Comparison of optimization techniques for MRR and surface roughness in wire EDM process for gear cutting

Mohapatraa, K.; Satpathy, Mantra Prasad; Sahooa, S.

doi:10.5267/j.ijiec.2016.9.002

Cited by 9 publications

(5 citation statements)

References 16 publications

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“…The standard orthogonal array (OA) L12 (2 4 ) was selected for the current study after two levels and four factors of degrees of freedom (DOFs) were analyzed [15,16]. Table 3 illustrates how its twelve rows correlate with the number of experiments or assessments.…”

Section: Design Of Experimentsmentioning

confidence: 99%

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An Experimental Analysis of Taguchi-Based Gray Relational Analysis, Weighted Gray Relational Analysis, and Data Envelopment Analysis Ranking Method Multi-Criteria Decision-Making Approaches to Multiple-Quality Characteristic Optimization in the CNC Drilling Process

Abdullahu,

Zhujani,

Todorov

et al. 2024

Processes

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The goal of this research is to optimize the input parameters utilized in dry CNC drilling of forging steel to attain sustainable machining. Particular emphasis will be placed on achieving high productivity while minimizing the impact on surface quality. To achieve the aforementioned goal, three Taguchi-based multi-criteria decision-making (MCDM) approaches, such as traditional gray relational analysis (GRA), weighted gray relational analysis (WGRA), and data envelopment analysis ranking (DEAR), were used for simultaneous optimization of the MRR and Ra. In Taguchi’s L12 (24) orthogonal array design, the cutting mode parameters—such as cutting speed, depth of cut, feed rate, and point angle—have been chosen as the input parameters for the modeling and analysis of the drilling process characteristics. The process of determining the effect of the input parameters on the output parameters was carried out with the use of analysis of variance (ANOVA). The best results from the studies were Ra = 2.19 and MRR = 375 mm3/s, which corresponded to Taguchi’s single optimization levels, S2F1D1A2 and S2F2D2A1, respectively. In the next step, the performance values obtained for each MCDM technique were reoptimized using the Taguchi method, and the optimal levels were obtained: for traditional GRA, the level S2F1D2A1 (Ra = 2.52 µm, MRR = 125 mm3/s); for WGRA, the level S2F1D1A1 (Ra = 2.31 µm, MRR = 83 mm3/s); and for DEAR, the level S2F2D2A1 (Ra = 4.42 µm, MRR = 375 mm3/s), respectively. Lastly, in order to compare the experiments’ performance, validation tests were carried out. The results of the experiments using multi-objective optimization show that traditional GRA improved the overall quality response characteristics by 29.86% compared to the initial setup parameters, while weighted GRA improved them by 34.48%, with the DEAR method providing an improvement of 96%. Based on the findings of this investigation, the DEAR optimization method outperforms the GRA method. As a result, the proposed methods are useful tools for multi-objective optimization of cutting parameters.

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Section: Design Of Experimentsmentioning

confidence: 99%

“…Thus, in order to appropriately assign values to various responses under optimization, a fair criterion for the objective calculation of weight factors must be developed [16].…”

Section: Multi-criteria Cutting Parameter Optimization Analysismentioning

confidence: 99%

An Experimental Analysis of Taguchi-Based Gray Relational Analysis, Weighted Gray Relational Analysis, and Data Envelopment Analysis Ranking Method Multi-Criteria Decision-Making Approaches to Multiple-Quality Characteristic Optimization in the CNC Drilling Process

Abdullahu,

Zhujani,

Todorov

et al. 2024

Processes

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show abstract

“…Besides, many other methods have also been used as Artificial neural network, Response surface methodology, and Taguchi's combination with other methods (GRA, MOORA-PCA, VIKOR, multiple response signal-to-noise, weighted signal-to-noise,...) for multi-target optimization in EDM (Tirumala et al, 2018;Munmun & Kalipad, 2017;Bhaumik & Maity, 2017;Nayaka et al, 2017;Munmun & Kalipada, 2017;Dey & Chakraborty, 2015). However, Taguchi-TOPSIS and Taguchi-GRA are the most commonly used (Kumar et al, 2018;Zerti et al, 2018;Mohapatraa et al, 2017). Dastagiri et al (2016) have shown that TOPSIS-Taguchi is more effective than Taguchi-GRA in multi-response optimization of PMEDM.…”

Section: Introductionmentioning

confidence: 99%

Multi-response optimization of process parameters for powder mixed electro-discharge machining according to the surface roughness and surface micro-hardness using Taguchi-TOPSIS

Huu¹,

Banh²,

Bui³

et al. 2018

10.5267/j.ijdns

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In this study, the efficiency of integration between Taguchi and TOPSIS in multi-response optimization of powder mixed electrical discharge machining (PMEDM) process was evaluated. The input parameters, such as workpiece and tool electrode material, polarity, pulse on time (ton), pulse off time (toff), Current (I) and powder concentration have been selected to optimize two responses; namely surface roughness (Ra) and surface hardness (HV). The results show that titanium powder mixed dielectric fluid improves multi-response optimization efficiency in PMEDM. In addition, machining conditions, such as tool electrode material, powder concentration, pulse on time, polarity, current density, A×G and B×G interactions play a very important role on S/N ratio of C* whereby powder concentration has the strongest influence. TOPSIS-Taguchi is a potential method for multi-response optimization in PMEDM. However, the optimal results using ANOVA analysis show that there is a necessity to have more studies in TOPSIS-Taguchi to improve the integration efficiency between two methods for optimizing multiple responses in PMEDM.

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“…The results proved that the optimum combination was found through the use of different methods. Mohapatraa et al (2017) used two multi objective optimization methods for MRR and surface quality comparison of optimization techniques for MRR and surface roughness in the wire EDM process for gear cutting. He investigated the comparison of the wire EDM phenomenon using the Taguchi quality loss function and the hybrid optimization method.…”

Section: Introductionmentioning

confidence: 99%

Modeling and optimization of surface roughness and productivity thru RSM in face milling of AISI 1040 steel using coated carbide inserts

Fnides¹,

Yallese²,

Khattabi³

et al. 2017

10.5267/j.ijiec

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The aim of this study is to evaluate the impact of factors such as cutting speed, feed rate, and depth of cut on surface roughness and Material Removed Rate (MRR) when machining in dry face milling AISI 1040 steel with coated carbide inserts GC1030 using the response surface methodology (RSM). For this purpose, a number of machining experiments based on statistical three-factor and three-level factorial experiment designs, completed (L27) with a statistical analysis of variance (ANOVA), were performed in order to develop mathematical models and to identify the significant factors of these technological parameters. Multi-objective optimization procedure for minimizing Ra, Ry and Rz and maximizing MRR using desirability approach has been also implementented. The current study was also carried out to investigate the tool life of the inserts. The models found the relationship between the cutting parameters (Vc, fz and ap) and the studied technological parameters. It has been found that the cutting speed was the most affecting surface roughness which is due to the geometry of the insert which has a scraping edge and enables to obtain low roughness even at important feed rate, followed by the feed rate and the depth of cut at the end. The optimal combination of cutting parameters were cutting speed of 314 m/min, feed rate of 0.16 mm/tooth and depth of cut of 0.6 mm with a composite desirability of 0.924.

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Comparison of optimization techniques for MRR and surface roughness in wire EDM process for gear cutting

Cited by 9 publications

References 16 publications

An Experimental Analysis of Taguchi-Based Gray Relational Analysis, Weighted Gray Relational Analysis, and Data Envelopment Analysis Ranking Method Multi-Criteria Decision-Making Approaches to Multiple-Quality Characteristic Optimization in the CNC Drilling Process

An Experimental Analysis of Taguchi-Based Gray Relational Analysis, Weighted Gray Relational Analysis, and Data Envelopment Analysis Ranking Method Multi-Criteria Decision-Making Approaches to Multiple-Quality Characteristic Optimization in the CNC Drilling Process

Multi-response optimization of process parameters for powder mixed electro-discharge machining according to the surface roughness and surface micro-hardness using Taguchi-TOPSIS

Modeling and optimization of surface roughness and productivity thru RSM in face milling of AISI 1040 steel using coated carbide inserts

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