Statistical analysis of wire electrical discharge turning on material removal rate

Mohammadi, Ali; Tehrani, Alireza Fadaei; Emanian, Ehsan; Karimi, Davoud

doi:10.1016/j.jmatprotec.2007.11.177

Cited by 70 publications

(35 citation statements)

References 10 publications

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“…The quality of the workpiece hinge essentially on the condition of the lathe. It is difficult to exploit the optimum functions of a machine owing to numerous adjustable machining parameters [8]. The Taguchi's dynamic experiments are efficient and systematic method to find out the optimum settings of machining parameters [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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Statistical and Regression Analysis of Vibration of Carbon Steel Cutting Tool for Turning of EN24 Steel Using Design of Experiments

Aherwar¹,

Unune²,

Pathri³

et al. 2014

IJMECH

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Section: Introductionmentioning

confidence: 99%

“…In experimental studies, ANOVA is often employed prior to other statistical analysis. Afterwards that, regression analysis is widely applied to show a relation between independent variables and dependent variables [8].…”

Section: Introductionmentioning

confidence: 99%

Statistical and Regression Analysis of Vibration of Carbon Steel Cutting Tool for Turning of EN24 Steel Using Design of Experiments

Aherwar¹,

Unune²,

Pathri³

et al. 2014

IJMECH

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show abstract

“…In order to predict the performance of MRR and Ra, Ramakrishnan and Karunamoorthy [12] and Kung and Chiang [13] developed a mathematical model using the response surface methodology. The Taguchi's robust design approach was proposed for the WEDM operation in order to optimize the MRR and Ra [14,15]. 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].…”

Section: Introductionmentioning

confidence: 99%

“…The Taguchi's robust design approach was proposed for the WEDM operation in order to optimize the MRR and Ra [14,15]. 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].…”

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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Prediction of Controllable Process Variables for Various Workpiece Materials in CNC-WEDM

Kachhap

Singh

2018

Innovation in Materials Science and Engineering

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Statistical analysis of wire electrical discharge turning on material removal rate

Cited by 70 publications

References 10 publications

Statistical and Regression Analysis of Vibration of Carbon Steel Cutting Tool for Turning of EN24 Steel Using Design of Experiments

Statistical and Regression Analysis of Vibration of Carbon Steel Cutting Tool for Turning of EN24 Steel Using Design of Experiments

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

Prediction of Controllable Process Variables for Various Workpiece Materials in CNC-WEDM

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