Intelligent approach for process modelling and optimization on electrical discharge machining of polycrystalline diamond

Ong, Pauline; Chong, Chon Haow; Rahim, Md. Mahbubur; Lee, Woon Kiow; Kiong, Sia Chee; Ahmad, Muhammad Ariff Haikal bin

doi:10.1007/s10845-018-1443-6

Cited by 39 publications

(15 citation statements)

References 59 publications

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“…Furthermore, the normality assumptions are satisfied, if projection of residuals plot projects along a straight line. 38 The residuals for MRR and SG (refer Figure 4) lie apparently close to a straight line indicating that errors are distributed normally, and predicted values bear good agreement with experimental results.…”

Section: Development Of Mathematical Relationssupporting

confidence: 70%

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Investigating the influence of WEDM process parameters in machining of hybrid aluminum composites

Kumar

Grover

Manna

et al. 2020

Advanced Composites Letters

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This article presents an experimental investigation to assess the influence of input process parameters of machinability of wire electrical discharge machining (WEDM) process for machining of triple-reinforced silicon carbide, graphite, and iron oxide hybrid aluminum (Al-6061) metal matrix composites. The composite work specimens, developed using stir casting process, have been processed through WEDM process by adopting a statistically controlled design of experimentation approach. Furthermore, analysis of variance and regression analysis have been performed to understand the influence of the input process parameters on material removal rate (MRR) and spark gap (SG) width. The statistical analysis highlighted the improvements in MRR and SG by 33.72% and −27.28%, respectively, upon adopting the suggested optimized range of input process parameters. Further, the morphology of the machined composite surfaces has also been studied using scanning electron microscopy and energy dispersive spectroscopy to report the phenomenon of formation of recast layer.

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Section: Development Of Mathematical Relationssupporting

confidence: 70%

“…The setting of current and pulse duration affects the spark intensity and hence the MRR. 38 Figure 2 illustrates that the MRR escalates as pulse peak current amplifies from 80 A to 100 A. However, a drop in MRR has been witnessed when pulse peak current amplifies from 100 A to 120 A.…”

Section: Resultsmentioning

confidence: 98%

Investigating the influence of WEDM process parameters in machining of hybrid aluminum composites

Kumar

Grover

Manna

et al. 2020

Advanced Composites Letters

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“…In addition, Somashekhar et al [ 30 ] combined ANN and genetic algorithm (GA) in optimizing the MRR in micro-EDM that the back-propagation network data along with the GA can successfully synthesize optimum input condition to maximize the MRR. Ong et al [ 31 ] developed a small mean-squared error (MSE) model of radial basis function neural network to predict the MRR and EWR of the EDM process. Ming et al [ 32 ] conducted cutting parameter optimization in the WEDM process by integrating ANN, and wolf pack algorithm based on the strategy of the leader (LWPA).…”

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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“…Under such circumstances, the effective utilization of experimental, modeling, and optimization methodology make possible a more considerable improvement in decision-making with a new technological solution that can simultaneously satisfy and control the several distinctive as well as contradictory objectives (multi-objective) in order to make the EDM process an excellent choice for machining of advanced metal matrix composite materials. Several statistical and computational approaches such as RSM [16][17][18][19][20][21], ANN [22][23][24][25][26][27] have been applied for predictive modeling and Taguchi method [28][29][30][31], GRA [32][33][34][35][36], desirability function approach of RSM [37][38][39][40], and PCA [41,42] have been employed for parametric as well as process optimization in electrical discharge machining. Extensive studies have been reported by employing various experimental designs, modeling techniques and optimization approaches in order to assess or investigate the machinability [43][44][45][46], to predict the various technological responses, and to control the process parameters in machining of different workpiece materials (AISI D2, D3, D6, MDN 300, AISI 316L, stainless steel, A 2 tool steel, grey cast iron, Inconel 600, 601, 625, 825, 718, Ti6Al4V, Ti13Zr13Nb, nickel alloy, Al7075, Al6061, Al6063 alloy, Al-SiC MMC, Si 3 N 4 -TiN MMC, Al-Mg 2 Si, WC, polycrystalline diamond).…”

Section: Introductionmentioning

confidence: 99%

Analysis, predictive modelling and multi-response optimization in electrical discharge machining of Al-22%SiC metal matrix composite for minimization of surface roughness and hole overcut

2020

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Due to the widespread engineering applications of metal matrix composites especially in automotive, aerospace, military, and electricity industries; the achievement of desired shape and contour of the machined end product with intricate geometry and dimensions that are very challenging task. This experimental investigation deals with electrical discharge machining of newly engineered metal matrix composite of aluminum reinforced with 22 wt.% of silicon carbide particles (Al-22%SiC MMC) using a brass electrode to analyze the machined part quality concerning surface roughness and overcut. Forty-six sets of experimental trials are conducted by considering five machining parameters (discharge current, gap voltage, pulse-on-time, pulse-off-time and flushing pressure) based on Box-Behnken's design of experiments (BBDOEs). This article demonstrates the methodology for predictive modeling and multi-response optimization of machining accuracy and surface quality to enhance the hole quality in Al-SiC based MMC, employing response surface methodology (RSM) and desirability function approach (DFA). Finally, a novel approach has been proposed for economic analysis which estimated the total machining cost per part of rupees 211.08 during EDM of Al-SiC MMC under optimum machining conditions. Thereafter, under the influence of discharge current several observations are performed on machined surface morphology and hole characteristics by scanning electron microscope to establish the process. The result shows that discharge current has the significant contribution (38.16% for Ra, 37.12% in case of OC) in degradation of surface finish as well as the dimensional deviation of hole diameter, especially overcut. The machining data generated for the Al-SiC MMC will be useful for the industry.

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Intelligent approach for process modelling and optimization on electrical discharge machining of polycrystalline diamond

Cited by 39 publications

References 59 publications

Investigating the influence of WEDM process parameters in machining of hybrid aluminum composites

Investigating the influence of WEDM process parameters in machining of hybrid aluminum composites

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

Analysis, predictive modelling and multi-response optimization in electrical discharge machining of Al-22%SiC metal matrix composite for minimization of surface roughness and hole overcut

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