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高速铣削表面形貌分析与耐磨性预测

Analysis of High-Speed Milling Surface Topography and Prediction of Wear Resistance.

作者信息

Zhang Wei, Li Kangning, Wang Weiran, Wang Ben, Zhang Lei

机构信息

Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China.

College of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin 150080, China.

出版信息

Materials (Basel). 2022 Feb 24;15(5):1707. doi: 10.3390/ma15051707.

DOI:10.3390/ma15051707
PMID:35268949
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8911380/
Abstract

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%.

摘要

表面形貌参数是影响零件耐磨性的重要因素，且形貌参数受工艺参数影响。为探究工艺参数对表面形貌参数的影响规律，并找出铣削表面形貌参数与耐磨性之间的定量关系。首先，本文以高速铣削后的表面为研究对象，基于静态加工参数建立了铣削表面的残余高度模型，并分析了表面残余高度与加工参数之间的关系。其次，设计了高速铣削实验，探究加工参数对表面形貌的影响规律，并分析加工参数对特定形貌参数的影响规律；最后，设计了摩擦磨损实验。基于BP神经网络，从磨损量和摩擦系数方面对铣削表面的耐磨性进行了预测。通过实验验证，预测模型的最大误差为16.39%，最小误差为6.18%。

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