Experimental investigation and optimization of turning process of EN8 steel using Taguchi L9 orthogonal array

Gurugubelli, Sasidhar; Chekuri, Rama Bhadri Raju; Penmetsa, Ravi Varma

doi:10.1016/j.matpr.2022.01.474

Cited by 8 publications

(5 citation statements)

References 3 publications

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“…It occurs because the cutting tool will engage the material more smoothly and efficiently when the spindle operates at a higher speed. The smoother movement reduces scratches and fluctuations on the surface profile of the workpiece, resulting in a lower Ra value [19]. Furthermore, as illustrated in Fig.…”

Section: The Influence Of Machining Processes On Surface Roughnessmentioning

confidence: 92%

Optimization of Machining Parameters on the Surface Roughness of Aluminum in CNC Turning Process Using Taguchi Method

Putra,

Timuda,

Darsono

et al. 2023

Int. J. Innov. Mech. Eng. Adv. Mater

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In this research, Taguchi method is employed by focusing on spindle speed, feed rate, and depth of cut to optimize the CNC turning parameters for aluminum alloy 6063. The main goal of this study is to improve the surface roughness of the material. A L9 orthogonal array is used for experimentation, and the results are subsequently analyzed using ANOVA (Analysis of Variance). A spindle speed of 1300 rpm, a feed rate of 0.5 m/min, and a depth of cut of 1.5 mm are the optimal conditions to achieve the minimum average surface roughness (Ra). The main effect plot of the signal-to-noise (S/N) ratio provides significant evidence supporting the primary research goal. Furthermore, the ANOVA table reveals that spindle speed contributes 59.71%, feed rate contributes 29.80%, while depth of cut only contributes minimally at 0.72%. Based on the research findings, spindle speed and feed rate can be adjusted to control surface roughness. Both factors are highly significant in influencing the surface roughness of the material. The prediction equation from the linear regression analysis is Ra = 1.745 – 0.001024 spindle speed + 0.3000 feed rate – 0.0233 depth of cut. A coefficient of determination or R-squared value of 0.9115 indicates that the independent variables can explain 91.15% of the variation in the dependent variable. The experimental and predicted surface roughness (Ra) values have a predicted error percentage of 2.26%.

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Section: The Influence Of Machining Processes On Surface Roughnessmentioning

confidence: 92%

Optimization of Machining Parameters on the Surface Roughness of Aluminum in CNC Turning Process Using Taguchi Method

Putra,

Timuda,

Darsono

et al. 2023

Int. J. Innov. Mech. Eng. Adv. Mater

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“…In the RSM method with Central Composite Design (CCD), the number of experiments is 20 tests [20]. For the Taguchi method using an orthogonal array matrix, OA L9 is used, namely 9 tests, selecting the L9 orthogonal array matrix because it can improve experimental efficiency, focus on critical factors, and simpler analysis [21]. In this study, data will be obtained in the form of the amount of water discharge generated from experiments with predetermined variations.…”

Section: Data Collection Methodsmentioning

confidence: 99%

Comparative Analysis of Response Surface Methodology (Rsm) Method and Taguchi Method: Optimization Hydraulic Ram Pump Performance

Romelin,

Zahedi,

Nusantara

2024

Preprint

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Hydraulic ram pumps offer an energy-efficient solution for water lifting, crucial in rural areas with limited electricity access. Comparative analysis using Response Surface Methodology (RSM) and Taguchi method reveals distinct experimental designs and optimization outcomes. RSM entails 20 experiments, yielding optimal points at Input Height (3 m), Input Length (12 m), and Vacuum Tube Length (120 cm). In contrast, Taguchi employs 9 experiments, with optimal points at Input Height (3 m), Input Length (6 m), and Vacuum Tube Lenght (120 cm). For the equation model, the RSM method shows a complex mathematical equation involving interactions between variables, while the Taguchi method provides a simpler equation. As for the most optimal variable when viewed from the Significant value in both methods in the ANOVA table, it is found that the input height variable is the most significant variable in optimising the response (discharge). A better understanding of these two methods can help the selection of appropriate methods for specific situations, strengthen the understanding of hydram pump performance, and contribute to the development of more efficient and sustainable hydram pump technology.

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“…% quarry dust composite. Moreover, to optimize the Ra in the turning of EN8 steel, Gurugubelli et al . (2022) implemented a Taguchi L9 orthogonal array.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The second method used in this research is the Taguchi method, which allows the determination of the optimum solution based on the signal-to-noise ratio (S/N ratio) (Mia and Dhar, 2017). The optimal solution is achieved by calculating the S/N ratio, which is robust since it is less sensitive to variations in responses (Gurugubelli et al ., 2022). Subsequently, the effects of individual cutting parameters on the vibration and wear mechanism of the ceramic tool wear have been examined during the study of the MOO, utilizing the analysis of variance (ANOVA) method.…”

Section: Introductionmentioning

confidence: 99%

Investigation of the effect of vibration in the multi-objective optimization of dry turning of hardened steel

Ahmed

Juberi

Bari

et al. 2023

IJIEOM

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PurposeThis study aims to investigate the effect of vibration on ceramic tools under dry cutting conditions and find the optimum cutting condition for the hardened steel machining process in a computer numerical control (CNC) lathe machine.Design/methodology/approachIn this research, an integrated fuzzy TOPSIS-based Taguchi L9 optimization model has been applied for the multi-objective optimization (MOO) of the hard-turning responses. Additionally, the effect of vibration on the ceramic tool wear was investigated using Analysis of Variance (ANOVA) and Fast Fourier Transform (FFT).FindingsThe optimum cutting conditions for the multi-objective responses were obtained at 98 m/min cutting speed, 0.1 mm/rev feed rate and 0.2 mm depth of cut. According to the ANOVA of the input cutting parameters with respect to response variables, feed rate has the most significant impact (53.79%) on the control of response variables. From the vibration analysis, the feed rate, with a contribution of 34.74%, was shown to be the most significant process parameter influencing excessive vibration and consequent tool wear.Research limitations/implicationsThe MOO of response parameters at the optimum cutting parameter settings can significantly improve productivity in the dry turning of hardened steel and control over the input process parameters during machining.Originality/valueMost studies on optimizing responses in dry hard-turning performed in CNC lathe machines are based on single-objective optimization. Additionally, the effect of vibration on the ceramic tool during MOO of hard-turning has not been studied yet.

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Experimental investigation and optimization of turning process of EN8 steel using Taguchi L9 orthogonal array

Cited by 8 publications

References 3 publications

Optimization of Machining Parameters on the Surface Roughness of Aluminum in CNC Turning Process Using Taguchi Method

Optimization of Machining Parameters on the Surface Roughness of Aluminum in CNC Turning Process Using Taguchi Method

Comparative Analysis of Response Surface Methodology (Rsm) Method and Taguchi Method: Optimization Hydraulic Ram Pump Performance

Investigation of the effect of vibration in the multi-objective optimization of dry turning of hardened steel

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