Analysis of process parameters in the micromachining of Ti-6Al-4V alloy

Jaffery, Syed Imran Husain; Driver, Nicholas; Mativenga, Paul

doi:10.1007/978-1-84996-432-6_55

Cited by 10 publications

(10 citation statements)

References 14 publications

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“…4 Figure 5 shows the value for minimum chip thickness obtained therein. The average value was found to be 0.2-0.4 mm, which lies in a range between 20% and 40% of the average tool edge radius (1 mm), as shown in Figure 5.…”

Section: Methodsmentioning

confidence: 97%

“…In a related research, the same methodology was used to correlate and contrast micro-milling carried out with undeformed chip thickness kept below edge radius, with micromachining keeping undeformed carried out above edge radius. 4 These were also compared with conventional macro-milling. The analysis of variance (ANOVA) results showed that in terms of surface roughness, burr width and tool wear, micro-milling carried out keeping the undeformed chip thickness above the tool edge radius was more analogous to macro-milling than micro-milling with undeformed chip thickness less than the tool edge radius.…”

Section: Methodsmentioning

confidence: 99%

“…3 A milling process may be classified as micro-milling if the milling cutter has a diameter less than 1 mm. 4 Mechanical micromachining commands a strong impact due to its versatility in terms of work materials, component interfaces and functional shapes. 2 Previous researchers have used tungsten carbide (WC) end mills to mill out walls with a minimum thickness of 18 mm and an aspect ratio of 1:24, while with mono-crystalline diamond tools 8-mm walls with an aspect ratio of 1:13 were machined.…”

Section: Introductionmentioning

confidence: 99%

“…3 A milling process may be classified as micro-milling if the milling cutter has a diameter less than 1 mm. 4…”

Section: Introductionmentioning

confidence: 99%

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Statistical analysis of process parameters in micromachining of Ti-6Al-4V alloy

Jaffery

Khan

Ali

et al. 2015

Proceedings of the Institution of Mechanical Engineers, Part B:

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The demand for miniaturized components is on the rise, especially from the biomedical and aerospace industry. As a result, there is a strong research potential towards the micro-manufacturing of biomedical and aerospace components. Titanium-based alloys are known for their biocompatibility and high strength-to-weight ratio, making them most suitable for such applications. In this research, flank wear progression, surface roughness and side burrs, the basic performance parameters of a typical micromachining operation, are presented and analysed through analysis of variance in order to determine the key process parameters. It was found that micromachining can be classified into two categories: micromachining with undeformed chip thickness below the tool edge radius and micromachining keeping the undeformed chip thickness above the tool edge radius. The results showed that when machining with undeformed chip thickness above edge radius, the feedrate remains the most significant parameter affecting tool wear (41% contribution ratio), surface roughness (83%) and burr width (80%). This result places this type of machining closer to macro-machining where feed contribution was found to be 69%, 92% and 75% as against micromachining below edge radius, where contributions stood at 17%, 53% and 52% on tool wear, surface roughness and burr width, respectively. The results underscored the importance of considering the tool edge radius in micromachining.

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Section: Methodsmentioning

confidence: 97%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…3 A milling process may be classified as micro-milling if the milling cutter has a diameter less than 1 mm. 4…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Statistical analysis of process parameters in micromachining of Ti-6Al-4V alloy

Jaffery

Khan

Ali

et al. 2015

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

show abstract

“…When the undeformed chip thickness is set less than the tool edge radius, the influence of the feed on tool wear, surface roughness, and burr width was observed to be lower. Jaffery et al [16] also pointed out that tool wear is driven by stochastic factors.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty analysis of force coefficients during micromilling of titanium alloy

Gözü

2017

Int J Adv Manuf Technol

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Predicting process forces in micromilling is difficult due to complex interaction between the cutting edge and the work material, size effect, and process dynamics. This study describes the application of Bayesian inference to identify force coefficients in the micromilling process. The Metropolis-Hastings (MH) algorithm Markov chain Monte Carlo (MCMC) approach has been used to identify probability distributions of cutting, edge, and ploughing force coefficients based on experimental measurements and a mechanistic model of micromilling. The Bayesian inference scheme allows for predicting the upper and lower limits of micromilling forces, providing useful information about stability boundary calculations and robust process optimization. In the first part of the paper, micromilling experiments are performed to investigate the influence of micromilling process parameters on machining forces, tool edge condition, and surface texture. Under the experimental conditions used in this study, built-up edge formation is observed to have a significant influence on the process outputs in micromilling of titanium alloy Ti6Al4V. In the second part, Bayesian inference was explained in detail and applied to model micromilling force prediction. The force predictions are validated with the experimental measurements. The paper concludes with a discussion of the effectiveness of employing Bayesian inference in micromilling force modeling considering special machining cases.

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Prediction of Machining Quality and Tool Wear in Micro-Turning Machine Using Machine Learning Models

Babu

Samuel

2022

Lecture Notes in Mechanical Engineering

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Analysis of process parameters in the micromachining of Ti-6Al-4V alloy

Cited by 10 publications

References 14 publications

Statistical analysis of process parameters in micromachining of Ti-6Al-4V alloy

Statistical analysis of process parameters in micromachining of Ti-6Al-4V alloy

Uncertainty analysis of force coefficients during micromilling of titanium alloy

Prediction of Machining Quality and Tool Wear in Micro-Turning Machine Using Machine Learning Models

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