Ravindra Nath Yadav

Multiobjective optimization of slotted electrical discharge abrasive grinding of metal matrix composite using artificial neural network and nondominated sorting genetic algorithm

The alternative use of electrical discharge grinding and abrasive grinding, which is applied with the application of slotted wheel named as slotted electrodischarge abrasive grinding, is much suitable for machining of metal matrix composites. But the selection of process parameters is a difficult task due to the complexity of the process. The aim of this study is to optimize the process parameters of slotted electrodischarge abrasive grinding process using a combined approach of artificial neural network and nondominated sorting genetic algorithm II. The artificial neural network architecture has been trained and tested with experimental data, and then the developed model is coupled with nondominated sorting genetic algorithm II to develop a hybrid approach of artificial neural network–nondominated sorting genetic algorithm II, which is used for optimization of process parameters. During experimentation, the effect of current, pulse on-time, pulse off-time, wheel speed and grit number has been studied on material removal rate and average surface roughness (Ra). The results have shown that prediction capability of artificial neural network model is within the range of acceptable limits. The developed hybrid approach of artificial neural network–nondominated sorting genetic algorithm II gives optimal solution with correlation coefficient of material removal rate and Ra as 0.9979 and 0.9982, respectively.

Influence of Input Parameters on Machining Performances of Slotted-Electrical Discharge Abrasive Grinding of Al/SiC/Gr Metal Matrix Composite

Hybrid machining processes are advantageous for machining of difficult-to-machine hard and brittle materials due to their superior performance as compared to the constituent processes. The aim of the present study is to propose a novel hybrid machining called Slotted-Electrical Discharge Abrasive Grinding (S-EDAG), which is developed by comprising the Electro-Discharge Grinding (EDG) and Abrasive Grinding (AG) in such a way that both processes (EDG and AG) occur alternatively with application of the compound grinding wheel. The performance of the developed process has been experimentally tested on Aluminum-Silicon Carbide-Graphite (Al/SiC/Gr) composite. For effective studies of the process behaviors, the experiments were performed according to one parameter at a time technique. All the experiments were conducted on EDM machine by fastening a separate attachment of rotary unit on its vertical column. During experimentation, the effect of pulse current, pulse duration, pulse interval, wheel speed, and grit number have been studied on material removal rate (MRR) and average surface roughness (Ra). The experimental results show that high MRR with moderate Ra can be achieved at pulse current = 15 A, pulse duration = 120 µs, pulse interval = 90 µs, and wheel speed = 1,300 RPM.

Application of Soft Computing Techniques for Modeling and Optimization of Slotted-Electrical Discharge Diamond Face Grinding Process

An innovative hybrid machining called as slotted-electrical discharge diamond face grinding (S-EDDFG) has been developed by comprising the electrical discharge face grinding and diamond face grinding. Due to complexity in S-EDDFG process, the selection of suitable combinations of input parameters is a challenging task for manufacturers. In recent, soft computing techniques are gaining more attention of researchers for modeling and optimization for the process parameters of complex manufacturing processes. In the present paper, two soft computing techniques known as artificial neural network (ANN) and non-dominated sorting genetic algorithm-II (NSGA-II) have been applied for modeling and optimization of process parameters (pulse current, pulse on-time, pulse off-time, wheel RPM and grit number) during machining of Aluminium–Silicon Carbide–Graphite (Al/SiC/Gr) composite. The results shown that two hidden layer ANN architecture was more appropriate to predict the material removal rate and average surface roughness during S-EDDFG of Al/SiC/Gr workpiece. The optimal results obtained by combined approach of ANN–NSGA-II are experimentally validated and found that optimal data are under acceptable limits.

Machining Performance of Slotted-Electrical Discharge Diamond Face Grinding of Al/SiC/Gr Composite

Electrical discharge diamond grinding (EDDG) is a well-known process for machining of electrically conductive hard and brittle materials. Even though, EDDG process suffers with several drawbacks such as loss of diamond particles without wear, high wheel wear, accumulation of debris into gap, abnormal arcing, etc. The aim of this study is to minimize the drawbacks of EDDG process with an alternative application of spark erosion and abrasive abrasion actions. Such developed process is knows as slotted-electrical discharge diamond grinding (S-EDDG) process. The performance of developed process was tested on face grinding mode of the S-EDDG during machining of the aluminum–silicon carbide–graphite (Al/SiC/Gr) workpiece. The role of current, pulse on-time, pulse off-time, wheel rotation per minute (RPM), and abrasive grit number has been investigated on material removal rate (MRR) and average surface roughness (Ra). It has been observed that higher wheel RPM gives higher MRR with better surface finish due to increase in flushing efficiency.

Experimental investigations of slotted electrical discharge abrasive grinding of Al/SiC/Gr composite

Machining of metal matrix composites has always been challenges for manufacturing engineers due to the presence of hard and brittle reinforced particles. In this article, a new way of alternate application of electrical discharge grinding and abrasive grinding has been applied through the use of slotted grinding wheel. The developed machining process has been named as slotted electrical discharge abrasive grinding. The performances of slotted electrical discharge abrasive grinding process are tested on aluminum–silicon carbide–graphite (Al/SiC/Gr) metal matrix composite workpiece. The experiments were performed using one parameter at a time approach considering the effect of current, pulse on-time, pulse off-time, wheel speed and grit number on material removal rate and average surface roughness. It has been found that current ranges from 3 to 15 A and wheel speed ranges from 700 to 1300 r/min are more appropriate for machining of Al/SiC/Gr composite material within the ranges of selected parameters.

Intelligent modelling and prediction of slotted-electrical discharge diamond grinding of aluminium-silicon carbide-graphite composite

The aim of this paper is to develop an intelligent model for prediction of input-output relationship of slotted-electrical discharge diamond grinding (S-EDDG) process using back propagation neural network (BPNN) architecture. The experiments were conducted on aluminium-silicon carbide-graphite (Al/SiC/Gr) composite workpiece using a slotted abrasive metallic wheel. The pulse current, pulse on-time, pulse off-time, wheel speed and abrasive grit number are taken as input parameters while material removal rate (MRR) and average surface roughness (Ra) are taken as performance parameters. It has been found that the developed BPNN model is capable to predict the MRR and Ra with absolute average percentage errors as 9.46% and 7.94%, respectively. It has also been found that higher MRR and better surface finish can be achieved at pulse current value as 14 A, pulse off-time as 100 μs and wheel speed as 1,300 RPM.

Machining of a hybrid–metal matrix composite using an erosion–abrasion-based compound wheel in electrical discharge grinding

ABSTRACT Machining of the composites made of matrix and reinforcement is always difficult for manufacturing industries due to their unusual properties. Among various existing traditional and non-traditional machining processes, erosion-based machining process i.e., Electrical Discharge Grinding (EDG) and the abrasion-based process i.e., Diamond Grinding (DG) have been shown their potential to machine such difficult-to-machine materials. The aims of the present study are to analyze the performances of the erosion–abrasion-based compound wheel during machining of the hybrid–metal matrix composite made of Aluminum–Silicon Carbide–Boron Carbide (Al/SiC/B4C) by the stir casting method. The performances of the compound wheel have been tested on the EDM machine in the face grinding mode. The role of pulse current, pulse on-time, pulse off-time, wheel RPM, and abrasive grit number have been analyzed on the material removal rate (MRR) and average surface roughness (Ra). The experimental results showed that the machining with compound wheel gives higher MRR with better surface finish as compared to the uniform wheel. It has also been observed that MRR and Ra are highly affected by the pulse current, pulse on-time, and wheel RPM.

A new way of erosion-abrasion hybrid machining using slotted-diamond grinding wheel

Machining of advanced engineering materials like super alloys, heat treated alloys, titanium and carbide alloys, metal matrix composites and ceramics are become challenge for manufacturing industries, while these materials are highly required in field of modern manufacturing. In this paper, a new way of erosion-abrasion hybrid machining (EAHM) process using slotted wheel has been presented for machining of the hard and brittle electrically conductive materials, which is developed by comprising the mechanical grinding with electrical discharge grinding (EDG) in such a way that both the processes happen alternately. The performance of proposed EAHM process has been tested on aluminium silicon carbide (Al/SiCp) composite and also compared with electrical discharge machining (EDM) and EDG in term of material removal rate (MRR) and average surface roughness (Ra). It has been found that EAHM gives higher MRR than EDM and EDG processes with better surface finish, when these processes are individually tested on the same material.

Artificial neural network modelling of erosion-abrasion-based hybrid machining of aluminium-silicon carbide-boron carbide composite

The erosion-abrasion-based hybrid machining (EAHM) is newly developed machining process, which comprises the erosion-based machining such as electro-discharge grinding (EDG) and abrasion-based machining such as diamond grinding (DG) for machining of difficult to machine hard and brittle materials. The aim of this study is to develop an artificial neural network (ANN) model for EAHM process during machining of aluminium-silicon carbide-boron carbide (Al%SiC%B4C) composite workpiece. The ANN model has been trained and tested with experimental observations, which are collected after experimentations. The experiments were conducted on EDM machine considering the effect of pulse current, pulse on-time, pulse off-time, wheel RPM and abrasive grit number on the material removal rate and average surface roughness. It has been found that the developed ANN model was significantly predicted the responses within the acceptable limit. Such developed model is further used to study the effect of process parameters on the performance measures.

Modelling and simulation of spark assisted diamond face grinding of tungsten carbide-cobalt composite using ANN

The aim of this study is to develop an artificial neural network (ANN) model for spark assisted diamond face grinding (SADFG) of cobalt bonded tungsten carbide (WC-Co) composite to predict the material removal rate (MRR) and average surface roughness (Ra). The experiments were conducted on a self-developed face grinding setup, which is attached with EDM machine. A bronze metal bonded diamond wheel is used for experimentations. All the experiments were performed according to the central rotatable design. The current, pulse on-time, duty factor and wheel speed were taken as input process parameters and responses are measured in terms of MRR and Ra. Central rotatable design is used for experimentation. The obtained experimental data set was used to train the ANN model. The ANN architecture with back propagation algorithm has been used for modelling of process parameters of SADFG process. It has been found that the developed ANN model is capable to predict the MRR and Ra with absolute average percentage error of 10.40% and 6.81%, respectively. It has been also found that wheel speed at 1,300 RPM is suitable for achieving of the better surface finish while duty factor at 0.70 has been found more appropriate for higher MRR.