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...
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| Format: | Article |
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MDPI AG
2022-02-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/15/5/1707 |
| _version_ | 1797474643881230336 |
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| author | Wei Zhang Kangning Li Weiran Wang Ben Wang Lei Zhang |
| author_facet | Wei Zhang Kangning Li Weiran Wang Ben Wang Lei Zhang |
| author_sort | Wei Zhang |
| collection | DOAJ |
| 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%. |
| first_indexed | 2024-03-09T20:33:02Z |
| format | Article |
| id | doaj.art-62a7e1b5bb58423da518d016d041e714 |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-09T20:33:02Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-62a7e1b5bb58423da518d016d041e7142023-11-23T23:17:47ZengMDPI AGMaterials1996-19442022-02-01155170710.3390/ma15051707Analysis of High-Speed Milling Surface Topography and Prediction of Wear ResistanceWei Zhang0Kangning Li1Weiran Wang2Ben Wang3Lei Zhang4Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, ChinaKey Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, ChinaKey Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, ChinaKey Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, ChinaKey Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, ChinaSurface 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%.https://www.mdpi.com/1996-1944/15/5/1707high-speed millingtopography parametersBP neural networkprediction of wear resistance |
| spellingShingle | Wei Zhang Kangning Li Weiran Wang Ben Wang Lei Zhang Analysis of High-Speed Milling Surface Topography and Prediction of Wear Resistance Materials high-speed milling topography parameters BP neural network 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 | high-speed milling topography parameters BP neural network prediction of wear resistance |
| url | https://www.mdpi.com/1996-1944/15/5/1707 |
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