Optimization of the electrical discharge machining process based on the Taguchi method with fuzzy logics

Lin, Jyh Dong; Wang, K.S.; Yan, Biing Hwa; Tarng, Y.S.

doi:10.1016/s0924-0136(00)00438-6

Cited by 235 publications

(124 citation statements)

References 6 publications

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“…In this study, to simplify the calculation process and statistics conveniently, machining time substitutes for machining efficiency Experimental design based on Taguchi method. Taguchi method is a powerful tool in the design of experiment methods, using the Taguchi method would markedly reduce the number of experiments, [5,6] . In this study, the experimental design was according to an L 25 (4 5 ) orthogonal array based on the Taguchi method.…”

Section: Design Factors Selectedmentioning

confidence: 99%

The effects of machining parameters on machining time in piezoelectric self-adaptive micro-EDM

Gao

Zhang

2012

Proceedings of the 2nd International Conference on Electronic and Mechanical Engineering and Information Technology (2012)

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Abstract.A new piezoelectric self-adaptive micro electrical discharge machining system, based on inverse piezoelectric effect, was developed in this paper to improve stability of micro-EDM process The Working mechanism and working process of this new technology were illustrated. In this new technology, the piezoelectric actuator was used as the micro driven system to control the discharge gap distance depending on the discharge state. This paper presents an investigation on the effects of machining parameters on machining efficiency in piezoelectric self-adaptive discharge machining. The effects of machining parameters such as open voltage, capacitance, resistance, spindle rotational speed and initial feed speed have been analyzed on machining efficiency by using signal to noise analysis method based on orthogonal experiment. IntroductionMicro-EDM is a precision machining process for machining micro-structures; its material removal mechanism is based on melting and vaporizing material with the heat and pressure generated by electrical discharge, so it can machine any type of conductive material, regardless of its hardness [1][2][3] . The basic characteristic of micro-EDM process is similar to that of normal EDM process [4] , but higher requirements were set on the precision and sensitivity of electrode drive system for micro-EDM system because of the reduction of discharge gap and discharge energy. In this paper, in order to improve the process stability and achieve better micro-EDM performance, a new piezoelectric self-adaptive micro-EDM technology, based on inverse piezoelectric effect, was presented in this paper.The working mechanism and micro-EDM device of this new technology is different from the normal micro-EDM, it has the special characteristics. The work presented in this paper aims to study the influence of machining parameters on machining time of this new piezoelectric self-adaptive micro-EDM.

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Section: Design Factors Selectedmentioning

confidence: 99%

The effects of machining parameters on machining time in piezoelectric self-adaptive micro-EDM

Gao

Zhang

2012

Proceedings of the 2nd International Conference on Electronic and Mechanical Engineering and Information Technology (2012)

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“…Furthermore, they extended their studies and developed a hybrid model for SR is to predict the behavior of the MDN 250 steel [2]. In EDM, for optimum machining performance measures, it is an important task to select proper combination of machining parameters [3].…”

Section: Introductionmentioning

confidence: 99%

Multi response optimization of process parameters using Response Surface Methodology in EDM of Inconel 825

Kumar¹,

ParameswaraRao²,

chary³

et al. 2017

IJMTER

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-Now days, mass production of complicated shapes of different components with high accuracy is very difficult and takes much time by conventional machining process. To get-rid of above said difficulties, Electrical Discharge Machine (EDM) is the one nonconventional machining process which is used very widely. In EDM, it is necessary to optimize the process parameters like pulse ontime, pulse off-time, discharge current, voltage, for maximization of Material Removal Rate (MRR) for INCONEL 825. Basically (EDM) is a well-established nonconventional machining process, used for manufacturing geometrically complex or hard and electrically conductive material parts that are extremely difficult-to-cut by other conventional machining processes. Erosion pulse discharge occurs in a small gap between the work piece and the electrode. This removes the unwanted material from the parent metal through melting and vaporizing in presence of dielectric fluid. Presence of metal particles in dielectric fluid diverts its properties, which reduces the insulating strength of the dielectric fluid and increases the spark gap between the tool and work piece. As a result, the process becomes more stable and surface finish increases. The EDM process is mainly used for making of dies, moulds, parts of aerospace, automotive industry and surgical components etc. For this purpose Response Surface methodology is adopted to determine the process parameter that optimizes the best machining process.

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“…Rao et al [9] developed mathematical model for predicting die-sinking electrical discharge machining of aluminium alloy characteristics such as the metal removal rate, the tool wear rate, the surface roughness (R a value) and the hardness using fuzzy mathematical method. Lin et al [10] presented the use of fuzzy logic to the Taguchi method for the modeling and optimization of the EDM process with the multiple performance characteristics. Shabgard et al developed a fuzzy-based algorithm for prediction of material removal rate, tool wear ratio, and surface roughness in the EDM and ultrasonic-assisted EDM processes.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Fuzzy Logic and Neural Network for Modelling Surface Roughness in EDM

Rodić¹,

Gostimirović²,

Kováč³

et al. 2014

IJMECH

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Surface roughness is the main indicator of technological performances of a component for electrical discharge machining (EDM). EDM process of manganese alloyed cold-work tool steel was modelled. In this paper we used the fuzzy logic (FL) and neural network (NN) to predict the effect of machining variables (discharge current and pulse duration) on the surface roughness of manganese alloyed cold-work tool steel in order to improve and increase its range of application. The experiments are carried out on manganese alloyed cold-work tool steel, processed with electrodes made of copper. The values of surface

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Optimization of the electrical discharge machining process based on the Taguchi method with fuzzy logics

Cited by 235 publications

References 6 publications

The effects of machining parameters on machining time in piezoelectric self-adaptive micro-EDM

The effects of machining parameters on machining time in piezoelectric self-adaptive micro-EDM

Multi response optimization of process parameters using Response Surface Methodology in EDM of Inconel 825

Comparison of Fuzzy Logic and Neural Network for Modelling Surface Roughness in EDM

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