Adaptive Control System for Optimizing the ECG Process under the Overcut Constraint
One of the main limitations 1n Peripheral Electro Chemical Grinding (ECG) is the overcut phenomenon. The dominant parameters which govern the dimensions of the geometrical overcut are the voltage and the feedrate. This paper deals with the development of an empirical model which describes the overcut as a function of the dominant parameters. Pased on this model an adpative control system was built and experiments in Profile Peripheral ECG were carried out. A comparison of the results obtained with and without the adaptive control system is presented as well as the relationship between the side and bottom overcuts. According to the above an adaptive strategy to control the geometrical overcut in Profile ECG is suggested.
MODELING OF ELECTROCHEMICAL GRINDING PROCESS A THESIS SUBMITTED FOR PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE AWARD OF THE DEGREE OF MASTER OF SCIENCE IN MECHANICAL ENGINEERING
Electrochemical Grinding (ECG) process is a mechanically assisted electro-chemical process for material processing. The process is able to successfully machine electrically conducting harder materials at faster rate with improved surface finish and dimensional control. The process generates burr-free, stress free surface, unlike mechanical grinding at a lower cost of production. The material removal mechanism and the parametric effects of different process parameters on material removal rate have extensively been analysed. The interrelationship amongst the process parameters has been established. The thesis includes the development of mathematical modeling for metal removal rate (MRR) in ECG process. The relative contribution of electrochernical dissolution and mechanical action to overall material removal rate has been evaluated. An experimental set-up has also been designed for electrochemical grinding process.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.