On precision improvement by ultrasonics-aided electrodischarge machining

The need for a closely defined surface texture on steel rolls used to produce steel sheet is described. Conventional shot-blasting as a method of texturing is shown to be increasingly limited by lack of adequate process control and its inability to treat hard alloys. Electrodischarge texturing (EDT) is presented as an alterative method which overcomes the difficulties encountered with the traditional technique. The mechanisms controlling EDT are shown to depend on the peak current arising from the pulsed electric field applied between the tool-electrode and the work-roll, the time for which the voltage pulse is applied, and the randomness of the discharges produced in the machining gap. These process conditions are included in a model for the process, which is then analysed in the light of physical behaviour known to arise in electrodischarge texturing, namely that the surface roughness produced increases with peak current and with the pulse time for the applied voltage. Attention is drawn to the implications of the model for electrodischarge machining process which has, in general, hitherto proved difficult to analyse, and yet is known to be subject to similar effects of peak current and the ratios of the times for which the pulsed voltage is applied and removed. The present paper therefore also throws light on methods for tackling the general theory of electrodischarge machining which is so widely used in industry.

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Relevance Vector Machine Based Analyses of MRR and SR of Electrodischarge Machining Designed by Response Surface Methodology

International Journal of Manufacturing Engineering ◽

10.1155/2013/170746 ◽

2013 ◽

Vol 2013 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Kanhu Charan Nayak ◽

Rajesh Kumar Tripathy ◽

Sudha Rani Panda

Keyword(s):

Response Surface Methodology ◽

Response Surface ◽

Duty Cycle ◽

Discharge Current ◽

Removal Rate ◽

Relevance Vector Machine ◽

Machining Process ◽

Machining Parameters ◽

Electrodischarge Machining ◽

Pulse On Time

Relevance vector machine is found to be one of the best predictive models in the area of pattern recognition and machine learning. The important performance parameters such as the material removal rate (MRR) and surface roughness (SR) are influenced by various machining parameters, namely, discharge current (Ip), pulse on time (Ton), and duty cycle (tau) in the electrodischarge machining process (EDM). In this communication, the MRR and SR of EN19 tool steel have been predicted using RVM model and the analysis of variance (ANOVA) results were performed by implementing response surface methodology (RSM). The number of input parameters used for the RVM model is discharge current (Ip), pulse on time (Ton), and duty cycle (tau). At the output, the corresponding model predicts both MRR and SR. The performance of the model is determined by regression test error which can be obtained by comparing both predicted MRR and SR from model and experimental data is designed using central composite design (CCD) based RSM. Our result shows that the regression error is minimized by using cubic kernel function based RVM model and the discharge current is found to be one of the most significant machining parameters for MRR and SR from ANOVA.

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