# Parametric Effects of Single Point Incremental Forming on Hardness of AA1100 Aluminium Alloy Sheets

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## Abstract

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## 1. Introduction

_{2}emissions, because it supports recycling and facilitates novel ways of preparing raw materials. An overview of the history of ISF was written by Emmens et al. [7], Li et al. [8], and Behera et al. [9]: they discussed the enormous benefits and many advantages of SPIF and particularly referenced the flexibility of the process, which allows SPIF to be used in more applications in industries and processes. Hence, SPIF will be considered an essential process for the industry in the future.

^{2}values between 0.657 and 0.979. In other studies, different tool materials and shapes were investigated experimentally to study factors including formability, geometric accuracy [31], and surface roughness [32] on an AlMn1Mg1 sheet formed using SPIF under various forming conditions. The researchers evaluated the performance of an Artificial Neural Network (ANN) and Support Vector Regression (SVR). Two different ANN models were built in the study: an R-squared value with other validation metrics and a feed-forward neural network with a backpropagation algorithm were used. A close correspondence was found between predicted roughness, formability, and geometric accuracy in the experimental results. The researchers derived regression equations to analytically predict surface roughness in terms of Ra and Rz. Baruah et al. [33] claimed that lubrication was the largest contributing factor in the process of ISF in all three directions (rolling, transverse, and angular) when surface roughness in ISF is meant to be reduced. In fact, to date, the applied lubricant and the viscosity of the lubricants on the ISF process have not been optimised or discussed, as attested by [5,34,35]. In addition, Kumar and Gulati [34] claimed that all parameters investigated in their study were significant for forming force except lubricating oil viscosity, and they also noted that surface roughness decreased when viscosity increased [35]. According to the literature, ANN is a helpful tool—before starting new experiments—for predicting and designing predictive models to estimate expected results, behaviour, or direction based on the use of the parameters of the studied process. Using ANN before starting an actual experiment has the essential benefits of selecting the correct parameters, reducing processing time, increasing efficiency, minimising errors, and comparing actual results with predicted ones so as to reach the best values. In addition, ANN is considered one of the most powerful tools for solving engineering problems by predicting experimental data. In addition, ANN can serve as a valuable means to generate and assess different processes and prepare the final details of tools.

## 2. Material and Methods

#### 2.1. Workpiece Material

#### 2.2. Experimental Setup

## 3. Results and Discussion

#### 3.1. Feed Rate

#### 3.2. Tool Speed

#### 3.3. Tool Diameter

#### 3.4. Grease Grade

#### 3.5. Regression Equations to Calculate the Hardness of SPIF Components

_{V}is viscosity of the lubricant, C is the intercept, and Coeff is a coefficient.

#### 3.6. Contribution Analysis of Input Variables

_{v}is the number of neurons in the input layer, n

_{h}is the number of neurons in the hidden layer, y

_{j}is the absolute value of connection weights between the input and the hidden layers, hO

_{j}is the absolute value of connection weights between the hidden and the output layers, ${{\displaystyle \sum}}_{j=1}^{{n}_{v}}{\left({y}_{vj}^{i}-{y}_{vj}^{f}\right)}^{2}$ is the sum squared difference between initial connection weights and final connection weights from the input layer to the hidden layer, and ${{\displaystyle \sum}}_{j=1}^{{n}_{v}}{{\displaystyle \sum}}_{v=1}^{n}{\left({y}_{vj}^{i}-{y}_{vj}^{f}\right)}^{2}$ is the total of the sum squared difference of all inputs.

## 4. Conclusions

- An increase in the feed rate increases hardness when coolant oil is used. Hardness decreases when grease is used (which happens by way of filling the grooves between asperities with debris carried by the grease).
- The hardness of the component increases when tool speed increases.
- Increases in tool diameter result in a decrease in the hardness of components.
- Grease properties are certain to affect hardness values.
- The use of grease instead of coolant oil generates homogeneous hardness values at different points of the same formed sheet.

## 5. Recommendations for Further Research

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**(

**a**) CAD geometry and dimensions of the experimental product and (

**b**) view of an inward spiral path.

**Figure 3.**(

**a**) Digital microhardness device, (

**b**) formed parts, (

**c**) three zones of hardness measurement, (

**d**) hardness test sample of the formed part.

**Figure 6.**(

**a**) Effects of different feed rates on hardness of formed sheet when using coolant oil, (

**b**) effects of varied feed rates on hardness of formed sheet when using grease.

**Figure 7.**Effect of different tool speeds on hardness of formed sheet (feed rate: 600 mm/min; tool diameter: 10 mm; coolant: oil).

**Figure 8.**Effect of different tool diameters on hardness of formed sheet (feed rate: 600 mm/min; tool speed: 2000 rpm; coolant: oil).

**Figure 9.**Effects of different grease types on hardness of formed sheet (feed rate: 600 mm/min; tool speed: 2000 rpm; tool diameter: 10 mm).

**Figure 11.**Actual and calculated values obtained with equations of (

**a**) Linear Cross-Validation Regression, (

**b**) Linear Cross-Validation with Multiple Regression of Viscosity, (

**c**) Multiple Regression, and (

**d**) Equation Based on Biases and Weights.

**Figure 12.**Relative importance of different input variables according to the (

**a**) Garson and (

**b**) Most-Squares algorithms.

Property | Ultimate Tensile Stress, MPa | Yield Strength, MPa | Elongation, % |
---|---|---|---|

Actual | 110 | 95 | 20 |

Nominal | 110 | 103 | 25 |

Standard Deviation, σ | 0 | 4 | 2.5 |

Element | Si | Fe | Cu | Mn | Mg | Cr | Ni | Zn | Ti | Pb | B | Sn | V | Al |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Actual | 0.110 | 0.482 | 0.004 | 0.005 | 0.001 | 0.0005 | 0.004 | 0.021 | 0.021 | 0.0005 | 0.003 | 0.001 | 0.014 | balance |

Nominal | 0.5 | 0.5 | 0.2 | 0.04 | 0.01 | Other 0.15 max | balance |

Grease Type | ISO Viscosity Grade | Average Dropping Point, °C (at 25 °C) | Flash Point, °C | Viscosity at 40 °C, mm^{2}/s |
---|---|---|---|---|

EP2 | ISO VG 15 | 90 | 180 | 15 |

Kaucuklu | ISO VG 22 | 88 | 172 | 22 |

Zinol | ISO VG 32 | 88 | 170 | 32 |

Gp Grease Calcium | ISO VG 46 | 58 | 60 | 46 |

Acidity, pH | Kinematic Viscosity at 29 °C, mm^{2}/s | Boiling Point, °C |
---|---|---|

1.086 | 1.086 | 95 |

Feed Rate, mm/min | Hardness HV | |||||||
---|---|---|---|---|---|---|---|---|

Coolant Oil | Grease | |||||||

Top | Middle | Bottom | Standard Deviation, σ | Top | Middle | Bottom | Standard Deviation, σ | |

200 | 43.89 | 40.40 | 39.02 | 2.0494 | 63.30 | 58.61 | 50.56 | 5.2610 |

400 | 39.16 | 43.61 | 41.78 | 1.8262 | 59.80 | 56.70 | 49.13 | 4.4816 |

600 | 44.08 | 46.44 | 48.22 | 1.6957 | 51.29 | 53.17 | 44.70 | 3.6317 |

800 | 47.76 | 53.84 | 49.54 | 2.5522 | 43.99 | 45.72 | 45.50 | 0.7689 |

Tool Speed, rpm | Hardness HV | |||
---|---|---|---|---|

Top | Middle | Bottom | Standard Deviation, σ | |

500 | 45.83 | 41.78 | 40.10 | 2.4050 |

1000 | 50.50 | 47.48 | 42.75 | 3.1895 |

1500 | 56.83 | 55.96 | 45.90 | 4.9601 |

2000 | 61.58 | 75.51 | 57.71 | 7.6439 |

Tool Diameter, mm | Hardness HV | |||
---|---|---|---|---|

Top | Middle | Bottom | Standard Deviation, σ | |

4 | 75.51 | 57.71 | 66.61 | 7.2668 |

6 | 56.33 | 50.65 | 53.01 | 2.3299 |

8 | 43.22 | 40.62 | 44.26 | 1.5308 |

10 | 41.78 | 40.10 | 42.57 | 1.0300 |

Grease Type | ISO Viscosity Grade | Average Dropping Point (at 25 °C) | Flash Point, °C | Hardness HV | |||
---|---|---|---|---|---|---|---|

Top | Middle | Bottom | Standard Deviation, σ | ||||

Gp Grease Calcium | ISO VG 15 | 58 | 60 | 46.81 | 72.00 | 56.95 | 10.3487 |

Zinol | ISO VG 22 | 88 | 170 | 46.34 | 42.70 | 40.74 | 2.3202 |

Kaucuklu | ISO VG 32 | 88 | 172 | 42.73 | 38.12 | 40.62 | 1.8843 |

EP2 | ISO VG 46 | 90 | 180 | 45.81 | 43.30 | 38.18 | 3.1751 |

**Table 9.**Assessment of best alternative equations with different validation metrics for hardness calculation.

Validation Metric | Linear Cross-Validation Regression | Linear Cross-Validation with Multiple Regression of Viscosity | Multiple Regression | Equation Based on Biases and Weights |
---|---|---|---|---|

Mean Error | 0.0000 | 0.0000 | −0.0002 | −0.0306 |

Mean Absolute Error | 4.8183 | 3.6826 | 2.7811 | 1.8954 |

Mean Square Error | 40.9419 | 29.2784 | 22.0436 | 20.2431 |

Root Mean Square Error | 6.3986 | 5.4110 | 4.6951 | 4.4992 |

Mean Relative Error | 0.0963 | 0.0727 | 0.0555 | 0.0367 |

Standard Deviation, σ | 6.5955 | 5.5775 | 4.8396 | 4.6376 |

Standard Error of Mean | 1.5996 | 1.3527 | 1.1738 | 1.1248 |

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**MDPI and ACS Style**

Najm, S.M.; Paniti, I.; Trzepieciński, T.; Nama, S.A.; Viharos, Z.J.; Jacso, A.
Parametric Effects of Single Point Incremental Forming on Hardness of AA1100 Aluminium Alloy Sheets. *Materials* **2021**, *14*, 7263.
https://doi.org/10.3390/ma14237263

**AMA Style**

Najm SM, Paniti I, Trzepieciński T, Nama SA, Viharos ZJ, Jacso A.
Parametric Effects of Single Point Incremental Forming on Hardness of AA1100 Aluminium Alloy Sheets. *Materials*. 2021; 14(23):7263.
https://doi.org/10.3390/ma14237263

**Chicago/Turabian Style**

Najm, Sherwan Mohammed, Imre Paniti, Tomasz Trzepieciński, Sami Ali Nama, Zsolt János Viharos, and Adam Jacso.
2021. "Parametric Effects of Single Point Incremental Forming on Hardness of AA1100 Aluminium Alloy Sheets" *Materials* 14, no. 23: 7263.
https://doi.org/10.3390/ma14237263