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A neural networks-based in-process adaptive surface roughness control (NN-IASRC) system in end-milling operations.

机译：端铣削中基于神经网络的过程中自适应表面粗糙度控制（NN-IASRC）系统。

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摘要

In this research, the neural networks-based in-process adaptive surface roughness control (NN-IASRC) system employing multiple cutting tools was successfully developed for end-milling operations. The dynamometer sensor was used to monitor the uncontrolled cutting tool conditions to increase the accuracy of the surface roughness control. An empirical approach was applied to discover the proper cutting force signals, the average resultant peak force in XY plane ( Fap) and the absolute average force in the Z direction (Faz). These two forces were employed to represent the uncontrollable cutting tool conditions for surface roughness control. A statistical method was employed to verify that the cutting tools could influence the surface roughness, and obtain the correlation between surface roughness and the cutting force signals for the preparation of constructing the NN-IASRC system.; A neural networks theorem was successfully applied to build the NN-IASRC system. The neural networks associated with sensing technology were applied as a decision-making technique to control the surface roughness for a wide range of machining parameters. The NN-IASRC system consisted of two subsystems. One was the in-process neural networks based surface roughness prediction (INN-SRP) system, which was employed to predict the surface roughness. The other was the neural networks based adaptive machining parameters control (NN-APMC) system, which was utilized to adjust the adaptive degree of feed rate when the quality of predicted surface roughness did not fit the desired one. The accuracy of the INN-SRP system was 93%, and 100% for the NN-IASRC system. The high accuracy of results within a wide range of machining parameters indicates that the system can be practically applied in industry.
机译：在这项研究中，成功​​开发了基于神经网络的过程中自适应表面粗糙度控制（NN-IASRC）系统，该系统采用了多种切削工具，用于端铣削加工。测力计传感器用于监视不受控制的切削刀具状况，以提高表面粗糙度控制的准确性。应用经验方法发现适当的切削力信号，XY平面上的平均合力峰值（ ap ）和Z方向上的绝对平均力（< italic> F az ）。这两个力被用来代表不可控制的切削刀具状态，以控制表面粗糙度。采用统计方法验证了切削刀具是否会影响表面粗糙度，并获得了表面粗糙度与切削力信号之间的相关性，为构造NN-IASRC系统做准备。神经网络定理已成功应用于构建NN-IASRC系统。与传感技术相关的神经网络被用作决策技术，以控制各种加工参数的表面粗糙度。 NN-IASRC系统由两个子系统组成。一种是基于过程神经网络的表面粗糙度预测（INN-SRP）系统，该系统用于预测表面粗糙度。另一个是基于神经网络的自适应加工参数控制（NN-APMC）系统，当预测的表面粗糙度的质量与所需的粗糙度不匹配时，该系统用于调整进给速率的自适应程度。 INN-SRP系统的准确性为93％，而NN-IASRC系统的准确性为100％。在广泛的加工参数范围内，结果的高精度表明该系统可以在工业上实际应用。

著录项

  • 作者

    Huang, Po-Tsang Bernie.;

  • 作者单位

    Iowa State University.;

  • 授予单位 Iowa State University.;
  • 学科 Engineering Industrial.
  • 学位 Ph.D.
  • 年度 2002
  • 页码 147 p.
  • 总页数 147
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 一般工业技术;
  • 关键词

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