Model-based predictive control with fuzzy characterization of goals and constraints, applied to the dynamic optimization of grinding plants

Orchard, M.; Flores, Alejandro; Muñoz, C.; Cipriano, Aldo

doi:10.1109/fuzz.2001.1009105

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…The hybrid control system was implemented in an open architecture system for practical applications. Orchard et al (2001aOrchard et al ( , 2001b presented a fuzzy predictive controller for a mineral concentration grinding plant in order to maximise the ore feed rate and to follow a predetermined particle size set-point. The process was a typical multi-input multi-output system, where the inputs were water and fresh ore flows, the outputs were percentage of solids, particle size and power demand.…”

Section: Hybrid Control Schemesmentioning

confidence: 99%

Survey on various control techniques in micro grinding processes

Tang

Jackson

2009

IJNM

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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.

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Section: Hybrid Control Schemesmentioning

confidence: 99%