# Application of ANN for Analysis of Hole Accuracy and Drilling Temperature When Drilling CFRP/Ti Alloy Stacks

^{1}

^{2}

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## Abstract

**:**

_{9}Taguchi orthogonal array and measured the drilling temperature, hole diameter, and out of roundness by using a thermocouple and coordinate measuring machine methods for ANN analysis. The results show that the drilling temperature was sensitive to the effect of stack material layer, cutting speed, and time delay factors. The hole diameter was mainly affected by feed, stack material layer, and time delay, while out of roundness was influenced by the time delay, stack material layer, and cutting speed. Overall, ANN can be used for the identification of the drilling parameters–hole quality relationship.

## 1. Introduction

_{d}) [8,14,17,18], thermal destruction [19] and damage value (Q

_{d}) [20], and burr height for Ti alloy [21,22,23]. Milentiev et al. reported the connection between tool geometry and process parameters on delamination factor in CFRP [24]. Overall, drilling of FRP/metal stacks is the critical machining operation in the manufacturing chain of aircraft components, which must be precisely controlled to achieve the required hole quality parameters.

## 2. Materials and Methods

#### 2.1. Workpiece Material and Cutting Tool

^{2}/90

^{2}] fabricated by hand lay-up technique, vacuum bag molding using vacuum pump in a controlled atmosphere. The total thickness of the CFRP plate is 9 ± 0.01 mm, with 60% fiber volume content [11]. The material of titanium alloy plate was chosen Ti-2.5Al-2Mn near α, alloy with a thickness of 8 mm, with the following mechanical properties: tensile strength of 735 MPa, elasticity modulus of 115 GPa, the density of 4550 kg/m

^{3}, and hardness of 178 HV [84]. In such a way, the total thickness of the CFRP/Ti alloy stack was 17 mm. The actual chemical composition of Ti alloy was defined at JEOL (JOEL Ltd., Tokyo, Japan) JSM-7600F scanning electron microscope (SEM) via dispersion spectroscopy (EDS) (Table 1).

#### 2.2. Proposed Approach for Drilling Temperature, Hole Diameter, and Roundness Prediction

#### 2.3. Experimental Set-Up

_{9}(34) Taguchi orthogonal array (Table 3 and Table 4). In each test, 23 holes were drilled. The drilling procedure was provided under the single-shot technique with the same process parameters for both CFRP and Ti alloy layers of the stack. The variable factors were: cutting speed varied on three levels from 15 m/min to 65 m/min, and the feed rate varied, 0.02 mm/r–0.08 mm/r. In order to approach the real production conditions, we also varied the drill bit temperature from the so-called “cold drill machining” (CDM) to “hot drill machining” (HDM) conditions. Under CDM conditions, the drill bit was cooled to a room temperature (20 ± 2 °C) for drilling each hole, which in the time domain corresponded to 120 s time delay (T

_{d}) prior to drilling the next hole (Figure 2b). Drilling under HDM conditions corresponded to the time delay (T

_{d}) from 5 s to 10 s between the adjacent holes (Figure 2c).

#### 2.4. Methodology of ANN Analysis

_{1}—number of holes, X

_{2}—cutting speed, X

_{3}—feed, X

_{4}—time delay, X

_{5}—hole depth measuring point) and output (Y

_{1}—drilling temperature, Y

_{2}—hole diameter, Y

_{3}—out of roundness). The non-homogeneity of the variables was compensated by a linear normalization, in which all values of the variables were reduced to values in the range from 0 to 1, based on the following equations:

- -
- From 50 to 70% to train new neural networks;
- -
- From 15 to 25% to control overfitting of neural networks;
- -
- From 15 to 25% to test trained neural networks.

## 3. Experimental Results and Discussion

#### 3.1. Drilling Temperature

_{g}≈ 180 °C. The effects of the cutting speed and feed rate on the temperature were negligible as compared to the drilling depth and delay time. Although a fractional DoE was used, it is evident in Figure 5 (e.g., compare the test 3 vs. 7) that the combined effect of the elevated cutting speed and feed elevates the temperature for no more than 20% at any hole depth. To recap, cutting speed and feed rate have an inferior influence on drilling temperature compared to the drilling depth and time delay.

#### 3.2. Hole Accuracy

#### 3.3. ANN Analysis of Experimental Results

_{1}, r2 = 0.923 for variable Y

_{2}, and r3 = 0.725 for variable Y

_{3}), was chosen.

_{1}), hole diameter (Y

_{2}), and hole out of roundness (Y

_{3}), was focused on the definition of each output (response) value sensitivity to factors X

_{1}–X

_{5}(Table 5) verified in the present study. The most precise architecture of ANN was defined as MLP 5-10-3 with hyperbolic tangent function, which activates hidden neurons, exponential function for activation of output neurons, and gradient learning algorithm. This architecture was used for feature analysis of the sensitivity of drilling temperature, hole diameter, and out of roundness to factors, namely cutting speed, feed, time delay, and also hole number and hole depth. However, hole number and hole depth were not classical factors considered in the design of the experiment. The sensitivity to the number of holes can be considered as a tool wear effect, while the depth of the hole corresponds to the stack layer sequence. The sensitivity of response values was evaluated in relative units, where “1” means that response is not sensitive to factors. The scale of sensitivity of analyzed responses corresponds to the correlation coefficient of the control subset. With the decreasing correlation coefficient of the control subset, the sensitivity scale was also reduced (Figure 12a).

## 4. Conclusions

- The drilling temperature is significantly affected by the time delay factor during single-shot drilling of the CFRP/Ti alloy stack. Increasing the drilling delay time between the two adjusted holes from 5 s to 120 s decreased the maximum drilling temperature from 350 °C to 150 °C in CFRP and from 690 °C to 575 °C in the Ti plate. Thus, the drill bit temperature is the key factor in controlling the drilling temperature;
- The hole diameter and out of roundness are affected by the drilling temperature. When drilling with a short time delay and aggressive feed, the drilling-induced heat transfers into the CFRP/Ti workpiece, deteriorating its fracture toughness and chip formation mechanism, respectively, that eventually results in oversizing. The diameter and out of roundness in CFRP grown along with the feed rate from 10.064 to 12.344 mm, and from 0.142 to 0.894, respectively. Low drilling temperature ensures higher hole accuracy;
- The ANN analysis allowed quantifying the sensitivities of the hole quality parameters to the drilling parameters. The drilling temperature was sensitive to cutting speed (4.33 points) and time delay (3.59 points) when drilling CFRP →Ti sequence, with a correlation coefficient of 0.97. The hole diameter was affected mainly by feed (7.97 points) and time delay (3.73 points) with a correlation coefficient of 0.94, while hole out roundness changed under the influence of time delay (5.20 points) and cutting speed (2.79 points) with a correlation coefficient of 0.74. Thus, ANN has a great potential for optimizing cutting parameters and other operational conditions of drilling holes in CFRP/Ti alloy stacks in industrial production;
- The most accurate ANN architecture to accomplish such a task is MLP-5-10-3, with a hyperbolic tangent function to activate hidden neurons, an exponential function to activate output neurons, and a gradient learning algorithm that provides an error of prediction up to 0.00388.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 2.**Principal scheme of drilling of CFRP/Ti alloy stack: (

**a**) scheme of TC embedment; (

**b**) technological scheme of drilling under “cool drill machining” condition (CDM); (

**c**) technological scheme of drilling under “hot drill machining” condition (HDM); (

**d**) scheme of electronic circuit of wireless system.

**Figure 5.**The drilling temperature measured in the tests № 1–№ 9 for the hole № 4: (

**a**) 3D graph; (

**b**) 2D graph.

**Figure 6.**The drilling temperature measured in the tests № 1–№ 9 for the hole № 23: (

**a**) 3D graph; (

**b**) 2D graph.

**Figure 11.**Average prediction errors for the test samples (

**a**) and correlation coefficients r1, r2, and r3 in-between the output and predicted values (

**b**).

**Figure 12.**Correlation coefficient of output data (response) (

**a**), and evaluation of response values sensitivity to factors (

**b**).

Ti | Al | C | O | Si | Mn | Fe |
---|---|---|---|---|---|---|

96.42% | 1.92% | 0.21% | 0.19% | 0.17%, | 0.89% | 0.20% |

Geometric Parameters | Drill Number in Respect to the Test Number | ||||||||
---|---|---|---|---|---|---|---|---|---|

1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |

D, (mm) | 10.008 | 10.003 | 10.003 | 10.003 | 10.000 | 10.000 | 10.000 | 10.000 | 10.000 |

Radial runout, (mm) | 0.010 | 0.012 | 0.016 | 0.008 | 0.008 | 0.008 | 0.008 | 0.008 | 0.008 |

Point angle, (θ°) | 140.52 | 140.61 | 140.35 | 140.30 | 140.84 | 140.60 | 140.60 | 140.60 | 140.60 |

Axial relief angle, (α_{a.r}°) | 7.54 | 7.42 | 8.18 | 20.14 | 7.58 | 7.54 | 8.26 | 8.52 | 7.50 |

Chisel edge angle, (ψ°) | 44.21 | 45.33 | 55.17 | 24.62 | 55.62 | 54.40 | 52.27 | 53.59 | 58.33 |

Helix angle, (ω°) | 30.00 | 30.09 | 29.92 | 29.93 | 29.91 | 29.99 | 29.81 | 29.97 | 30.06 |

Drilling Performance (Factors) | Levels of Factors | |||
---|---|---|---|---|

1 | 2 | 3 | ||

A | Cutting speed, v_{c} (m/min) | 15 | 40 | 65 |

B | Feed, f (mm/r) | 0.02 | 0.05 | 0.08 |

C | Time delay, T_{d} (s) | 120 | 10 | 5 |

Test № | Cutting Speed, v (m/min) | Feed, f (mm/r) | Time Delay, T _{d} (s) | Remark to Time Delay |
---|---|---|---|---|

1 | 15 | 0.02 | 120 | CDM |

2 | 15 | 0.05 | 10 | HDM |

3 | 15 | 0.08 | 5 | HDM |

4 | 40 | 0.02 | 10 | HDM |

5 | 40 | 0.05 | 5 | HDM |

6 | 40 | 0.08 | 120 | CDM |

7 | 65 | 0.02 | 5 | HDM |

8 | 65 | 0.05 | 120 | CDM |

9 | 65 | 0.08 | 10 | HDM |

A | B | C | D | E | F | G | H | I | |
---|---|---|---|---|---|---|---|---|---|

1 | Test № | Hole № | Cutting Speed, v (m/min) | Feed, f (mm/r) | Time Delay, Tt (s) | Hole Depth Measuring Point, (mm) | Drilling Temperature, t (°C) | Hole Diameter,D (mm) | Hole Out of Roundness, ΔD (mm) |

2 | X_{1} | X_{2} | X_{3} | X_{4} | X_{5} | Y_{1} | Y_{2} | Y_{3} | |

3 | 1 | 1 | 15 | 0.02 | 120 | 0.5 | 58.68 | 10.118 | 0.031 |

4 | 1 | 1 | 15 | 0.02 | 120 | 1 | 61.26 | 10.139 | 0.022 |

5 | 1 | 1 | 15 | 0.02 | 120 | 1.5 | 63.37 | 10.178 | 0.026 |

6 | 1 | 1 | 15 | 0.02 | 120 | 2 | 65.06 | 10.184 | 0.031 |

7 | 1 | 1 | 15 | 0.02 | 120 | 2.5 | 67 | 10.202 | 0.053 |

8 | 1 | 1 | 15 | 0.02 | 120 | 3 | 67.44 | 10.215 | 0.062 |

9 | 1 | 1 | 15 | 0.02 | 120 | 3.5 | 67.68 | 10.239 | 0.067 |

10 | 1 | 1 | 15 | 0.02 | 120 | 4 | 67.79 | 10.282 | 0.082 |

4745 | 9 | 23 | 65 | 0.08 | 10 | 10.5 | 422.14 | 10.097 | 0.048 |

4746 | 9 | 23 | 65 | 0.08 | 10 | 11 | 442.94 | 10.079 | 0.052 |

4747 | 9 | 23 | 65 | 0.08 | 10 | 11.5 | 460.33 | 10.064 | 0.055 |

4748 | 9 | 23 | 65 | 0.08 | 10 | 12 | 474.01 | 10.065 | 0.055 |

4749 | 9 | 23 | 65 | 0.08 | 10 | 12.5 | 494.37 | 10.063 | 0.063 |

4750 | 9 | 23 | 65 | 0.08 | 10 | 13 | 508.78 | 10.065 | 0.061 |

4751 | 9 | 23 | 65 | 0.08 | 10 | 13.5 | 529.73 | 10.057 | 0.063 |

4752 | 9 | 23 | 65 | 0.08 | 10 | 14 | 533.29 | 10.055 | 0.065 |

4753 | 9 | 23 | 65 | 0.08 | 10 | 14.5 | 504.25 | 10.056 | 0.07 |

4754 | 9 | 23 | 65 | 0.08 | 10 | 15 | 510.09 | 10.057 | 0.077 |

№ | Architecture | Function to Activate Hidden Neurons | Function to Activate Output Neurons | Learning Algorithm | Sensitivity | ||||
---|---|---|---|---|---|---|---|---|---|

X_{1} | X_{2} | X_{3} | X_{4} | X_{5} | |||||

1 | MLP 5-10-3 | Hyperbolic tangent | Exponential | Gradient | 1.422 | 3.534 | 2.276 | 4.802 | 16.83 |

2 | MLP 5-11-3 | Logistic sigmoid | Logistic sigmoid | BFGS | 1.979 | 7.209 | 2.74 | 8.326 | 14.37 |

3 | MLP 5-10-3 | Logistic sigmoid | Exponential | BFGS | 1.663 | 3.164 | 1.694 | 46.87 | 12.62 |

4 | MLP 5-11-3 | Hyperbolic tangent | Exponential | BFGS | 1.239 | 2.445 | 1.195 | 6.851 | 14.32 |

5 | MLP 5-11-3 | Identity | Logistic sigmoid | BFGS | 1.243 | 2.941 | 1.401 | 29.51 | 13.15 |

6 | MLP 5-9-3 | Logistic sigmoid | Exponential | BFGS | 2.223 | 1.965 | 1.254 | 7.033 | 14.04 |

7 | MLP 5-11-3 | Hyperbolic tangent | Logistic sigmoid | BFGS | 1.521 | 4.18 | 1.792 | 4.207 | 12.28 |

8 | MLP 5-9-3 | Logistic sigmoid | Exponential | Gradient | 1.225 | 2.845 | 1.859 | 3.878 | 12.52 |

9 | MLP 5-11-3 | Logistic sigmoid | Logistic sigmoid | Gradient | 2.123 | 3.669 | 1.771 | 3.469 | 12.27 |

10 | MLP 5-10-3 | Exponential | Logistic sigmoid | BFGS | 1.803 | 3.373 | 2.847 | 13.75 | 10.63 |

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## Share and Cite

**MDPI and ACS Style**

Kolesnyk, V.; Peterka, J.; Alekseev, O.; Neshta, A.; Xu, J.; Lysenko, B.; Sahul, M.; Martinovič, J.; Hrbal, J.
Application of ANN for Analysis of Hole Accuracy and Drilling Temperature When Drilling CFRP/Ti Alloy Stacks. *Materials* **2022**, *15*, 1940.
https://doi.org/10.3390/ma15051940

**AMA Style**

Kolesnyk V, Peterka J, Alekseev O, Neshta A, Xu J, Lysenko B, Sahul M, Martinovič J, Hrbal J.
Application of ANN for Analysis of Hole Accuracy and Drilling Temperature When Drilling CFRP/Ti Alloy Stacks. *Materials*. 2022; 15(5):1940.
https://doi.org/10.3390/ma15051940

**Chicago/Turabian Style**

Kolesnyk, Vitalii, Jozef Peterka, Oleksandr Alekseev, Anna Neshta, Jinyang Xu, Bohdan Lysenko, Martin Sahul, Jozef Martinovič, and Jakub Hrbal.
2022. "Application of ANN for Analysis of Hole Accuracy and Drilling Temperature When Drilling CFRP/Ti Alloy Stacks" *Materials* 15, no. 5: 1940.
https://doi.org/10.3390/ma15051940