Intelligent Control Systems Using Soft Computing Methodologies
Intelligent Control Systems Using Soft Computing Methodologies
A Bayesian network model for surface roughness prediction in the machining process
International Journal of Systems Science
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Short Communication: Intelligent fuzzy accelerated method for the nonlinear 3-D crane control
Expert Systems with Applications: An International Journal
Modelling and predicting surface finish in turning using an adaptive neuro-fuzzy inference system
International Journal of Intelligent Engineering Informatics
Hi-index | 12.06 |
This paper discusses the development of an in-process surface roughness adaptive control system for a CNC turning operation, using fuzzy-nets modeling and tool vibrations measured with an accelerometer. The goal of this system is to predict the surface roughness of a surface being turned, determine if the surface roughness being generated is higher than the desired specification, and if so to adapt the feed rate of the turning operation in order to obtain a surface roughness no higher than that specified. Fuzzy-nets models for prediction of surface roughness and adapted feed rate were trained using feed rate, spindle speed, tangential vibration and measured surface roughness data collected during experimental runs. A series of validation runs indicated that the system could successfully meet its goal both in detecting out-of-spec surface roughness conditions, and adapting the machine tool to obtain a final surface roughness at or slightly below the desired surface roughness.