**3.2 Fabrication of microfluidic using micromilling**

The Taguchi method as shown in **Table 2** is used taking into account 3 main parameters, namely, spindle speed, cutting depth, and feed rate to obtain the lowest surface roughness. The Taguchi method which uses 3 parameters along with 3 stages is used as **Table 2**. Spindle speeds consisting of 4000 rpm, 5000 rpm, and 6000 rpm, and spindle speeds lower than 10,000 rpm are used because PMMA material will burn when high speeds are used, as high speeds can increase the temperature on the tool can cause micro flow size the result is greater than desired. The cutting depths used for each cut are 0.01 μm, 0.025 μm, and 0.05. This is to ensure that the discarded chip is smaller than the tip of the tool. While the feed rate used is 10 mm/min, 15 mm/min, and 20 mm/min. Due to the high feed rate it can cause the tool to break. The total number of experiments produced is 9 experiments as shown in **Table 3**, each surface roughness average will be recorded, based on the smallerer the better method, and the smallest surface roughness average parameter will be taken. Then the optimal parameters will be repeated 10 times to ensure that the parameters produce consistent and stable results.

After analyzing the experimental data from **Table 4**, the lowest surface roughness can be obtained by using a spindle speed of 4000 rpm, a feed rate of 10 mm/ min and a depth cut of 0.01 mm. However, based on **Table 4**, it can be seen that while the spindle speed is 6000 rpm, cutting depth and feed rate do not have a significant impact on surface roughness, where the average surface roughness is recorded around 100 nm to 200 nm, at the same time, increasing cutting depth and feed rate, increasing average surface roughness resulting. Moreover, it can be observed that all the resulting surface roughness is less than 450 nm. Next, to validate the experiment, 10 microcontrollers were built on PMMA with spindle speed parameters of 4000 rpm, feed rate of 10 mm/min and depth depth of 0.01.


#### **Table 2.**

*Advances in Microfluidics and Nanofluids*

Coated

micro-manufacturing.

0.45 mm Diamond

0.1 mm to 0.5 mm

**Table 1.**

**3.1 Design of microfluidic**

**3. Case study**

thickness of 2 mm.

roughness can be achieved up to 38 nm if the micro tool used is coated with the diamond. Micro–tool coated with high-cost diamond are not an option for

*Surface roughness using different of material, spindle speed, feed rate and depth of cut.*

0.8 mm Carbide 2000 rpm 2 mm/min 1.5 μm 0.352 μm [17]

0.2 mm N/A 20,000 rpm 300 mm/min 10 μm 0.13 μm [19]

0.8 mm Carbide 30,000 rpm 2.65 mm/min 40 μm 128.24 nm [21]

Carbide 10,000 rpm 20 mm/min 10–20 μm 70–85 nm [20]

**Diameter Material Spindle speed Feed rate Depth of** 

**cut**

150,000 rpm 5 μm/flute 50 μm 38 nm [18]

**Surface roughness** **Reference**

Since this study uses a micro milling a microfluidic design with a rectangular geometry will be used. From **Figure 3**, the designed depth is 50 um, 200 um wide, and the circle on the inlet and outlet has a diameter of 0.6 um. **Figure 3** shows microfluidics with 2 layer PMMA to be fabricated. From **Figure 3**, the top layer has 4 holes with a diameter of 0.8 mm, the design of the hole is based on the need to place a tube with an outer diameter of 0.7 mm. While the design for the bottom layer of microfluidics, there is a circular inlet and outlet with a diameter of 0.6 mm which is smaller than the outer diameter of the tube, to allow the tube to be above

The tool used in this research is a 0.2 mm diameter tool made of carbide material, has 2 flutes and Aluminum coated. While the workpiece that will be used in this research is Poly (methyl methacrylate) or referred to as acrylic which has a

the microfluidic layer and the entire fluid can enter the micro flow.

**84**

**Figure 3.**

*Top and bottom layer of microfluidic.*

*Machining parameter (Taguchi method).*


#### **Table 3.**

*Experiment number (Taguchi method).*


**Table 4.**

*Surface roughness by using different machining parameters.*

During the machining process, a drop of water is placed on the substrate to remove debris during machining. The average surface roughness obtained from 10 validation experiments is shown in **Table 5**, where the average roughness is 24.0824 nm with a standard deviation of 4.2509 nm.

Selecting the cutting depth range and the feed rate with less than the minimum value will result in an increase in machining time, however, the cutting depth value, spindle speed and high feed rate, can increase the risk of damaged tool as reported [22]. From **Table 4** a total of 9 microchannel with a depth of 50 μm and a width of 200 μm were tested using the Alicona Infinite Focus Microscopy (IFM) 3D Optical Profiler used to measure the roughness of the surface on the cut of microchannels. The area of surface roughness shown at **Figure 4**. Analytical factors can be used to determine the main cutting parameters in the micro milling of the PMMA substrate. Based on **Table 4**, the larger the resulting range, the greater the influence of these factors on surface roughness, in this research, the depth of cutting indicates the largest range. This shows that the depth of cutting has a great influence on surface roughness. Whereas, the feed rate indicates a low range, this means that the feed rate has the least influence on surface roughness.


**87**

*Micro Milling Process for the Rapid Prototyping of Microfluidic Devices*

*Area for surface roughness measurement using infinite focus microscopy.*

**Table 4** also shows the optimal cutting parameters for obtaining minimal surface roughness. **Table 4** shows the combination of machining parameters to obtain the smallest surface roughness is the spindle speed 4000 rpm, cutting depth 0.01 μm and feed rate 10 mm/min. Based on **Table 4**, the average surface roughness average achieved for this parameter is 67.3018 nm. Moreover, from this study, based on **Figure 5**, if the study is compared by looking at the same parameter readings, shows that the spindle speed of 6000 rpm can produce the lowest surface roughness compared to the spindle speed of 4000 rpm and 5000 rpm. It shows that the cutting depth of 0.01 μm produces the lowest surface roughness followed by 0.025 mm and 0.05 mm. Furthemore, the feed rate of 15 mm/min produces the lowest surface roughness followed by 10 mm/min and 20 mm/min. Based on **Table 4**, it shows that the cutting depth most influences the roughness of the resulting surface followed by spindle speed and feed rate. This is in line with the theory that low cutting depths can result in low chip loads, this allows lower surface roughness to be achieved. As

previously discussed, low depth of cut can result in low surface roughness.

After successful microfluidic installation, the experiment was continued by testing the hydrodynamic focus. This feature is important to ensure that the designed microfluidics can operate, there are several factors that can cause the microfluidics to be unable to operate, firstly due to clogged microwaves, secondly because the bond between the 2 wafers is not strong causing small holes that cause leakage. Based on **Table 6**, the resulting focusing width is related to the sheath and sample flow rate ratio. The resulting focusing width can be adjusted according to the desired application. However, the sample flow width must be adjusted according to the specific cell size for detection, at the same time, allowing cells to pass through them one by one on the sample flow, this is to increase the sensitivity of the constructed device. Reynold numbers are kept in low condition, this is to avoid

Based on this hydrodynamic focusing experiment shown at **Figure 6**, the side

path with a flow rate of 3000 μl/min and the flow rate for the sample path of 10 μl/min can produce a focusing width as low as 39 μm. However, with an sheath flow rate of 3000 μl/min and a sample path flow rate of 100 μl/min, the resulting focusing width is 60 μm. Both of these results answer for the objective of the study, namely the production of hydrodynamic focusing around 60 μm. Based on **Table 6** it can also be observed, that if a flow rate ratio of 10 and 100 is used, a

**3.3 Hydrodynamic focusing experiment**

uninterrupted flow of microfluidics [23].

*DOI: http://dx.doi.org/10.5772/intechopen.96723*

**Figure 4.**

#### **Table 5.**

*Surface roughness by using optimal machining parameters.*

*Micro Milling Process for the Rapid Prototyping of Microfluidic Devices DOI: http://dx.doi.org/10.5772/intechopen.96723*

*Advances in Microfluidics and Nanofluids*

*Surface roughness by using different machining parameters.*

with a standard deviation of 4.2509 nm.

feed rate has the least influence on surface roughness.

*Surface roughness by using optimal machining parameters.*

**Number Surface roughness (nm)**

1 21.3106 2 20.1148 3 26.7489 4 23.628 5 19.3741 6 23.5145 7 22.9668 8 27.5627 9 33.6486 10 21.9548 Average 24.08238

**Table 4.**

**Number Spindle speed (rpm) Depth of cut (mm) Feed rate (mm/min) Surface roughness (nm)**

During the machining process, a drop of water is placed on the substrate to remove debris during machining. The average surface roughness obtained from 10 validation experiments is shown in **Table 5**, where the average roughness is 24.0824 nm

Selecting the cutting depth range and the feed rate with less than the minimum value will result in an increase in machining time, however, the cutting depth value, spindle speed and high feed rate, can increase the risk of damaged tool as reported [22]. From **Table 4** a total of 9 microchannel with a depth of 50 μm and a width of 200 μm were tested using the Alicona Infinite Focus Microscopy (IFM) 3D Optical Profiler used to measure the roughness of the surface on the cut of microchannels. The area of surface roughness shown at **Figure 4**. Analytical factors can be used to determine the main cutting parameters in the micro milling of the PMMA substrate. Based on **Table 4**, the larger the resulting range, the greater the influence of these factors on surface roughness, in this research, the depth of cutting indicates the largest range. This shows that the depth of cutting has a great influence on surface roughness. Whereas, the feed rate indicates a low range, this means that the

 4000 0.01 10 67.3018 4000 0.025 15 267.2102 4000 0.05 20 406.8926 5000 0.01 15 170.2524 5000 0.025 20 350.468 5000 0.05 10 442.6494 6000 0.01 20 119.4901 6000 0.025 10 139.6821 6000 0.05 15 170.2192

**86**

**Table 5.**

**Figure 4.** *Area for surface roughness measurement using infinite focus microscopy.*

**Table 4** also shows the optimal cutting parameters for obtaining minimal surface roughness. **Table 4** shows the combination of machining parameters to obtain the smallest surface roughness is the spindle speed 4000 rpm, cutting depth 0.01 μm and feed rate 10 mm/min. Based on **Table 4**, the average surface roughness average achieved for this parameter is 67.3018 nm. Moreover, from this study, based on **Figure 5**, if the study is compared by looking at the same parameter readings, shows that the spindle speed of 6000 rpm can produce the lowest surface roughness compared to the spindle speed of 4000 rpm and 5000 rpm. It shows that the cutting depth of 0.01 μm produces the lowest surface roughness followed by 0.025 mm and 0.05 mm. Furthemore, the feed rate of 15 mm/min produces the lowest surface roughness followed by 10 mm/min and 20 mm/min. Based on **Table 4**, it shows that the cutting depth most influences the roughness of the resulting surface followed by spindle speed and feed rate. This is in line with the theory that low cutting depths can result in low chip loads, this allows lower surface roughness to be achieved. As previously discussed, low depth of cut can result in low surface roughness.
