# Wear Improvement of Tools in the Cold Forging Process for Long Hex Flange Nuts

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## Abstract

**:**

## 1. Introduction

## 2. Research Methodology and Experimental Structure

#### 2.1. Research Methodology

#### 2.2. Mechanical Properties of Metal Materials

- (1)
- Upper punch, high-speed steel SKH55 is characterized by high wear resistance, sintering resistance and high compressive strength.
- (2)
- Lower punch, SKH9 is characterized by high wear resistance, high compressive strength, excellent surface treatment processing and temper softening resistance.
- (3)
- Die, WC is characterized by extremely high hardness, wear resistance and high compressive strength.

Workpiece | Upper Punch | Die | Lower Punch | |
---|---|---|---|---|

Properties | Elastic-plastic | Rigid | ||

Material | C1010 | SKH55 | WC | SKH9 |

Mesh number | 35,000 | 30,000 | ||

Hardness | HRC38 | HRC66 | HRC77 | HRC61 |

#### 2.3. Establishing the Flow Stress-Strain Curve

#### 2.3.1. Cylinder Experiment

**Figure 2.**Compression experiment and compressed cylindrical samples at 0%, 30%, 60% and 75% reductions.

#### 2.3.2. Calculation of Flow Stress-Strain Curve

_{E}is engineering stress, F is load, A

_{0}is cross-sectional area, ε

_{E}is engineering strain, L is changed length and L

_{0}is original length. The true stress-true strain equations are:

_{T}is true stress and ε

_{T}is true strain. The flow stress curve of the C1010 material at room temperature can be determined (Figure 3) based on the above equations and the load displacement diagram. When received, the flow stress curve of the material under true forging conditions was imported into the material database in the DEFORM-3D finite element analysis software to more precisely simulate the material’s changes during forging and, thereby, to allow simulation results to better represent realistic conditions. The flow curve derived by statistical regression was represented as σ = 568.17ε

^{0.09}.

**Figure 3.**True-stress and true-strain curve of C1010 (H

_{i}was the initial height of the compressed cylindrical sample, and D

_{i}was its diameter).

#### 2.4. Measurement of the Constant Shear Friction Factor

#### 2.4.1. Establishment of the Constant Shear Friction Factor Calibration Curve

_{0}is the original ring radius.

#### 2.4.2. Ring Compression Test

#### 2.5. DEFORM Simulation Parameter Planning

^{0.09}. Punch speed was set to 253 mm/s downward and each step set to 0.1 mm. The friction coefficient was set to 0.17 with a constant shear friction mode according to the experimental results. The wear analysis in DEFORM was performed using the Archard wear theory, which is the only one that is broadly accepted and used in metal forming. Additionally, the wear equation was a simple model used for describing sliding wear and based on asperity contact theory. The Archard wear theory calculation equation is:

^{−6}was used in this simulation [17,18]. From the equation:

- (1)
- The wear volume of the material is proportional to the sliding distance,
- (2)
- The wear volume of the material is proportional to the load,
- (3)
- The wear volume of the material is inversely proportional to hardness and,
- (4)
- The wear volume of the material is irrespective of sliding speed.

#### 2.6. Preform Design and Planning

#### 2.7. Taguchi Quality Method

_{i}is the measured value and n the number of repeated measurements.

_{ij}is the mean of the S/N ratio containing I factors and j levels, k is the k-th S/N ratio with i factors and j levels and N is the number of experiments with i factors and j levels.

_{16}(4

^{5}) were applied to acquire the forming processes. The 16 sets contain five factors, each of which has four levels for an experiment.

Factors | Specifics | Level 1 | Level 2 | Level 3 | Level 4 |
---|---|---|---|---|---|

A | f (ϕ, mm) | 0 | 1.725 | 3.45 | 5.175 |

B | h (mm) | 0.5 | 0.7 | 0.8 | 1 |

C | α (Deg) | 8 | 10 | 12 | 15 |

D | r (mm) | 0.3 | 0.5 | 0.7 | 1 |

E | d (ϕ, mm) | 6.71 | 6.76 | 6.82 | 6.9 |

## 3. Results and Discussion

#### 3.1. Load Analysis of the Preform Design

#### 3.2. Preform Design Wear Analysis

**Figure 9.**Velocity, effective stress and wear distribution of the first stage in 3D. (

**a**) Velocity; (

**b**) Effective stress; (

**c**) Wear.

**Figure 10.**Velocity, effective stress and wear distribution of the second stage in 3D. (

**a**) Velocity; (

**b**) Effective stress; (

**c**) Wear.

**Figure 11.**Velocity, effective stress and wear distribution of the third stage in 3D. (

**a**) Velocity; (

**b**) Effective stress; (

**c**) Wear.

**Figure 12.**Velocity, effective stress and wear distribution of the fourth stage in 3D. (

**a**) Velocity; (

**b**) Effective stress; (

**c**) Wear.

**Figure 13.**Velocity, effective stress and wear distribution of the fifth stage in 3D. (

**a**) Velocity; (

**b**) Effective stress; (

**c**) Wear.

**Figure 14.**Velocity, effective stress and wear distribution of the sixth stage in 3D. (

**a**) Velocity; (

**b**) Effective stress; (

**c**) Wear.

#### 3.3. Simulating Wear Optimization

_{16}(4

^{5}). The forging load considered was chosen as a reference for comparison with the minimum wear. Table 4 and Table 5 show the factor response tables of the S/N

_{W}ratio on wear and the S/N

_{L}ratio on forging load, which were calculated from Table 3’s simulation results. Level values listed under columns present the effects of variability, and the variability among different levels could be regarded as an effect of controlling factors on S/N

_{W}or S/N

_{L}. The range column in Table 4 shows the maximum range of variability, where a larger variability in rank is more important to design optimization. Hence, arc length (r) was the largest factor in the simulation results, followed by top flat (f), ring height (h), outer diameter (d) and oblique angle (α) (Table 4). As small S/N

_{W}ratios were considered better (smaller-the-better), the optimal simulation settings for minimum wear were A3B4C3D1E4, implying a 3.45 mm top flat, 1 mm head height, 12° oblique angle, 0.3 mm arc length and 6.9 mm outer diameter. The quality characteristic values in Figure 16 represent the response graph according to Table 4, where x-axis A1 represented the reaction with Controlling Factor A and Level Number 1. The y-axis represented the S/N ratio of wear. The notation in this figure is similar to that of Table 4. D, A, B and E were significant factors, and C had little influence in all simulations.

Exp. | Wear (nm) | S/N_{W} (dB) | Load (kN) | S/N_{L} (dB) |
---|---|---|---|---|

1 | 525 | −54.40 | 83.0 | −38.38 |

2 | 819 | −58.27 | 74.7 | −37.47 |

3 | 655 | −56.32 | 75.4 | −37.55 |

4 | 717 | −57.11 | 72.1 | −37.16 |

5 | 768 | −57.71 | 76.0 | −37.62 |

6 | 866 | −58.75 | 70.2 | −36.93 |

7 | 568 | −55.09 | 75.3 | −37.54 |

8 | 618 | −55.82 | 84.3 | −38.52 |

9 | 897 | −59.06 | 73.5 | −37.33 |

10 | 654 | −56.31 | 86.8 | −38.77 |

11 | 621 | −55.86 | 76.0 | −37.62 |

12 | 441 | −52.89 | 78.0 | −37.84 |

13 | 919 | −59.27 | 77.4 | −37.77 |

14 | 519 | −54.30 | 77.9 | −37.83 |

15 | 903 | −59.11 | 92.5 | −39.32 |

16 | 837 | −58.45 | 72.3 | −37.18 |

A (f) | B (h) | C (α) | D (r) | E (d) | |
---|---|---|---|---|---|

Level 1 | −56.53 | −57.61 | −56.87 | −54.17 | −56.41 |

Level 2 | −56.84 | −56.91 | −56.99 | −57.30 | −57.72 |

Level 3 | −56.03 | −56.60 | −56.38 | −57.20 | −56.81 |

Level 4 | −57.78 | −56.07 | −56.94 | −58.51 | −56.25 |

Range | 1.75 | 1.54 | 0.62 | 4.34 | 1.47 |

Rank | 2 | 3 | 5 | 1 | 4 |

A (f) | B (h) | C (α) | D (r) | E (d) | |
---|---|---|---|---|---|

Level 1 | −37.64 | −37.77 | −37.53 | −37.90 | −38.75 |

Level 2 | −37.65 | −37.75 | −38.06 | −37.84 | −37.38 |

Level 3 | −37.89 | −38.01 | −37.81 | −37.78 | −37.52 |

Level 4 | −38.03 | −37.67 | −37.81 | −37.68 | −37.56 |

Range | 0.39 | 0.33 | 0.54 | 0.21 | 1.37 |

Rank | 3 | 4 | 2 | 5 | 1 |

Wear (nm) | Load (kN) | |||
---|---|---|---|---|

Simulation | Predicted | Simulation | Predicted | |

Original design | 473.00 | 475.14 | 73.55 | 72.14 |

Optimal design | 378.58 | 384.99 | 76.50 | 76.01 |

Improvement | 94.42 (19.87%) | 90.15 (18.97%) | −2.95 (−4.01%) | −3.87 (−5.37%) |

#### 3.4. Complete Product and Simulation Size Measurement

**Figure 19.**Workpieces of the simulation and actual long hex flange nuts from the original billet and the first to sixth stages.

Exp. | FEM | Error (%) | |
---|---|---|---|

S1 (mm) | 11.96 | 11.90 | −0.51 |

S2 (mm) | 13.64 | 13.74 | 0.73 |

S3 (mm) | 6.89 | 6.90 | 0.15 |

S4 (mm) | 49.88 | 49.95 | 0.14 |

S5 (mm) | 1.60 | 1.63 | 1.87 |

S6 (mm) | 17.33 | 17.30 | −0.17 |

S7 (°) | 119.00 | 120.00 | 0.84 |

## 4. Conclusions

- (1)
- Introducing CAD/CAE to new fastener product forming and die design analysis can effectively shorten product development schedules and reduce the number of die testing intervals.
- (2)
- Maximum wear depth and the position of stress concentration between die and workpiece can be determined through die action and workpiece flow during the third and fifth stages. In these cases, we recommend using tungsten steel for longer-wearing punches having reduced costs.
- (3)
- Optimization analysis of the upper punch during the third stage indicated a potential for 19.87% improvement in die life. The arc length of the upper punch was the primary impact factor, followed by flat top, ring height, outer diameter and oblique angle.
- (4)
- SEM micrographs of worn surfaces indicated that adhesive wear may be the primary wear mechanism.
- (5)
- In comparing simulation versus real production sizes, the nut forging size error was 2%, thereby demonstrating the accuracy of the simulated forming process.

## Acknowledgments

## Author Contributions

## Conflicts of Interest

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**MDPI and ACS Style**

Hsia, S.-Y.; Shih, P.-Y.
Wear Improvement of Tools in the Cold Forging Process for Long Hex Flange Nuts. *Materials* **2015**, *8*, 6640-6657.
https://doi.org/10.3390/ma8105328

**AMA Style**

Hsia S-Y, Shih P-Y.
Wear Improvement of Tools in the Cold Forging Process for Long Hex Flange Nuts. *Materials*. 2015; 8(10):6640-6657.
https://doi.org/10.3390/ma8105328

**Chicago/Turabian Style**

Hsia, Shao-Yi, and Po-Yueh Shih.
2015. "Wear Improvement of Tools in the Cold Forging Process for Long Hex Flange Nuts" *Materials* 8, no. 10: 6640-6657.
https://doi.org/10.3390/ma8105328