Multi Objective Optimization of Wire EDM Machining Parameters for AA7075-PAC Composite Using Grey - Fuzzy Technique

Ramanan, G.; Dhas, J. Edwin Raja

doi:10.1016/j.matpr.2017.11.519

Cited by 23 publications

(17 citation statements)

References 8 publications

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“…Results are utilized to develop the Gray-fuzzy model. From this technique, the optimum combinations of process parameters are obtained and corresponding values of maximum MRR and minimum SR are found out [134].…”

Section: Modeling and Optimization Of Surface Qualitymentioning

confidence: 99%

A review on optimization of machining performances and recent developments in electro discharge machining

Nahak

Gupta

2019

Manufacturing Rev.

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Electro discharge machining (EDM) is a popular unconventional machining process widely employed in die-making industries. Careful selection of process parameters such as pulse current, voltage, on and off time, etc. is essential for machining of hard and conductive materials using EDM. Previous researchers working in the area of EDM have extensively analyzed the machining performance through experimental study, modeling, and simulation and also by theoretical analysis. This article discusses the significant summary of the work performed by earlier researchers through a detailed literature survey. Relevant literature on EDM and impact of process parameters on performance measures such as surface quality, tool wear rate and material removal rate are reviewed. The challenge and limitation of EDM process are also highlighted in this article. It is observed that optimization of process parameters is essentially required for effective and economical machining. So, this article addresses the various issues related to EDM and also provides brief insight into some of the current generation applications of EDM process explored in various industries.

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Section: Modeling and Optimization Of Surface Qualitymentioning

confidence: 99%

A review on optimization of machining performances and recent developments in electro discharge machining

Nahak

Gupta

2019

Manufacturing Rev.

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“…CNC turning machine is used for performing the machining operation. Experimental design is created by Minitab software followed by statistical analysis [12]. Statistical studies of computer applications have some advantage like reliable, accurate and usually runs faster than other computing statistics and drawing graphs.…”

Section: Experimental Designmentioning

confidence: 99%

Optimization of Machining Characteristics of Hybrid Composites Using Grey Relational Technique

Ramanan.¹,

Rajan.R.R²,

S³

et al. 2018

IJET

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Metal matrix composite imparts several advantages over alloys. The MMCs exhibit improved properties compared with monolithic alloy. They are particularly suited for applications that require higher strength, dimensional stability and enhanced structural rigidity. Aluminium composite materials are engineered materials made from at least two or more constituent materials having different physical or chemical properties. In this work Seventeen turning experiments were conducted using response surface methodology. The machining parameters cutting speed, feed rate, and depth of cut are varied with respect to different machining conditions for each run. The optimal parameters were predicted by grey relational analysis technique. The optimum process parameter predicted from RSM techniques is cutting speed 250m/min, feed rate 0.06mm and depth of cut 1.5mm are found.

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“…Gangil and Pradhan (2018) investigated the optimal process parameters using the RSM technique couple with VIM. Ramanan et al (2018) have studied Aluminium 7075 MMC with different percentages of reinforcement. In total twenty-seven Kumar et al (2019) and Mardi et al (2017) have also examined the EDM of Aluminum-based MMC and observed the ease of machining for their useful applications.…”

Section: Introductionmentioning

confidence: 99%

Prediction and optimization of tool wear rate during electric discharge machining of Al/Cu/Ni alloy using adaptive neuro-fuzzy inference system

Phate¹,

Bendale²,

Toney³

et al. 2020

Heliyon

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Aluminum (Al)-copper (Cu)-nickel (Ni) alloy is a versatile material with lightweight and excellent strength. It also possesses properties such as superior corrosion resistance, fatigue strength. These alloys are essential in sectors viz. automobile, aerospace, defense, aerospace, etc. In this research work, the authors have presented the prediction and analysis of tool wear rate (TWR). The impact of electrical discharge machining (EDM) on process parameters viz. input current (IP), pulse on time (TON), pulse off time (TOFF)/for Al/Cu/Ni alloy with the composition 91/4/5 and 87/8/5 (weight %) is analyzed. Taguchi's L 18 (2 1 *3 3) mixed plan is employed to plan the experimentation. A mathematical model develops to correlate these process parameters. A soft computing technique known as an adaptive neuro-fuzzy inference system (ANFIS) utilizes to predict TWR. Taguchi analysis reveals that input current is the most influencing parameter followed by pulse on time. TWR decreases with a decrease in the amount of Aluminium. It increases in the amount of copper in the alloy. TWR firstly decreases with an increase in pulse on time and then starts to grow after the median value of 25 micro-sec. The confirmation experiments have conducted using optimum process parameters to validate the obtained results. The experimental finding shows the superior capability of ANFIS to predict the TWR with acceptable accuracy. The optimized TWR obtained was 0.1238 mm 3 /min based on the optimal settings of input parameters.

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Multi Objective Optimization of Wire EDM Machining Parameters for AA7075-PAC Composite Using Grey - Fuzzy Technique

Cited by 23 publications

References 8 publications

A review on optimization of machining performances and recent developments in electro discharge machining

A review on optimization of machining performances and recent developments in electro discharge machining

Optimization of Machining Characteristics of Hybrid Composites Using Grey Relational Technique

Prediction and optimization of tool wear rate during electric discharge machining of Al/Cu/Ni alloy using adaptive neuro-fuzzy inference system

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