Survey on various control techniques in micro grinding processes

被引：1

作者：

Department of Mechanical, Materials and Aerospace Engineering, University of Central Florida, 4000 Central Florida Blvd., Orlando, FL 32816, United States ^{[1
]}

不详 ^{[2
]}

机构：

[1] Department of Mechanical, Materials and Aerospace Engineering, University of Central Florida, Orlando, FL 32816

[2] Department of Mechanical Engineering Technology, Purdue University, West Lafayette

来源：

Int. J. Nanomanufacturing | 2009年 / 4卷 / 398-408期

关键词：

Adaptive control; Artificial intelligence; Expert system; Fuzzy logic; Grinding; Neural network; Robust control; System online identification;

D O I：

10.1504/IJNM.2009.027503

中图分类号：

学科分类号：

摘要：

Due to the highly demanding geometric accuracy and surface finish for many modern products, grinding processes have been extensively used in manufacturing industry. However, it is also well accepted that grinding is one of the most complicated machining processes due to the high non-linearities, intrinsic uncertainties and time-varying characteristics. Multiple challenging problems exist in the process that limits its overall quality and production in practice. With the increasing demands for higher part geometry accuracy, better surface integrity, more productivity and other desired product parameters (e.g., minimisation of subsurface micro-damage) with less operator intervention, various control methods have been studied and implemented to control position, velocity, force, power, temperature and the material removal rate (MRR) during the grinding process, in order to achieve the desired system performance within certain cost/time. This paper reviews different control strategies in order to provide a guideline for academic researchers and industrial practitioners in improving the final product quality with increased possible process flexibility. Copyright © 2009, Inderscience Publishers.

引用

页码：398 / 408

页数：10

共 43 条

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