基于递归模糊推理的磨削过程表面粗糙度在线估计

F. Kobayashi, F. Arai, T. Fukuda, M. Onoda, Norimasa Marui
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引用次数: 0

摘要

在制造系统中,经常使用磨削工艺来产生光滑的表面。近年来,由于磨刀石的长期使用,磨削系统需要对表面粗糙度进行测量。然而,表面粗糙度的测量比较困难,并且在加工过程中测量需要花费很长时间。因此,我们必须利用在线传感信息来估计加工过程中的表面粗糙度。在本文中,我们提出了一种具有循环输入的递归模糊推理(RFI),并将其应用于多传感器融合系统中用于估计表面粗糙度。RFI的隶属函数用范围不敏感的径向基函数(RBF)表示。RFI的学习方法是基于最陡下降法和增量学习,可以添加新的模糊规则。其中,新模糊规则的形状由先前模糊规则的适应度决定。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Online Estimation of Surface Roughness by Recurrent Fuzzy Inference in Grinding Process
Grinding process is frequently used to produce a smooth surface in the manufacturing system. Recently, for using a whetstone for a long time, grinding system needs to measure the surface roughness. However, it is difficult to measure the surface roughness and it takes a long time to measure it in process. Thus, we have to estimate surface roughness in process by online sensing information. In this paper, we propose a Recurrent Fuzzy Inference (RFI) with recurrent inputs and is applied to a multi-sensor fusion system for estimating the surface roughness. The membership functions of RFI are expressed by Radial Basis Function (RBF) with insensitive ranges. The learning method of RFI is based on the steepest descent method and incremental learning which can add new fuzzy rules. Where, the shape of new fuzzy rules is determined by the fitness of previous fuzzy rules.
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