Application of Type-2 Fuzzy AHP-ARAS for Selecting Optimal WEDM Parameters

Sen, Binayak; Hussain, Syed Abou Iltaf; Gupta, Archisman Das; Gupta, Munish Kumar; Pimenov, Danil Yurievich; Mikołajczyk, Tadeusz

doi:10.3390/met11010042

Cited by 42 publications

(18 citation statements)

References 45 publications

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“…Considering these factors, an attempt is made in this work to investigate the effects of wire-EDM process parameters such as current, pulse-on and pulse-off time, wire speed and voltage on MRR and optimize these settings to improve the productivity in terms of MRR. Although, artificial neural networkbased optimization methods such as fuzzy AHP-ARAS [34], genetic algorithm [35], particle swarm, moth-flame and Grass-Hooper optimization [36] methods are more attractive tools of optimization for the experiments with large data sets, for limited experimental trails as in this work, Taguchi and response surface [37] methods are more viable methods. Therefore, this works adopts Taguchi method for optimization of MRR and to develop the regression model to predict the MRR.…”

Section: Introductionmentioning

confidence: 97%

Optimization and Modeling of Material Removal Rate in Wire-EDM of Silicon Particle Reinforced Al6061 Composite

et al. 2021

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The mechanical, physical and interfacial properties of aluminum alloys are improved by reinforcing the silicon carbide particles (SiCp). Machinability of such alloys by traditional methods is challenging due to higher tool wear and surface roughness. The objective of research is to investigate the machinability of SiCp reinforced Al6061 composite by Wire-Electrical Discharge Machining (wire-EDM). The effect of wire-EDM parameters namely current (I), pulse-on time (Ton), wire-speed (Ws), voltage (Iv) and pulse-off time (Toff) on material removal rate (MRR) is investigated and their settings are optimized for achieving the high MRR. The experiments are designed by using Taguchi L16 orthogonal arrays. The MRR obtained at different experiments are analyzed using statistical tools. It is observed that all the chosen process parameters showed significant influence of on the MRR with contribution of 27.39%, 22.08%, 21.32%, 15.76% and 12.94% by I, Iv, Toff, Ton and Ws, respectively. At optimum settings, the Wire-EDM resulted in MRR of 65.21 mg/min and 62.41 mg/min for samples with 4% and 8% SiCp. The results also indicated reinforcing SiCp upto 8% showed marginally low influence on MRR. Microstructural investigation of the cut surface revealed the presence of craters with wave pattern on its surface. The top surface of the crater is featured by the recast layers connecting adjacent craters. Further, the statistical model is developed using linear regression to predict the MRR (?2—73.65%) and its predicting accuracy is verified by the confirmation trials. The statistical model is useful for predicting the MRR for different settings of the process parameters. The optimized settings can be used to improve the machining productivity by increasing the MRR while machining of Al6061-SiCp (upto 8 wt. %) alloy by wire-EDM industries.

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Section: Introductionmentioning

confidence: 97%

Optimization and Modeling of Material Removal Rate in Wire-EDM of Silicon Particle Reinforced Al6061 Composite

et al. 2021

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“…Electrical discharge machining (EDM) is continuously expanding its presence in the industry due to the ability to perform high-precision processing of conductive materials regardless of their mechanical properties. The continuously increasing role of EDM makes research aimed at increasing the stability and productivity of the process and at creating the opportunities for the transition to "smart" EDM [1][2][3][4][5][6][7][8][9][10][11][12][13] operating in a fully automatic mode very interesting and important. In this work, EDM using wire-cut machines with an automatically adjustable interelectrode gap (IEG) was studied.…”

Section: Introductionmentioning

confidence: 99%

Physicomechanical Nature of Acoustic Emission Preceding Wire Breakage during Wire Electrical Discharge Machining (WEDM) of Advanced Cutting Tool Materials

Grigoriev

Pivkin

Kozochkin

et al. 2021

Metals

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The field of applied wire electrical discharge machining (WEDM) is rapidly expanding due to rapidly increasing demand for parts made of hard-to-machine materials. Hard alloys composed of WC, TiC and Co are advanced cutting materials widely used in industry due to the excellent combination of hardness and toughness, providing them obvious advantages over other cutting materials, such as cubic boron nitride, ceramics, diamond or high-speed steel. A rational choice of the WEDM modes is extremely important to ensure the dimensional quality of the manufactured cutting inserts, while roughness of the machined surface on the cutting edge is of great importance with regards to the application of wear-resistant coatings, which increases tool life. However, the stock control systems of CNC WEDM machines, which are based on assessment of electrical parameters such as amperage and voltage, are unable to timely detect conditions at which a threat of wire breakage appears and to prevent wire breakage by stopping the electrode feed and flushing out the interelectrode gap (IEG) when hard alloys with high heat resistance and low heat conductivity, such as WC, TiC and Co composites, are being machined, due to the inability to distinguish the working pulses and pulses that expend a part of their energy heating and removing electroerosion products contaminating the working zone. In this paper, the physicomechanical nature of the WEDM of hard alloy WC 88% + TiC 6% + Co 6% was investigated, and the possibility of using acoustic emission parameters for controlling WEDM stability and productivity were explored. Acoustic emission (AE) signals were recorded in octave bands with central frequencies of 1–3 and 10–20 kHz. It was found that at the initial moment, when the dielectric fluid is virtually free of contaminants, the amplitude of the high-frequency component of the VA signal has its highest value. However, as the contamination of the working zone by electroerosion products increases, the amplitude of the high-frequency component of the AE signal decreases while the low-frequency component increases in an octave of 1–3 kHz. By the time of the wire breakage, the amplitude of the high-frequency component in the octave of 10–20 kHz had reduced by more than 5-fold, the amplitude of the low-frequency component in the octave of 1–3 kHz had increased by more than 2-fold, and their ratio, coefficient Kf, decreased by 12-fold. To evaluate the efficiency of Kf as a diagnostic parameter, the quality of the surface being machined was investigated. The analysis of residual irregularities on the surface at the electrode breakage point showed the presence of deep cracks and craters typical of short-circuit machining. It was also found that the workpiece surface was full of deposits/sticks, whose chemical composition was identical to that of the wire material. The presence of the deposits evidenced heating and melting of the wire due to the increased concentration of contaminants causing short circuits. It was also shown that the wire breakage was accompanied by the “neck” formation, which indicated simultaneous impacts of the local heating of the wire material and tensile forces. Due to the elevated temperature, the mechanical properties the wire material are quickly declining, a “neck” is being formed, and, finally, the wire breaks. At the wire breakage point, sticks/deposits of the workpiece material and electroerosion products were clearly visible, which evidenced a partial loss of the pulses’ energy on heating the electroerosion products and electrodes. A further increase in the contamination level led to short circuits and subsequent breakage of the wire electrode. It was shown that in contrast to the conventional controlling scheme, which is based on the assessment of amperage and voltage only, the analysis of VA signals clearly indicates the risk of wire breakage due to contamination of the working zone, discharge localization and subsequent short circuits. The monotonic dependence of WEDM productivity on AE parameters provides the possibility of adaptive adjustment of the wire electrode feed rate to the highest WEDM productivity at a given contamination level. As the concentration of contaminants increases, the feed rate of the wire electrode should decrease until the critical value of the diagnostic parameter Kf, at which the feed stops and the IEG flushes out, is reached. The link between the AE signals and physicomechanical nature of the WEDM of advanced cutting materials with high heat resistance and low heat conductivity in different cutting modes clearly shows that the monitoring of AE signals can be used as a main or supplementary component of control systems for CNC WEDM machines.

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“…ANFIS combines fuzzy logic and neural networks organically and makes a fuzzy system more systematic and less dependent upon expert knowledge. Also, an interval type-2 fuzzy-integrated AHP-ARAS method is designed to select the best WEDM parameter settings as well compute the weightage of the criteria by applying the ARAS ranking method and AHP method, respectively [ 27 ]. Suganthi et al [ 28 ] carried out the comparative experiments about ANN model and ANFIS model and revealed the fact that ANFIS outperformed to ANN in terms of modeling and prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Spark Analysis Based on the CNN-GRU Model for WEDM Process

et al. 2021

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Wire electrical discharge machining (WEDM), widely used to fabricate micro and precision parts in manufacturing industry, is a nontraditional machining method using discharge energy which is transformed into thermal energy to efficiently remove materials. A great amount of research has been conducted based on pulse characteristics. However, the spark image-based approach has little research reported. This paper proposes a discharge spark image-based approach. A model is introduced to predict the discharge status using spark image features through a synchronous high-speed image and waveform acquisition system. First, the relationship between the spark image features (e.g., area, energy, energy density, distribution, etc.) and discharge status is explored by a set of experiments). Traditional methods have claimed that pulse waveform of “short” status is related to the status of non-machining while through our research, it is concluded that this is not always true by conducting experiments based on the spark images. Second, a deep learning model based on Convolution neural network (CNN) and Gated recurrent unit (GRU) is proposed to predict the discharge status. A time series of spark image features extracted by CNN form a 3D feature space is used to predict the discharge status through GRU. Moreover, a quantitative labeling method of machining state is proposed to improve the stability of the model. Due the effective features and the quantitative labeling method, the proposed approach achieves better predict result comparing with the single GRU model.

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Application of Type-2 Fuzzy AHP-ARAS for Selecting Optimal WEDM Parameters

Cited by 42 publications

References 45 publications

Optimization and Modeling of Material Removal Rate in Wire-EDM of Silicon Particle Reinforced Al6061 Composite

Optimization and Modeling of Material Removal Rate in Wire-EDM of Silicon Particle Reinforced Al6061 Composite

Physicomechanical Nature of Acoustic Emission Preceding Wire Breakage during Wire Electrical Discharge Machining (WEDM) of Advanced Cutting Tool Materials

Spark Analysis Based on the CNN-GRU Model for WEDM Process

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