Multi-response optimization of machining parameters in micro milling of alumina ceramics using Nd:YAG laser

Multi-response optimization of machining parameters in micro milling of alumina ceramics using Nd:YAG laser

Accepted Manuscript Multi-Response Optimization of Machining Parameters in Micro Milling of Alumina Ceramics using Nd: YAG Laser Usama Umer, Muneer Kh...

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Accepted Manuscript Multi-Response Optimization of Machining Parameters in Micro Milling of Alumina Ceramics using Nd: YAG Laser Usama Umer, Muneer Khan Mohammed, Abdulrahman Al-Ahmari PII: DOI: Reference:

S0263-2241(16)30552-8 http://dx.doi.org/10.1016/j.measurement.2016.10.004 MEASUR 4366

To appear in:

Measurement

Received Date: Revised Date: Accepted Date:

30 May 2016 28 July 2016 4 October 2016

Please cite this article as: U. Umer, M. Khan Mohammed, A. Al-Ahmari, Multi-Response Optimization of Machining Parameters in Micro Milling of Alumina Ceramics using Nd: YAG Laser, Measurement (2016), doi: http://dx.doi.org/10.1016/j.measurement.2016.10.004

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Multi-Response Optimization of Machining Parameters in Micro Milling of Alumina Ceramics using Nd: YAG Laser Usama Umer1, Muneer Khan Mohammed1, Abdulrahman Al-Ahmari1, 2 1

Princess Fatima Alnijiris’s Research Chair for Advanced Manufacturing Technology (FARCAMT), Advanced Manufacturing Institute, King Saud University, Riyadh, Saudi Arabia 2

Industrial Engineering Department, College of Engineering, King Saud University, Riyadh, Saudi Arabia

Abstract Laser micro milling by material ablation is a growing technology for developing micro features in a wide range of materials and offers various advantages in terms of both product quality and process efficiency. In contrast, other non-traditional methods such as micro electro-discharge machining, ion milling or lithography are material dependent, slow and often associated low dimensional accuracies. This paper considers the analysis and multi-response optimization of machining parameters during Nd: YAG laser micro milling of Alumina ceramic using MOGA-II. MOGA-II is a multi-objective genetic algorithm that uses a new smart multi-search elitism operator for optimization. The results show that the pulse intensity and pulse overlap have a significant effect on the depth of the machined cavity and surface roughness. Better surface finish for micro-milled alumina ceramic is obtained with low pulse overlaps, high intensities, low frequencies and shorter pulse durations. Whereas material removal rate (MRR) is mostly influenced by intensity of the laser beam and the high material removal rates are associated with high laser beam intensities. Key words: Alumina, Laser micro milling, MOGA-II

Introduction Micro-featured products based on structural ceramics provide reliability and performance in applications requiring high strength and hardness at elevated temperatures, thermal shock and wear resistances and chemical stability. However, these attractive properties also make them difficult to manufacture using conventional methods. In addition, the product performance requirements in various areas like communications, sensors and bio-

medical engineering are getting tougher day by day. The trend towards miniaturization, performance integration and shorter lifecycle is setting new challenges for designers and manufacturers and traditional manufacturing methods find it harder to cope with it [1]. Structural ceramics are traditionally being manufactured using sintering processes. These processes require expensive tooling and are best suited for high volume production. Several applications in medical devices and other areas require small batch sizes with fine and intricate product details. These requirements led the researchers to find some alternate ways to manufacture micro-featured based ceramic products. One such area is micro-layered

manufacturing

which

include

electrochemical

deposition

and

photolithography for micro-electro mechanical systems (MEMS). However, these technologies are limited to few material types and cannot be used for mass production due to slow processing [2]. Another popular method is micro machining which includes both mechanical (toolcontact) and advanced machining techniques like laser beam machining, electron beam machining, plasma arc machining and many other methods. Mechanically machined ceramics although have good dimensional accuracies and surface finish, they are usually associated with surface micro-cracks, residual stresses and pulverization layers. In addition, high tool wear, built-up edge formation and machine vibration are common issues associated with tool-contact based methods. Advanced machining methods described above do not require any tool contact and hence free from such limitations. In contrast to other radiation methods, laser beam machining provides better process control, improved product quality and does not require a vacuum chamber. In general laser milling is carried out by multiple laser pulses moving in lateral and transverse directions with certain overlap to machine an area or volume of the material. The combined effect of these lateral and transverse overlap effects the surface morphology of the machined cavity. If the pulse overlap is zero or very low, then there is a non-uniform material removal and some patterning affect. On the other hand if the pulse overlap is very high then the surface morphology is very rough due to very high material removal rates [3, 4]. Laser micro milling of structural ceramics enable good dimensional accuracies and acceptable material removal rates. However improper selection of laser parameters often

leads to undesirable surface characteristics such as micro-cracks, heat effected zones, recast layers and residual stresses. In order to obtain better product quality and process efficiency with laser beam machining, optimization of various process parameters against multiple objectives is indispensable. A poorly designed laser beam process during micro machining of ceramics usually leads to problems like changes in surface morphology due to phase transformation, recast layers due to cooling effect, crack formation due to residual stresses and heat effected zones [5]. Nd: YAG and excimer lasers are mostly used in micro machining of ceramics using pulse widths of micro, nano, pico and femtosecond range. Analysis were usually carried out to find the effect of different input parameters e.g.: laser fluence, pulse repetition rate, pulse width, pulse overlap, inside air pressure, and scanning strategies on various process and product quality measures such as material removal rate, surface roughness, heat effected zone, recast layers, dimensional accuracy etc. Machining of various ceramic oxides were carried out by Ihleman et al. [6] using nano and femtosecond lasers having different wavelengths. It was found that ablation mechanisms vary with laser pulse width. While machining with nanoseconds pulses, plasma mediation was found to be the dominant factor whereas multi-photon absorption was found with femtoseconds lasers. Kononenko et al. [7] performed ablation analysis with different materials including various ceramics in different ambient environments with different laser wavelengths and pulse widths. They reported that maximum ablation depths for through holes were obtained in vacuum. The threshold fluence for through holes with high aspect ratio depends on radiation wavelengths, pulse duration and ambient atmosphere. Jandeleit et al. [8] investigated the removal processes in metals and ceramics using 10 nanosecond and 40 picosecond Nd:YAG lasers. The threshold fluence for 40 ps laser are always found to be below than that of 10 nanosecond laser. In addition, the minimum material removal rates per pulse are smaller for picosecond laser than that are for nanosecond pulses. Heyl et al. [9] developed 3D structures for micro-tools using ultraviolet Nd:YAG laser. For hard metals above threshold fluence, the ablated volume per pulse grows almost linearly with the laser pulse energy. Better surface quality was achieved through low laser fluence.

Perrie et al. [10] machined alumina ceramic for developing micro-features using femtosecond laser in the near infrared region. With the applied fluence of 1.4 to 21 J/cm2, they were able to achieve ablation rates from 25 to 900 µm3/pulse. They concluded that surface roughness as good as 0.8 µm could be obtained by optimizing the process parameters while re-melting could be minimized using ultra-fast pulses. Wang and Zeng [11] studied the effects of Nd:YAG laser processing parameters on single layer carving depth and surface quality while 3D laser carving on alumina ceramic. They concluded that maintaining the equality between actual single layer carving depth and the slicing thickness of this layer is very important for the quality and precision of 3D laser carving. Pham et al. [12] developed 3D micro-featured products using laser beam milling in alumina and silicon nitride ceramics. Two types of products were analyzed with respect to material removal rate, dimensional accuracy and surface roughness. The effects of pulse frequency, pulse width, lamp current and scanning speed were investigated. They reported that good surface finish and dimensional accuracy are obtained with low frequency, low pulse width and low scanning speed. Laser micro drilling of alumina and zirconia were investigated by Kuar et al. [13, 14]. The quality of hole was analyzed by measuring the heat effected zone and degree of taper. Optimization was carried out using grey relational and response surface method and the results were validated experimentally. Knowles et al. [15] utilized nanosecond lasers for micro machining of metals, ceramics and polymers. Optimum intensities for metals and ceramics were found to be 10 to 100 times higher than polymers. Short pulse and shorter wavelength were found to be better for machining of ceramics, cracking due to thermal stresses could be eliminated by keeping low heat input to the bulk of workpiece. Machining of micro groove using Nd: YAG laser in aluminum titanate was studied by Dhupal et al. [16] . They optimized the processing parameters to improve the dimensional accuracy. The deviation of taper angle was found to be largely dependent on lamp current. The deviation of taper angle initially decreased and then increased with the lamp current. Hayashi et al. [17] analyzed the effect of changing harmonics on plasma generation while micro machining of ceramic using Nd:YAG laser. The plasma growth direction was found to be varying with increase in harmonics. The axial growth with fundamental

harmonics changed into radial direction with higher harmonics. They reported that weaker plasma generation and better surface quality is obtained with higher harmonics. A comparative analysis was performed by Parry et al. [18] using nano and picosecond lasers while machining zirconia ceramics. They demonstrated that crack free surface with good surface finish can be produced with picosecond lasers. Flexural strength was also found to be satisfactory using four-point bend test. Kibria et al. [19] performed multi-objective optimization to minimize surface roughness and depth deviation in laser micro-turning of alumina. Optimization was carried out using grey relational grade analysis and average power, pulse repetition rate and feed rate were found to be the significant factors. Alumina and aluminum nitride were machined with nanosecond lasers by Preusch et al. [20]. Using high pulse overlaps and fluence of 64 J/cm2 they were able to achieve material removal rates up to 94 mm3/hr and 135 mm3/hr for alumina and aluminum nitride respectively. Despite several attempts in analyzing laser micro machining of structural ceramics, most of the studies are focused on turning and drilling and limited to factorial analysis. Comprehensive studies in micro milling with Nd: YAG lasers giving complete optimization solutions are rare. In this work, four input parameters namely, power intensity, pulse frequency, pulse duration (pulse width) and pulse overlaps are optimized with regard to surface roughness, material removal rate and depth of machined layer while micro machining alumina ceramic using Nd: YAG laser.

Experimental materials and methods Alumina (aluminium Oxide, Al2O3 99.7%) is used as the workpiece material from CeramTec Germany. Each of the alumina samples were of the dimension 50 mm x 50 mm x 10 mm approximately. Lasertec-40 from DMG is used for micromachining of these alumina samples. The machine is equipped with the Q-switched Nd: YAG laser operating at a wavelength of 1064 nm with a maximum power of 30 W. The laser unit mainly consists of an Nd: YAG (neodymium-doped yttrium aluminium garnet; Nd: Y3Al5O12) rod which acts as the lasing medium, Flash lamp as a pumping source and a Q-switch for pulsing. The beam is operated in fundamental gaussian mode (TEM00) and is focused on to the sample with the help of scanning system as shown in Figure 1. The laser spot size is kept constant at 30µm.

Figure 1 Schematic diagram of laser processing system

The experiments were carried out based on the factorial design methodology with four factors viz. lamp intensity, pulse frequency, pulse duration and pulse overlap at three levels for each. Table 1 shows the selected factors and their respective levels. Lamp intensity controls the amount of energy input to the surface by the laser beam. Pulse frequency is the laser pulse repetition frequency or the number of pulses in one second. Pulse duration is the amount of time during which laser beam interacts with the workpiece. Pulse overlap is the percentage of overlap between successive pulses and it is given by the following equation (1).

   = 1 −



∗

  100

(1)

V is the laser scan speed in mm/sec, f is the pulse frequency in Hz and D is the laser spot diameter in mm.

Table 1 Factors and their respective levels Factor/levels

1

2

3

Lamp intensity (%)

85

90

95

Pulse frequency (kHz)

4

6

8

Pulse duration (µs)

2

4

6

Pulse overlap (%)

33.333

50

66.666

Lamp intensity directly controls the total power output of the laser. The average power output at the selected levels of lamp intensity was measured using the sensor and Nova II power meter from Ophir. Table 2 shows the average power output for the selected levels of lamp intensity. Table 2 Average power at selected levels of lamp intensity Lamp intensity (%)

Average power (W)

85

4.86

90

7.5

95

10.97

The responses that were considered in this study are depth per scan (DS), surface roughness (Sa) and material removal rate (MRR). The total depth of material removed for each combination of process parameters was measured by the touch probe attached to the machine after completion of selected 12 layers. The value of depth per layer (DL) related to the single scan was obtained through the Eq. (2), in which n is the number of machined layers (12 layers) and ∆z is the depth measured by the probe.

 =

∆ 

(2)

Laser micromachining is a layer by layer material removal process. In this process the laser process parameters are optimized in order to achieve the particular layer depth per one complete laser scan. Machining time is directly proportional to the number of layers. High DL gives shorter machining time but at the expense of surface quality. On the contrary very low DL means good machining quality but high machining time. Therefore, DL should be carefully selected keeping the balance between the quality of machining and machining time. Sa which is a 3D surface roughness parameter is used instead of Ra which is a 2D parameter to study the surface quality of the laser micro machined cavities. Sa is defined as the arithmetic mean of the absolute of the ordinate values within a definition area A, according to ISO 25178.



 =  ∬|" ($, &)|() (*

(3)

The measurement of surface roughness was done using Talysurf CCI 6000 which works on coherence correlation interferometry measurement technique. Figure 1 shows the setup of Talysurf CCI 6000.

Figure 2 Surface roughness measuring instrument (Talysurf CCI 6000) Square cavities of dimension 10 mm x 10 mm were machined on the alumina samples for 12 complete laser scans. Scan strategy is the manner in which the laser scans the work surface. The scan strategy used in this work is based on [19]. It has the scan lines parallel to the lamination direction for the first layer and at 45 degree increments for the next three layers as shown in Figure 3.

Figure 3 Selected laser Scan strategy

Results and Discussions Design of Experiment (DOE) based on Central Composite Design (CCD) method is selected for the present study. With four factors having three level each, the CCD-25 matrix with the output parameters is listed in Table 3. Different combinations of the parameters produced different colors of the machined cavity as shown in the Figure 4. This was also reported by previous work in the literature [22]. The change in surface color can be attributed to the different levels of energies. The parametric combinations with high energy densities resulted in darker surface due to high average temperature and corresponding thermal effects. whereas the ones with lower energy densities resulted in lighter surface of the machined cavity. The DOE table with selected input parameters and output results is shown in Table 3. Table 3: DOE runs for the experimental plan No.

Intensity

Frequency

Pulse

Overlap

Depth

Surface

Material removal

(%)

(kHz)

duration

(%)

per scan

roughness

rate (mm3/min)

(µm)

(µm)

(µs) 0

85

4

2

33.334

2.750

8.200

0.258

1

85

4

6

33.334

2.833

8.097

0.265

2

85

4

2

66.667

13.500

9.291

0.321

3

85

4

6

66.667

14.417

9.460

0.343

4

95

4

2

33.334

7.417

4.096

0.694

5

95

4

6

33.334

7.417

4.947

0.694

6

95

4

2

66.667

32.167

8.422

0.764

7

95

4

6

66.667

34.917

8.063

0.830

8

85

8

2

33.333

1.250

8.736

0.233

9

85

8

6

33.333

0.833

7.622

0.153

10

85

8

2

66.666

7.250

13.900

0.342

11

85

8

6

66.666

8.417

11.840

0.397

12

95

8

2

33.333

4.417

8.076

0.811

13

95

8

6

33.333

4.667

7.015

0.859

14

95

8

2

66.666

24.000

8.659

1.132

15

95

8

6

66.666

23.167

9.316

1.093

16

90

4

4

50.000

8.917

6.835

0.473

17

90

8

4

50.000

4.833

10.030

0.506

18

85

6

4

50.000

3.500

9.271

0.273

19

95

6

4

50.000

10.917

4.508

0.852

20

90

6

4

33.333

4.167

6.612

0.567

21

90

6

4

66.666

19.083

7.815

0.672

22

90

6

2

50.000

8.333

7.350

0.653

23

90

6

6

50.000

8.000

7.051

0.624

24

90

6

4

50.000

8.833

7.281

0.690

Figure 4 laser micro machined cavities on alumina

(a)

(b)

(c)

Figure 5 SEM images of alumina sample (a) Raw before machining (b) best surface sample 4 (c) worst surface sample 10

SEM analysis was carried in order to study the surface before and after machining. Figure 5 (a) shows the surface morphology of raw alumina before machining, figure 5(b) and 5(c) shows the surface morphology of best and worst surfaces respectively after laser micromachining. The surface morphology of sample 4 as shown in Figure 5(b) is smooth and uniform which is somewhat similar to the raw alumina surface as shown in Figure 5(a), whereas the surface morphology of sample 10 as depicted in Figure 5(c) is rough with lot of voids in between micro mountains resulting in very rough surface. It is to be noted that the worst surface was found for the setting with high pulse overlap of 66.66%. High pulse overlap means high energy per unit area which results in higher depth per scan and hence very rough surface.

Surface morphology of the laser machined surfaces was also analyzed using the 3D surface measurements. Figure 6 and Figure 7 shows the 3D surfaces and the corresponding surface parameters of sample 4 and sample 10 respectively. It can be seen that both the surface parameters Sa and Sq are lower for the sample 4 as compared to the sample 10.

Figure 6 3D surface morphology and corresponding surface parameters of sample 4

Figure 7 3D surface morphology and corresponding surface parameters of sample 10 Figure 8 elaborate the relative strength of input variables for depth per layer (DL) which are calculated by smoothing spline analysis of variance in Mode Frontier® software. Among single factors pulse overlap and intensity are found to be most significant. The combined effect of overlap and intensity is also found to be significant. Frequency and combined effect of intensity and frequency shows minimal changes in DL values.

Figure 8: Relative strength of input variables for depth per layer (DL)

Increase in the intensity, increases the average power of the laser there by removing more material per scan. Whereas, increase in pulse overlap increases the energy density which removes more depth of material. Decrease in frequency increases pulse energy when others parameters are fixed. Similarly, low pulse duration ensures higher peak pulse power. Figure 9 shows the variation of DL with overlap and Intensity at 6 kHz frequency and 4 µs pulse duration. With low and moderate overlap values there is very little increment in DL with increasing intensities. However, at higher overlaps there is marked increment in DL with increasing intensity as shown in Figure 9. This effect is somewhat reduced at higher frequencies due to low fluence. This phenomenon clearly shows that for ceramics, with low overlaps or higher scanning speeds, the effect of increase in pulse energies is negligible.

Figure 9: Effect of overlap and Intensity at 6 kHz frequency and 4 µs pulse duration on depth per layer (DL)

The variations in DL values can also be visualized using frequency-intensity graph at 50% overlap and 4 µs pulse duration as shown in Figure10. Low DL values are observed at low intensities and higher frequencies due to low laser fluence (energy per unit area). Higher DL is achieved through low frequencies and higher intensities. In addition, the

effect of frequency is negligible at low intensities and it is increasing with increase in intensity.

Figure 10: Effect of intensity and frequency at 50% overlap and 4 µs pulse duration on depth per layer Figure 11 shows the relative strength of input variables for the surface roughness (Sa) obtained while machining alumina workpiece. Intensity, pulse overlap, frequency and combined effect of frequency and pulse duration are the greatest contributors for variation in the surface roughness. It can be seen that the effect of intensity and overlap is almost equal and it is twice that of frequency, whereas pulse duration shows negligible effect on surface roughness. In addition, the combined effect of frequency and pulse duration is somewhat higher than the individual effect of pulse duration on surface roughness.

Figure 11: Relative strength of input variables for surface roughness (Sa) The effect of intensity and overlap on surface roughness (Sa) at 6 kHz frequency and 4 µs pulse duration is shown in Figure 12. Surface roughness is higher at low intensities and higher overlaps. This simply means that for alumina ceramic, low intensities correspond to non-uniform material removal which increases with low scanning speeds or high pulse overlaps.

Similarly, low surface roughness values are obtained at low to medium

overlaps with higher intensities. The effect of overlap is higher at low intensities and it is gradually decreasing with the increase in intensity values.

Figure 12: Effect of intensity and overlap at 6 kHz frequency and 4 µs pulse duration on surface roughness (Sa)

The variation in surface roughness (Sa) with pulse overlap and frequency at 90% intensity and 4 µs pulse duration is shown in Figure 13. Low surface roughness is achieved by low overlaps and low frequencies. Surface roughness shows an increasing trend with increase in overlap values particularly with in the high frequency domain. This is due to low pulse energies at higher frequencies that result in non-uniform machining and the effect is more pronounced at higher overlaps. In contrast, at low and moderate frequencies the effect of overlap is not much significant and good surface finish is obtained.

Figure 13: Effect of frequency and overlap at 90% intensity and 4 µs pulse duration on surface roughness (Sa) The effect of frequency and pulse duration on surface roughness at 95% intensity level and 33.33% overlap is shown in Figure 14. It is obvious that good surface finish is obtained with low frequencies and low pulse durations. At low frequencies, the effect of pulse duration is not much significant. However, at higher frequencies the effect is somewhat more pronounced and surface roughness is decreasing with increase in pulse duration. Similarly, at low pulse durations, the effect of frequency is higher at it is decreasing with increase in pulse duration. As discussed earlier, the single effect of pulse duration is very much less in comparison to other factors. With other factors fixed, the pulse duration however, can be varied to fine tune surface roughness to a desired value.

Figure 14: Effect of frequency and pulse duration at 95% intensity and 33.33% pulse overlap on surface roughness (Sa)

The relative strength of input variables for the material removal rate (MRR) is shown in Figure 15. Intensity has marked effect on MRR whereas pulse overlaps and frequency effects are not contributing much for the change in MRR values. In addition, the combined effect of intensity and frequency is little bit higher as compared to frequency and combined effect of overlap and frequency.

Figure 15: Relative strength of input variables for material removal rate (MRR)

Effect of intensity and overlap on material removal rate (MRR) at 6 kHz frequency and 4 µs pulse duration is given by Figure 16. High material removal rates are obtained at higher intensities and high pulse overlaps. At low intensities, pulse overlap has negligible effect on MRR and its effect increases slightly with increase in intensity values. Intensity effect on MRR is almost same for all overlap values except that it moves to high MRR zone with increase in pulse overlaps.

Figure 16: Effect of intensity and overlap at 6 kHz frequency and 4 µs pulse duration on material removal rate (MRR)

Figure 17 shows the effect of intensity and frequency at 50% pulse overlap and 4 µ s pulse duration on material removal rate (MRR). MRR is low at low intensities and does not change much with the increase of frequency. At higher intensities, MRR increases with frequency and maximum MRR corresponds to the top right corner of the contour. This indicates that at higher intensities (high average power), pulse repetition rate contributes to increase in MRR and the effect of low pulse peak power is not significant.

Figure 17: Effect of intensity and frequency at 50% pulse overlap and 4 µs pulse duration on material removal rate (MRR) Radial basis functions are selected for the development of response surfaces for the three output parameters. These interpolant surfaces pass exactly through the training points and the errors are negligible as compared to the polynomial and neural network models. These response surfaces are used as input for the optimization solver. Multi-response optimization problem is formulated in order to minimize the surface roughness and maximize the material removal rate. In addition to avoid very low depth per scan and material removal rate obtained through response surfaces a minimum limit is set for each as a constraint. In addition, design points with very high surface roughness i.e. greater than 9.0 µm are also regarded as unfeasible. The objective functions and constraint are listed in Table 4. The workflow for the optimization study is developed using Mode Frontier® software as shown in Figure 18. Table 4: Objective functions and constraints for the optimization study Objective

1) Minimize surface roughness (Sa)

functions

2) Maximize material removal rate (MRR)

Constraints

1) Depth per layer (DL) ≥ 1.0 µm 2) Surface roughness (Sa) < 9.0 µm 3) Material removal rate (MRR) ≥ 0.20 mm3/min

Figure 18: Optimization workflow using MOGA-II and RSM MOGA-II, a multi-objective genetic algorithm is used for the optimization search. It is an efficient algorithm that uses a smart multi-search elitism. This new elitism operator is able to preserve some excellent solutions without bringing premature convergence to local-optimal frontiers. For simplicity, MOGA-II requires only very few user-provided parameters, several other parameters are internally settled in order to provide robustness and efficiency to the optimizer. The algorithm attempts a total number of evaluations that is equal to the number of points in the design of experiment (DOE) table (the initial

population) multiplied by the number of generations. For further detail regarding MOGAII, readers are directed to mode frontier documentation [23]. A total of 2000 generations are requested with the original DOE matrix. Characteristics of the design points obtained after optimization runs are shown using bubble charts in Figures 17 and 18. Design points contained in the original DOE matrix are real whereas predicted ones from RSM are virtual. The 3D bubble chart is plotted using the design points against the two objective functions i.e. material removal rate (MRR) and surface roughness (Sa) as shown in Figure 19. One of the output variable i.e. depth per layer (DL) is represented by diameter of the bubbles. It can be visualized that high MRR and higher surface roughness corresponds to higher depth per layer obtained and vice-versa. As the objective of the optimization problem is to minimize the surface roughness and maximize the material removal rate, the design points corresponding to lower right corner of the bubble chart will be candidates for the optimal solution. Optimal design points are highlighted in Figure 19 and corresponds to depth per layer values between 4 to 11 microns. Four variables at a time can be analyzed using a 4D bubble chart as shown in Figure 20. Here diameter of the bubbles represents pulse overlaps whereas the color represents the intensity of the laser beam. The effect of intensity on MRR is well obvious from the 4D chart as the high MRR area corresponds to higher intensities. Similarly, low surface roughness is associated with higher intensities and low to moderate pulse overlaps. Similar findings were reported by Pham et al. [12]. Figure 21 shows a 4D bubble chart in which bubble diameter represents pulse duration and color represents the pulse frequency. Most of the design points in the high MRR and high surface roughness area have higher frequencies and long pulse duration. Optimal design points are characterized by low frequencies and shorter pulse durations.

Figure 19: A 3D bubble chart showing the design points obtained with the output variable of DL.

Figure 20: A 4D bubble chart showing the design points obtained with variables of MRR, surface roughness, pulse overlap and laser intensity.

Figure 21: A 4D bubble chart showing the design points obtained with variables of MRR, surface roughness, pulse duration and pulse repetition rate. Another way to analyze the design points is using a parallel coordinate chart as shown in Figure 22. A parallel coordinate chart can show design points with all the parameters used in the study. It is evident that most of the unfeasible design points are linked to low intensities and high pulse overlaps leading to low MRR and high surface roughness. In summary, optimum results are found with high intensities, low pulse overlaps, low frequencies and shorter pulse durations. Details of the optimal solutions are listed in Table 5. Out of six optimal deign points, the firsts two are real and corresponds to the original DOE matrix.

Figure 22: A parallel coordinate chart for the analysis of laser micromilling parameters

Table 5: Details of the optimal solutions Pulse duration

Depth per scan

Surface roughness

(µm)

(µm)

Material removal rate (mm3/min)

33.333

7.417

4.096

0.694

4

50

10.917

4.508

0.852

5

4

33.333

4.383

4.600

0.741

95

4

3

33.333

6.153

4.307

0.686

5.

95

4

4

33.333

5.653

4.492

0.681

6.

95

5

3

33.333

4.857

4.578

0.743

Intensity

Frequency

No.

(%)

(kHz)

1.

95

4

2

2.

95

6

3.

95

4.

(µsecs)

Overlap (%)

CONCLUSIONS In this paper the analysis and multi-response optimization of laser micro milling of alumina ceramic was studied in order to find the effect of laser process parameters on the selected responses such as surface roughness, depth of layer and MRR. The overall analysis and optimization study using smoothing spline ANOVA, radial basis function and MOGA-II is found to be useful for understanding the effect of different input parameters on process performance. The results show that depth of the machined cavity per layer is mostly influenced by pulse overlap and intensity of the laser. Roughness of the machined surface is mostly affected by intensity, pulse overlap and frequency of the laser beam. Material removal rate (MRR) is controlled by intensity of the laser beam. Other factors have negligible effect on MRR. Moreover, multi-objective optimization has been successfully carried out by the MOGA-II algorithms in order to obtain optimal process parameters for minimizing the surface roughness and maximizing the MRR, and the optimized solutions are inconsistent with the ANOVA results. Optimized solutions are characterized by low frequencies, shorter pulse duration, high intensities and low pulse overlaps.

ACKNOWLEDGMENTS The project was financially supported by King Saud University, Vice Deanship of Research Chair.

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Highlights



Multi-response optimization of laser micro milling of alumina ceramic.



The effect of laser process parameters on the selected responses such as surface roughness, depth of layer and MRR is analyzed.



Optimized solutions are characterized by low frequencies, shorter pulse duration, high intensities and low pulse overlaps.