Neural-network-based modeling and optimization of the electro-discharge machining process

Assarzadeh, S.; Ghoreishi, M.

doi:10.1007/s00170-007-1235-1

Cited by 92 publications

(40 citation statements)

References 14 publications

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“…Mohammadi and Aminollah also used the Taguchi's method analysis of variance (ANOVA) to study the relationship between WEDM input and output measures [15,16], and other scholars also used this method to investigate surface modification in EDM [17]. The artificial neural network was utilized to establish a mathematical relationship between the cutting parameters and output measures [18,19]. It can be seen from above that the MRR and Ra are the most widely characteristics that are used to describe WEDM cutting conditions [6][7][8][9][10][11][12][13][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…The artificial neural network was utilized to establish a mathematical relationship between the cutting parameters and output measures [18,19]. It can be seen from above that the MRR and Ra are the most widely characteristics that are used to describe WEDM cutting conditions [6][7][8][9][10][11][12][13][15][16][17][18]. In terms of workpiece material, tool steel YG15 was used because of its wide application in cutting tools and mold industry.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of cutting conditions of YG15 on rough and finish cutting in WEDM based on statistical analyses

Huang

Ming

Guo

et al. 2013

Int J Adv Manuf Technol

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In this paper, the effects and the optimization of cutting parameters on surface roughness (Ra) and material removal rate (MRR) in the wire electrical discharge machining (WEDM) of high hardness tool steel YG15 are analyzed. In the WEDM process, the key process parameters, such as pulse-on time, pulse-off time, power, cutting feed rate, wire tension, wire speed, and water pressure, are optimized. Experimental data were initially collected based on the Taguchi method of experimental design, which are L 18 2 1 × 3 5 and L 18 2 1 × 3 4 Taguchi standard orthogonal array on rough and finish cutting experiments, respectively. The level of importance of the cutting parameters on the Ra and MRR was determined on both finish and rough cutting by using statistical analyses; average gap voltage is discussed in order to balance cutting efficiency and stability on both finish and rough cutting. In addition, comparative analysis of finish and rough cutting is drawn to analyze the difference between rough cutting and finish cutting. Then, regression models and signal-to-noise ratio are used to obtain the optimum cutting parameter combination. Finally, the results present the optimized MRR and Ra of the rough and finish process, respectively, and confirm the efficiency and abilities of the model.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization of cutting conditions of YG15 on rough and finish cutting in WEDM based on statistical analyses

Huang

Ming

Guo

et al. 2013

Int J Adv Manuf Technol

View full text Add to dashboard Cite

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“…Having compared the results of the neural network model with estimates obtained via multiple regression analysis, they concluded that the neural model is more accurate and also less sensitive to noise included in the experimental data [8]. Assarzadeh and Ghoreishi have presented a new integrated neural network based approach for the prediction and optimal selection of process parameters in die sinking electro-discharge machining with a flat electrode [9]. Basheer et al have investigated the roughness of machined surfaces on Al/SiC metal metrix composites and developed an ANN-based model to predict surface roughness of machined surfaces using a feedforward network and an algorithm involving Bayesian regularization combined with the Levenberg-Marquardt modification to train the neural network [10].…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling of Hole Sinking Electrical Discharge Micro Machining of Ni-Based Super Alloy Thin Sheet

2016

Arimpie - 2016

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Abstract-From the productivity and accuracy point of view fabrication of the symmetrical and nonsymmetrical holes is a challenging task for manufacturing engineers. Creation of these kinds of holes in thin sheets of super alloys can only be possible by using hole sinking electro discharge micro machining (HS-EDMM) process. In the present paper Artificial Neural Network (ANN) model has been proposed for the prediction of material removal rate (MRR) in HS-EDMM. For this purpose Matlab with the neural networks toolbox (nntool) has been used. The neural network based on process model has also been developed to establish relationship between input process conditions (gap voltage and capacitance of the capacitor) and process response material removal rate. The ANN model has been trained and tested using the data generated from an extensive series of experiments on HS-EDMM machine. The trained neural network system has been used to predict MRR for different input conditions. The ANN model has been found to predict accurately HS-EDMM process responses for chosen process conditions. Keywords -Hole sinking electrical discharge micro machining (HS-EDMM), Artificial neural network (ANN), Back-propagation (BP) algorithm.

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“…In this paper, we are focusing on combining regression, ANN and multiGA to optimize multiple performances of EDM. Different from existing literatures which uses variable numerical experimentation [9] or random selection [10][11][12], we proposed orthogonal array as a technique to select the network parameters of ANN modeling in this study. Basically, the used of orthogonal array L256, is targeted to reduce the trial and error guesswork during the network parameters and network architecture selection.…”

Section: Introductionmentioning

confidence: 99%

Computational Approach for Multi Performances Optimization of EDM

2016

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Abstract. This paper proposes a new computational approach employed in obtaining optimal parameters of multi performances EDM. Regression and artificial neural network (ANN) are used as the modeling techniques meanwhile multi objective genetic algorithm (multiGA) is used as the optimization technique. Orthogonal array L256 is implemented in the procedure of network function and network architecture selection. Experimental studies are carried out to verify the machining performances suggested by this approach. The highest MRR value obtained from OrthoANN -MPR -MultiGA is 205.619 mg/min and the lowest Ra value is 0.0223μm.

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Neural-network-based modeling and optimization of the electro-discharge machining process

Cited by 92 publications

References 14 publications

Optimization of cutting conditions of YG15 on rough and finish cutting in WEDM based on statistical analyses

Optimization of cutting conditions of YG15 on rough and finish cutting in WEDM based on statistical analyses

Modelling of Hole Sinking Electrical Discharge Micro Machining of Ni-Based Super Alloy Thin Sheet

Computational Approach for Multi Performances Optimization of EDM

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