly, this paper took the surface after high-speed milling as the research object, established the residual height model of the milled surface based on static machining parameters, and analyzed the relationship between the residual height of the surface and the machining parameters. Secondly, a high-speed milling experiment was designed to explore the influence law of processing parameters on surface topography and analyzed the influence law of processing parameters on specific topography parameters; Finally, a friction and wear experiment was designed. Based on the BP neural network, the wear resistance of the milled surface in terms of wear amount and friction coefficient was predicted. Through experimental verification, the maximum error of the prediction model was 16.39%, and the minimum was 6.18%. MDPI 2022-02-24 /pmc/articles/PMC8911380/ /pubmed/35268949 http://dx.doi.org/10.3390/ma15051707 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhang, Wei Li, Kangning Wang, Weiran Wang, Ben Zhang, Lei Analysis of High-Speed Milling Surface Topography and Prediction of Wear Resistance |
title | Analysis of High-Speed Milling Surface Topography and Prediction of Wear Resistance |
title_full | Analysis of High-Speed Milling Surface Topography and Prediction of Wear Resistance |
title_fullStr | Analysis of High-Speed Milling Surface Topography and Prediction of Wear Resistance |
title_full_unstemmed | Analysis of High-Speed Milling Surface Topography and Prediction of Wear Resistance |
title_short | Analysis of High-Speed Milling Surface Topography and Prediction of Wear Resistance |
title_sort | analysis of high-speed milling surface topography and prediction of wear resistance |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8911380/ https://www.ncbi.nlm.nih.gov/pubmed/35268949 http://dx.doi.org/10.3390/ma15051707 |
work_keys_str_mv | AT zhangwei analysisofhighspeedmillingsurfacetopographyandpredictionofwearresistance AT likangning analysisofhighspeedmillingsurfacetopographyandpredictionofwearresistance AT wangweiran analysisofhighspeedmillingsurfacetopographyandpredictionofwearresistance AT wangben analysisofhighspeedmillingsurfacetopographyandpredictionofwearresistance AT zhanglei analysisofhighspeedmillingsurfacetopographyandpredictionofwearresistance |