Authors: Chengying Xu, Yan Tang, Mark J. Jackson
Addresses: Department of Mechanical, Materials and Aerospace Engineering, University of Central Florida, 4000 Central Florida Blvd, FL 32816, USA. ' Department of Mechanical, Materials and Aerospace Engineering, University of Central Florida, 4000 Central Florida Blvd, FL 32816, USA. ' Department of Mechanical Engineering Technology, Purdue University, West Lafayette, IN 47907, USA
Abstract: 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.
Keywords: intelligent grinding; adaptive control; robust control; system online identification; artificial intelligence; expert systems; neural networks; fuzzy logic; microgrinding.
International Journal of Nanomanufacturing, 2009 Vol.3 No.4, pp.398 - 408
Published online: 28 Jul 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article