珩磨过程中Abbott Firestone粗糙度参数的神经网络建模

IF 1 4区 工程技术 Q4 ENGINEERING, MECHANICAL International Journal of Surface Science and Engineering Pub Date : 2017-12-29 DOI:10.1504/IJSURFSE.2017.088973
I. B. Corral, Xavier Parra, Mauricio Sivatte Adroer
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引用次数: 4

摘要

本研究采用人工神经网络(ANN)对abbot - firestone或轴承面积曲线中定义的三个粗糙度参数Rk、Rpk和Rvk进行建模。输入变量为磨料的粒度和密度、工件表面磨料的压力、工件的切向速度或转速以及珩磨头的线速度。考虑了两种策略,要么使用一个网络同时对三个参数建模,要么使用三个网络,每个参数一个。总的来说,最好的神经网络由三个网络组成,每个网络对应一个粗糙度参数,其中一个隐藏层分别有25个、9个和5个神经元,分别对应Rk、Rpk和Rvk。然而,对于三个粗糙度参数使用一个网络将允许寻址一个间接模型。在这种情况下,最佳解决方案对应于两个隐藏层,分别有26个和11个神经元。
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Neural network modelling of Abbott-Firestone roughness parameters in honing processes
In present study, three roughness parameters defined in the Abbott-Firestone or bearing area curve, Rk, Rpk and Rvk, were modelled for rough honing processes by means of artificial neural networks (ANN). Input variables were grain size and density of abrasive, pressure of abrasive stones on the workpiece's surface, tangential or rotation speed of the workpiece and linear speed of the honing head. Two strategies were considered, either use of one network for modelling the three parameters at the same time or use of three networks, one for each parameter. Overall best neural network consists of three networks, one for each roughness parameter, with one hidden layer having 25, nine and five neurons for Rk, Rpk and Rvk respectively. However, use of one network for the three roughness parameters would allow addressing an indirect model. In this case, best solution corresponds to two hidden layers having 26 and 11 neurons.
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来源期刊
CiteScore
1.60
自引率
25.00%
发文量
21
审稿时长
>12 weeks
期刊介绍: IJSurfSE publishes refereed quality papers in the broad field of surface science and engineering including tribology, but with a special emphasis on the research and development in friction, wear, coatings and surface modification processes such as surface treatment, cladding, machining, polishing and grinding, across multiple scales from nanoscopic to macroscopic dimensions. High-integrity and high-performance surfaces of components have become a central research area in the professional community whose aim is to develop highly reliable ultra-precision devices.
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