Optimization of process parameters of small hole dry drilling in Ti–6Al–4V using Taguchi and grey relational analysis

Prasanna, J.; Karunamoorthy, L.; Raman, M. Venkat; Prashanth, S.; Chordia, D. Raj

doi:10.1016/j.measurement.2013.11.020

Cited by 126 publications

(46 citation statements)

References 24 publications

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“…The coefficient of determination (R 2 ) of the equations obtained with the linear regression model for T, F c , and k s were calculated as 89.93%, 88.07%, and 87.04%, respectively, for AISI 304 and 89.93%, 88.59%, and 87.04%, respectively, for AISI 2205. R 2 is the statistical measure of how well the regression line approximates the real data points and its value should be between 0.8 and 1 [44]. Figure 5 shows normal probability plots of the residuals and this reveals that almost all the residuals follow a straight-line pattern which confirms that the regression model obtained from T, F c , and k s for drilling the two selected stainless steels matches very well with the experimental data (R 2 > 0.8).…”

Section: Empirical Models and Prediction Performancementioning

confidence: 99%

Prediction and Optimization of Drilling Parameters in Drilling of AISI 304 and AISI 2205 Steels with PVD Monolayer and Multilayer Coated Drills

Youssef

El-Hofy

Ahmed

2018

JMMP

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Due to their high ductility, high durability, and excellent corrosion resistance, stainless steels are attractive materials for a variety of applications. However, high work hardening, low thermal conductivity, and high built-up edge (BUE) formation make these materials difficult to machine. Rapid tool wear and high cutting forces are the common problems encountered while machining these materials. In the present work, the application of Taguchi optimization methodology has been used to optimize the cutting parameters of the drilling process for machining two stainless steels: austenitic AISI 304 and duplex AISI 2205 under dry conditions. The machining parameters which were chosen to be evaluated in this study are the tool material, cutting speed, and feed rate, while, the response factors to be measured are the tool life (T), cutting force (F c ), and specific cutting energy (k s ). Additionally, empirical models were created for predicting the T, F c and k s using linear regression analysis. The results of this study show that AISI 2205 stainless steel has a shorter tool life, a higher cutting force, and a higher specific cutting energy than AISI 304 stainless steel. In addition, the Taguchi method determined that A 3 B 1 C 1 and A 3 B 3 C 1 (A 3 = TiN-coated twist drill, B 1 = 13 m/min, B 3 = 34 m/min, C 1 = 0.12 mm/rev) are the optimized combination of levels for the best tool life and the lowest cutting force, respectively. Meanwhile, the optimized combination of levels for all three control factors from the analysis, which provides the lowest specific cutting energy, was found to be A 3 B 1 C 3 (A 3 = TiN-coated twist drill, B 1 = 13 m/min, C 3 = 0.32 mm/rev) for both stainless steels.

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Section: Empirical Models and Prediction Performancementioning

confidence: 99%

Prediction and Optimization of Drilling Parameters in Drilling of AISI 304 and AISI 2205 Steels with PVD Monolayer and Multilayer Coated Drills

Youssef

El-Hofy

Ahmed

2018

JMMP

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“…prasanna et.al. [34] investigated, through holes were machined in a Ti-6Al-4V plate of 0.4 mm thickness using twisted carbide drill bits of 0.4 mm diameter by conventional dry drilling. The Taguchi's experimental design and Analysis of Variance (ANOVA) techniques have been implemented to understand the effects, contribution, significance and optimal machine settings of process parameters, namely, spindle speed, feed rate and air pressure.…”

Section: Elk Asia Pacific Journals -Special Issue Isbn: 978-81-930411mentioning

confidence: 99%

Modelling of Electro-Discharge Machining of Difficult-to-Machine Materials: An Overview

2015

International Conference on Advancements and Recent Innovations in Mechanical, Production and Industrial Engineering

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Abstract-Electro discharge machining (EDM) is anunconventional electro thermal machining process used for manufacturing geometrically complex or hard material parts that are extremely difficult-tomachine by conventional machining process. Modelling of the EDM process provides a functional relationship between operating parameters and output parameters (process performance index) of EDM process. This is the scientific way to study quantitatively EDM process behaviour which helps to get the better understanding of the process. This paper reviews various research work carried out within past decades for the development of EDM process. The study is mainly focused on aspects related to the experimental modelling of EDM process using Artificial Neural Network (ANN) and Response Surface Methodology (RSM).

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“…Hwang et al [10] investigated and optimized the high speed end milling of SKD61 Tool Steel using Taguchi methods with grey relational analysis. Recently, this method attracts more and more widely applied on mechanical machining and tribological engineering, including drilling [12][13][14], turning [15][16], milling [17][18], wire electrical discharge machining [19][20], and tribology [21].…”

Section: Introductionmentioning

confidence: 99%

Multi-response Optimization of Surface Roughness Roundness and MRR in Precision Turn-Boring of 15-5PH Stainless Steel Using Taguchi-Grey Approach

Hwang¹,

Tsai²

2017

Preprint

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Abstract:The present study propose an innovative turn-boring operation method and focuses on finding optimal turn-boring process parameters for 15-5PH Stainless steel by considering multiple performance characteristics using Taguchi orthogonal array with the grey relational analysis, the effect of machining variables such as concentration of cutting fluid , temperature of cutting fluid , feed rate, depth of cut and cutting speed are optimized with considerations of multiple performance characteristics namely surface roughness, roundness error and material removal rate, the optimal values were found out from the Grey relational grade. The result of the Analysis of Variances (ANOVA) is shown that the most significant factor is cutting speed, followed by feed rate, concentration of cutting fluid, radial depth of cut and temperature of cutting fluid. Finally, confirmation tests were carried out to make a comparison between the experimental results and developed model.Experimental results have shown that machining performance in the turn-boring process can be improved effectively through this approach.

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Optimization of process parameters of small hole dry drilling in Ti–6Al–4V using Taguchi and grey relational analysis

Cited by 126 publications

References 24 publications

Prediction and Optimization of Drilling Parameters in Drilling of AISI 304 and AISI 2205 Steels with PVD Monolayer and Multilayer Coated Drills

Prediction and Optimization of Drilling Parameters in Drilling of AISI 304 and AISI 2205 Steels with PVD Monolayer and Multilayer Coated Drills

Modelling of Electro-Discharge Machining of Difficult-to-Machine Materials: An Overview

Multi-response Optimization of Surface Roughness Roundness and MRR in Precision Turn-Boring of 15-5PH Stainless Steel Using Taguchi-Grey Approach

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