Modeling and optimization of process parameters in face milling of Ti6Al4V alloy using Taguchi and grey relational analysis

Rahman, Al Mazedur; Rob, S M Abdur; Srivastava, A. K.

doi:10.1016/j.promfg.2021.06.023

Cited by 17 publications

(7 citation statements)

References 22 publications

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“…The following data analysis employs statistical methods. The Taguchi optimization method is applied to attain low surface roughness in various cutting operations, as evidenced by References [9,14,[31][32][33][34]. Furthermore, the signal-to-noise ratio, serving as a metric for assessing quality characteristics, is closely monitored.…”

Section: Utilizing the Taguchi Methods For Optimizing Cutting Parametersmentioning

confidence: 99%

“…In general, tool manufacturers recommend that alloys be produced at lower cutting speeds, so recommendations are often in the range of 30 to 90 m per minute (m/min). Reviewing the relevant literature reveals that researchers utilize various cutting speeds for milling titanium alloys, primarily falling within the range of 30 m/min to 200 m/min [7][8][9][10][11][12][13][14][15][16].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The colling lubricating technique shows physical and statistical insignificance for a confidence level of 95% and, therefore, for a confidence level of 99%. Extensive research has consistently demonstrated that the feed rate stands out as the most impactful factor, showing the most substantial influence on determining the arithmetic mean roughness (Ra) [7,11,14,15].…”

Section: Analysis Of Variance (Anova)mentioning

confidence: 99%

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Next-Gen Manufacturing: Machine Learning for Surface Roughness Prediction in Ti-6Al-4V Biocompatible Alloy Machining

Kosarac,

Tabakovic,

Mladjenovic

et al. 2023

JMMP

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Mechanical engineering plays an important role in the design and manufacture of medical devices, implants, prostheses, and other medical equipment, where the machining of bio-compatible materials have a special place. There are a lot of different conventional and non-conventional types of machining of biocompatible materials. One of the most frequently used methods is milling. The first part of this research explores the machining parameters optimization minimizing surface roughness in milling titanium alloy Ti-6Al-4V. A full factorial design involving four factors (cutting speed, feed rate, depth of cut, and the cooling/lubricating method), each having three levels, implies the 81 experimental runs. Using the Taguchi method, the number of experimental runs was reduced from 81 to 27 through an orthogonal design. According to the analysis of variance (ANOVA), the most significant parameter for surface roughness is feed rate. The second part explores the possibilities of using different ML techniques to create a predictive model for average surface roughness using the previously created small datasets. The paper presents a comparative analysis of several commonly used techniques for handling small datasets and regression problems. The best results indicate that the widely used machine learning algorithm Random Forest excels in handling regression problems and small datasets.

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Section: Utilizing the Taguchi Methods For Optimizing Cutting Parametersmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Analysis Of Variance (Anova)mentioning

confidence: 99%

See 1 more Smart Citation

Next-Gen Manufacturing: Machine Learning for Surface Roughness Prediction in Ti-6Al-4V Biocompatible Alloy Machining

Kosarac,

Tabakovic,

Mladjenovic

et al. 2023

JMMP

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“…Hence the higher cutting speed and lower axial depth of cut could produce the lower specific cutting energy. 9,25 The higher variation from the mean line shows the higher percentage of influence the parameter have on the corresponding performance characteristic. From grey-relational analysis, it can be noted that the axial depth of cut, and feed rate have more impact than the cutting speed, when all the performance characteristics are considered together.…”

Section: Identification Of Significant Process Parametersmentioning

confidence: 99%

Effects of machining parameters on enhancing Alpha-Beta Titanium alloy using Taguchi-grey relational analysis for aerospace applications

Pillai

Shunmugavel

Muthuramalingam

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part E:

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The need for enhancement of productivity and quality of the machined titanium alloy components is paramount to meet the high demands of aerospace parts. Various optimization techniques are being used widely to find suitable machining parameters to improve productivity and reduce surface defects. The present study aims to derive an optimal combination of process parameters during the end milling operation of Ti-6Al-4V alloy for enhancing performance measures which are surface roughness, specific cutting energy and material removal rate (MRR). The Grey relational optimization techniques along with Taguchi method has been utilized for finding the optimum machining parameters. It has been found that the axial depth of cut has the most significant factor on machining characteristics in end milling process of titanium alloy. Using Taguchi-Grey relational analysis technique, optimal process parameter combination has been computed and the performance enhancement using the optimal process combination has been verified by the confirmation test

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“…Experimental trials have been used to validate predicted values derived using GRA at an ideal setting, and they demonstrate a very close relationship. Rahman et al [27] analysed the interaction between input factors of face milling of Ti6Al4V alloy for selected responses. For multi-objective optimization, Taguchi-based GRA has revealed substantial improvements in all responses, such as tool life, SR, and cutting forces, which were improved by 55.81%, 6.12%, and 23.98%, respectively.…”

Section: Introductionmentioning

confidence: 99%

Effect of Near-Dry WEDM Process Variables through Taguchi-Based-GRA Approach on Performance Measures of Nitinol

Vora

Shah

Khanna³

et al. 2022

JMMP

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The machining of Nitinol shape memory alloys (SMA) through conventional machining techniques imposes several challenges due to the alloys’ comprehensive mechanical qualities. Wire electrical discharge machining (WEDM) process is a non-conventional machining technique that is suitable mainly for producing complex shape geometries with excellent surface features for difficult-to-cut materials. The current study attempted the use of a near-dry WEDM process for Nitinol SMA with the consideration of multiple response variables. The studied literature and machine capabilities have identified input factors of pulse-on-time (Ton), pulse-off-time (Toff), and current and output factors of MRR, SR, and RLT. Through the Taguchi approach, a total of nine experimental trials were designed to analyze the performance of the process. The statistical significance of input factors on the performance measures was studied with the help of ANOVA techniques. Statistical analysis for all the output measures has shown that the generated regression terms had a significant influence. For single output measures, the current was found to have a substantial effect on both MRR and SR, while Toff was the most significant contributor in the case of RLT. The obtained results of residual plots for all performance measures implied good ANOVA results. The effect of near-dry WEDM variables was studied on output measures through main effect plots. Grey relational analysis (GRA) has been employed to attain optimal parametric settings of multiple performance measures. GRA technique for the optimal parametric settings of simultaneous performance measures of MRR, SR, and RLT was found to have a Ton of 30 µs, Toff of 24 µs, and current of 4 A. Validation trials were conducted to check the adequacy of the GRA technique. The minor acceptable deviation was recorded among the anticipated and recorded values. This clearly reveals the acceptability of the integrated approach of the Taguchi–Grey method. The surface morphology for the near-dry and wet-WEDM has also been investigated through scanning electron microscopy (SEM). The author considers that the present study will be beneficial for users working in WEDM and near-dry WEDM processes for hard machining materials.

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Modeling and optimization of process parameters in face milling of Ti6Al4V alloy using Taguchi and grey relational analysis

Cited by 17 publications

References 22 publications

Next-Gen Manufacturing: Machine Learning for Surface Roughness Prediction in Ti-6Al-4V Biocompatible Alloy Machining

Next-Gen Manufacturing: Machine Learning for Surface Roughness Prediction in Ti-6Al-4V Biocompatible Alloy Machining

Effects of machining parameters on enhancing Alpha-Beta Titanium alloy using Taguchi-grey relational analysis for aerospace applications

Effect of Near-Dry WEDM Process Variables through Taguchi-Based-GRA Approach on Performance Measures of Nitinol

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