# Investigations on Surface Roughness and Tool Wear Characteristics in Micro-Turning of Ti-6Al-4V Alloy

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Workpiece and Cutting Tool Material

#### 2.2. Experimental Setup

#### 2.3. Surface Roughness Measurement

^{3}/min, V

_{c}is the cutting speed, f is feed rate and a

_{p}is depth of cut. MRR is an indication of how slow or fast the machining speed works. It is an important performance parameter for micro-machining. In micro-machining (especially micro-milling), a high MRR value results in high surface roughness, rapid tool wear, and burr formation. It is important to determine the maximum MRR value without compromising surface quality and for longer tool life.

#### 2.4. Design of Experiment

#### 2.5. Response Surface Methodology

_{0}is the constant. The coefficient βi is the coefficient term for the linear terms, β

_{ii}are coefficients for the square term of the variables, and the β

_{ij}, are the coefficients for the interacting terms.

## 3. Results and Discussions

_{c}, f and a

_{p}), square terms (V

_{c}

^{2}, f

^{2}and a

_{p}

^{2}) and interaction terms (V

_{c}f, V

_{c}a

_{p}and fa

_{p}). The F-value was marked for the relative influence determination. Table 4 lists the experimental results respective to the 20 experiments, which were oriented as per the description of the design of the experiment. Besides the surface roughness parameters for the micro-turning operation, the material removal rate was considered (calculated).

#### 3.1. Model of Average Roughness (Sa)

^{2}.

_{c}

^{2}, being a non-significant term, is still in the model. This term is kept to maintain the hierarchy. After such refinement, the R

^{2}values are compared before and after the backward elimination, listed in Table 7.

^{2}and adjusted R

^{2}remained the same and they are very close to unity; however, the predicted R

^{2}value increased from 0.85 to 0.92. Moreover, the adequate precision value increased. This indicates that the refinement of the model improved the efficiency. As such, the final model for Sa, which was used for further analysis and optimization, is shown by Equation (4).

#### 3.2. Model for Maximum Roughness Height (Sz)

_{p}and a

_{p}

^{2}). As such, it is imperative to refine the model by using backward elimination. That has been done here, and the new model is listed in Table 9.

^{2}parameters in Table 10. Table 10 shows that the overall R

^{2}value decreased from 0.91 to 0.84. As such, the model has lack of fitness. It can also be noticed that the adjusted R

^{2}value of the model was closer for the backward elimination compared to the primary model. This shows that the model efficiency was increased. Equation (5), the mathematical model of the maximum height roughness, was achieved and used for further computation and optimization.

#### 3.3. Adequacy Tests

#### 3.4. Experimental Verification Test

#### 3.5. 3D Response Surface, One Factor Plots and Analysis by SEM

_{c}) on the Sa factors was found to be mixed, as shown in Figure 9. It was observed that the value of Sa tends to decrease with increases in the cutting speed, but it rises again with further increases in V

_{c}. The effect of cutting speed on the Sz value is not significant, as shown in Figure 10. The depth of cut (a

_{p}) has a significant effect on the surface roughness values (Sa and Sz), which is evident from the surface roughness plots shown in Figure 9 and Figure 10. For both cases, it was observed that surface roughness values first tend to increase when the depth of cut is increased from 5 to 15 µm. However, further rises in the depth of cut tend to produce lower values of surface roughness parameters. This trend was also supported by the SEM images taken for tool and chips for different depths of cut (5 to 15 µm), which are shown in Figure 13. The crater wear on the tool was not significant, except for the depth of cut = 15 µm, which can be observed in Figure 13. As can be seen from the SEM photographs of Figure 12 and Figure 13, tool wear is minimal and BUE and chip plastering occurs mainly at the tool tip. The nose radius is almost unchanged. Therefore, the change in surface roughness is affected by BUE and chip plaster, not by tool wear.

#### 3.6. Optimization of Surface Roughness Parameters and Material Removal Rate

_{i}, and the responses are represented by n. Depending on the condition, each response should either have a low value or high value. As the highest is the better value, the desirability is defined as Equation (7).

- For minimum Sa;
- For minimum Sz;
- For minimization of both Sa and Sz simultaneously;
- For minimum of surface roughness (Sa and Sz) and maximum MRR at the same time.

^{3}/min. The contour plot and ramp function plot respective to the optimum solution is shown in Figure 14 and Figure 15, respectively. Moreover, the solutions for all the cases are listed in Table 13.

## 4. Conclusions

- Empirical relations between cutting parameters and surface roughness (Sa and Sz) of the TiAl4V alloy was successfully developed using RSM for the micro-turning process;
- The efficiency of both models was checked according to the different R
^{2}terms. The developed models showed good accuracy in terms of correlation coefficient, close to unity. The residual plots and the outliers plot showed the adequacy of the models. Last but not least, the verification test showed superior accuracy, an error value of less than 7% for both the average roughness parameter and maximum height roughness parameter; - With the increase in feed rate, both the Sa and Sz of the TiAl4V alloy were found to be increased, while a mixed trend was observed for other cutting parameters. Overall, the most dominant factor which affects the Sa and Sz of the micro-turned TiAl4V was found to be the feed rate;
- The tool wear results show that the crater wear is the dominant wear for micro-turned Ti-6Al-4V alloys. Moreover, the higher serrations in the chips were observed at high feed rate values, which is also the reason for the poor surface roughness values;
- All optimization results are as follows:
- Minimum Sa optimization: V
_{c}= 156.14 m/min, f =10.44 μm /rev and a_{p}= 24.92 μm; - Minimum Sz optimization: V
_{c}= 339.67 m/min, f =10.55 μm /rev and a_{p}= 24.87 μm; - Minimum Sa and Sz optimization: V
_{c}= 340.49 m/min, f = 10.24 μm /rev and a_{p}= 24.87 μm; - For minimum of surface roughness (Sa and Sz) and maximum MRR at the same time: V
_{c}= 400 m/min, f = 23.71 μm/rev and a_{p}= 25 μm;

- The optimized values for Sa, Sz and MRR obtained by the multi-objective optimization approach were 0.50 μm, 4.16 μm and 239.03 mm
^{3}/min, respectively.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**(

**a**) Illustration of turning operation showing nose radius (Re), feed rate (f), maximum surface roughness (Rz); (

**b**) conventional and (

**c**) micro-cutting process h: undeformed chip thickness, Re: Edge Radius.

**Figure 2.**(

**a**) Edge radius (

**b**) nose radius (

**c**) other dimensions of the cutting tool used in micro turning process.

**Figure 3.**(

**a**) Schematic representation of micro-turning setup; (

**b**) position of cutting tool and workpiece relative to each other; (

**c**) axis definitions of experimental setup; (

**d**) close view of cutting setup.

**Figure 4.**Measurement procedure of surface roughness in current work, (

**a**) surface roughness tester; (

**b**) area under consideration for the roughness measurement; (

**c**) 3D surface topography of the area.

**Figure 5.**Residual plots for the model developed for (

**a**) average surface roughness (Sa); (

**b**) maximum roughness height (Sz).

**Figure 12.**SEM images showing the effect of feed rate on the tool and chip morphology at V

_{c}= 250 m/min and a

_{p}= 15 µm.

**Figure 13.**SEM images showing the effect of depth of cut on the tool and chip morphology at V

_{c}= 250 m/min and feed rate = 25 µm/rev.

Element | Al | V | Fe | C | O | N | H | Ti |
---|---|---|---|---|---|---|---|---|

Wt % | 6.40 | 4.16 | 0.16 | 0.028 | 0.154 | 0.017 | 0.001 | Balance |

Properties | Value |
---|---|

Tensile Strength (MPa) | 900–1000 |

Yield Strength (MPa) | 830–910 |

Elongation (%) | 10–18 |

Elastic Modulus (GPa) | 114 |

Hardness (Brinell) | 330–340 |

Levels | Cutting Speed (V_{c}) (m/min) | Feed Rate (f) (μm/rev) | Depth of Cut (a_{p}) (μm) |
---|---|---|---|

1 | 100 | 25 | 5 |

2 | 250 | 10 | 15 |

3 | 400 | 40 | 25 |

**Table 4.**Experimental results on Sa, Sz and material removal rate (MRR) for different cutting parameters.

Sr. NO | Inputs | Outputs | ||||
---|---|---|---|---|---|---|

Cutting Speed (V _{c})(m/min) | Feed Rate (f) (μm/rev) | Depth of Cut (a _{p})(μm) | Average Roughness (Sa) (μm) | Maximum Roughness Height (Sz) (μm) | Material Removal Rate (mm ^{3}/min) | |

1 | 100.00 | 25.00 | 15.00 | 0.72 | 5.94 | 37.50 |

2 | 400.00 | 10.00 | 25.00 | 0.39 | 3.35 | 100.00 |

3 | 250.00 | 10.00 | 15.00 | 0.42 | 3.83 | 37.50 |

4 | 250.00 | 25.00 | 15.00 | 0.70 | 6.98 | 93.75 |

5 | 100.00 | 10.00 | 5.00 | 0.52 | 3.48 | 05.00 |

6 | 100.00 | 40.00 | 25.00 | 0.62 | 6.87 | 100.00 |

7 | 250.00 | 40.00 | 15.00 | 0.91 | 7.48 | 150.00 |

8 | 250.00 | 25.00 | 25.00 | 0.48 | 4.23 | 156.25 |

9 | 250.00 | 25.00 | 15.00 | 0.70 | 6.98 | 93.75 |

10 | 250.00 | 25.00 | 15.00 | 0.69 | 6.93 | 93.75 |

11 | 400.00 | 40.00 | 5.00 | 0.99 | 7.12 | 80.00 |

12 | 400.00 | 40.00 | 25.00 | 0.64 | 5.02 | 400.00 |

13 | 250.00 | 25.00 | 5.00 | 0.62 | 5.06 | 31.25 |

14 | 250.00 | 25.00 | 15.00 | 0.70 | 6.95 | 93.75 |

15 | 400.00 | 10.00 | 5.00 | 0.48 | 4.07 | 20.00 |

16 | 100.00 | 40.00 | 5.00 | 1.02 | 7.85 | 20.00 |

17 | 250.00 | 25.00 | 15.00 | 0.70 | 6.85 | 93.75 |

18 | 100.00 | 10.00 | 25.00 | 0.33 | 3.63 | 25.00 |

19 | 250.00 | 25.00 | 15.00 | 0.69 | 6.77 | 93.75 |

20 | 400.00 | 25.00 | 15.00 | 0.79 | 5.59 | 150.00 |

Source | Sum of Squares | Degree of Freedom | Mean Square | F-Value | Dominance of Factor | p-Value |
---|---|---|---|---|---|---|

Model | 0.657 | 9 | 0.072 | 56.69 | 99.55% | <0.0001 |

V_{c} | 6.084 × 10^{−4} | 1 | 6.084 × 10^{−4} | 0.48 | 0.09% | 0.5040 |

f | 0.42 | 1 | 0.42 | 330.95 | 63.64% | <0.0001 |

a_{p} | 0.14 | 1 | 0.14 | 108.67 | 21.21% | <0.0001 |

V_{c}^{2} | 0.015 | 1 | 0.015 | 12.16 | 2.27% | 0.0059 |

f^{2} | 4.423 × 10^{−4} | 1 | 4.423 × 10^{−4} | 0.35 | 0.07% | 0.5676 |

a_{p}^{2} | 0.047 | 1 | 0.047 | 37.09 | 7.12% | 0.0001 |

V_{c}f | 7.812 × 10^{−5} | 1 | 7.812 × 10^{−5} | 0.062 | 0.01% | 0.8088 |

V_{c}a_{p} | 3.240 × 10^{−3} | 1 | 3.240 × 10^{−3} | 2.56 | 0.49% | 0.1407 |

f a_{p} | 0.026 | 1 | 0.026 | 20.26 | 3.94% | 0.0011 |

Residual | 0.013 | 10 | 1.266 × 10^{−3} | - | 1.97% | - |

Total | 0.66 | 19 | - | - | 100% | - |

Source | Sum of Squares | Degree of Freedom | Mean Square | F-Value | Dominance of Factor | p-Value |
---|---|---|---|---|---|---|

Model | 0.64 | 6 | 0.11 | 84.73 | 96.97% | <0.0001 |

V_{c} | 6.084 × 10^{−4} | 1 | 6.084 × 10^{−4} | 0.48 | 0.09% | 0.4999 |

f | 0.42 | 1 | 0.42 | 331.71 | 63.64% | <0.0001 |

a_{p} | 0.14 | 1 | 0.14 | 108.92 | 21.21% | <0.0001 |

V_{c}^{2} | 0.016 | 1 | 0.016 | 12.44 | 2.42% | 0.0037 |

a^{2} | 0.059 | 1 | 0.059 | 46.47 | 8.94% | <0.0001 |

f a_{p} | 0.026 | 1 | 0.026 | 20.31 | 3.94% | 0.0006 |

Residual | 0.016 | 13 | 1.263 × 10^{−3} | - | 2.42% | - |

Total | 0.66 | 19 | - | - | 100% | - |

**Table 7.**R

^{2}parameter for average roughness (Sa) model before and after the backward elimination.

Parameter | Before | After |
---|---|---|

R^{2} (overall) | 0.98 | 0.98 |

Adjusted R^{2} | 0.96 | 0.96 |

Predicted R^{2} | 0.85 | 0.92 |

Adeq Precision | 27.44 | 31.37 |

Source | Sum of Squares | Degree of Freedom | Mean Square | F-Value | Dominance of Factor | p-Value |
---|---|---|---|---|---|---|

Model | 40.00 | 9 | 4.44 | 11.29 | 91.03% | 0.0004 |

V_{c} | 0.69 | 1 | 0.69 | 1.74 | 1.57% | 0.2161 |

F | 25.54 | 1 | 25.54 | 64.87 | 58.12% | <0.0001 |

a_{p} | 2.01 | 1 | 2.01 | 5.10 | 4.57% | 0.0475 |

V_{c}^{2} | 0.018 | 1 | 0.018 | 0.045 | 0.04% | 0.8368 |

f^{2} | 0.099 | 1 | 0.099 | 0.25 | 0.23% | 0.6264 |

a_{p}^{2} | 3.96 | 1 | 3.96 | 10.06 | 9.01% | 0.0100 |

V_{c}f | 1.04 | 1 | 1.04 | 2.65 | 2.37% | 0.1345 |

V_{c}a_{p} | 0.50 | 1 | 0.50 | 1.26 | 1.14% | 0.2883 |

f a_{p} | 0.79 | 1 | 0.79 | 2.00 | 1.80% | 0.1876 |

Residual | 3.94 | 10 | 0.39 | - | 8.97% | - |

Total | 43.94 | 19 | - | - | 100% | - |

Source | Sum of Squares | Degree of Freedom | Mean Square | F-Value | Dominance of Factor | p-Value |
---|---|---|---|---|---|---|

Model | 36.82 | 3 | 12.27 | 27.57 | 83.80% | <0.0001 |

f | 25.54 | 1 | 25.54 | 57.37 | 58.13% | <0.0001 |

a_{p} | 2.01 | 1 | 2.01 | 4.51 | 4.57% | 0.0497 |

a^{2} | 9.28 | 1 | 9.28 | 20.84 | 21.12% | 0.0003 |

Residual | 7.12 | 16 | 0.45 | - | 16.20% | - |

Total | 43.94 | 19 | - | - | 100% | - |

**Table 10.**R

^{2}parameter for maximum roughness height (Sz) model before and after the backward elimination.

Parameter | Before | After |
---|---|---|

R^{2} | 0.91 | 0.84 |

Adjusted R^{2} | 0.83 | 0.81 |

Predicted R^{2} | 0.61 | 0.73 |

Adequate Precision | 10.73 | 16.78 |

Name | Goal | Lower Limit | Upper Limit | Importance |
---|---|---|---|---|

Cutting speed (Vc) | is in range | 100 | 400 | - |

Feed rate (f) | is in range | 10 | 40 | - |

Depth of cut (ap) | is in range | 5 | 25 | - |

Average roughness (Sa) | Minimize | 0.325 | 1.02 | 5 |

Maximum roughness height (Sz) | Minimize | 3.35 | 7.85 | 5 |

Material removal rate (MMR) | Maximize | 5 | 400 | 5 |

Sr. No. | Vc | f | a_{p} | Sa | Sz | MMR | Desirability |
---|---|---|---|---|---|---|---|

1 | 400.00 | 23.71 | 25.00 | 0.50 | 4.16 | 239.03 | 0.714 Selected |

2 | 400.00 | 23.88 | 25.00 | 0.50 | 4.17 | 240.45 | 0.714 |

3 | 400.00 | 23.18 | 25.00 | 0.49 | 4.13 | 234.525 | 0.713 |

4 | 400.00 | 22.24 | 24.97 | 0.49 | 4.08 | 226.261 | 0.712 |

5 | 400.00 | 22.56 | 24.94 | 0.49 | 4.11 | 228.645 | 0.710 |

6 | 400.00 | 33.41 | 25.00 | 0.59 | 4.70 | 321.503 | 0.700 |

7 | 400.00 | 10.31 | 25.00 | 0.37 | 3.16 | 125.099 | 0.659 |

8 | 100.00 | 10.00 | 5.01 | 0.47 | 3.21 | 27.4834 | 0.356 |

Optimization Cases | V_{c}(m/min) | f (μm/rev) | a_{p}(μm) | Sa (μm) | Sz (μm) | MMR (mm ^{3}/min)
| Desirability |
---|---|---|---|---|---|---|---|

Minimum Sa | 156.14 | 10.44 | 24.92 | 0.32 | - | - | 1.000 |

Minimum Sz | 339.67 | 10.55 | 24.87 | - | 3.34 | - | 1.000 |

Minimization of both Sa and Sz | 340.49 | 10.24 | 24.87 | 0.32 | 3.30 | - | 1.000 |

Minimization of Sa and Sz and maximization of MMR | 400.00 | 23.71 | 25.00 | 0.50 | 4.16 | 239.03 | 0.714 |

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**MDPI and ACS Style**

Aslantas, K.; Danish, M.; Hasçelik, A.; Mia, M.; Gupta, M.; Ginta, T.; Ijaz, H.
Investigations on Surface Roughness and Tool Wear Characteristics in Micro-Turning of Ti-6Al-4V Alloy. *Materials* **2020**, *13*, 2998.
https://doi.org/10.3390/ma13132998

**AMA Style**

Aslantas K, Danish M, Hasçelik A, Mia M, Gupta M, Ginta T, Ijaz H.
Investigations on Surface Roughness and Tool Wear Characteristics in Micro-Turning of Ti-6Al-4V Alloy. *Materials*. 2020; 13(13):2998.
https://doi.org/10.3390/ma13132998

**Chicago/Turabian Style**

Aslantas, Kubilay, Mohd Danish, Ahmet Hasçelik, Mozammel Mia, Munish Gupta, Turnad Ginta, and Hassan Ijaz.
2020. "Investigations on Surface Roughness and Tool Wear Characteristics in Micro-Turning of Ti-6Al-4V Alloy" *Materials* 13, no. 13: 2998.
https://doi.org/10.3390/ma13132998