# Thin-Wall Machining of Light Alloys: A Review of Models and Industrial Approaches

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## Abstract

**:**

## 1. Introduction

## 2. Type of Parts and Associated Problems

#### 2.1. Thin-Wall Parts: Characteristics and Types

#### 2.2. Dynamic and Static Problems

## 3. Analytic Models

#### 3.1. Cutting Force Prediction

_{0}) and maximum (z

_{1}) values applied on the instant angle (Figure 4b).

#### 3.2. Dynamic Model

- Temperature and other factors related to the machining process do not affect the behavior of the tool and the workpiece during the cutting operation.
- The only force considered is the cutting force, and deformation is only elastic.

**M**

_{s}), damping (

**C**

_{s}) and stiffness (

**K**

_{s}) are matrices with dimension $3{n}_{s}\times 3{n}_{s}$ on the workpiece (w) and the tool (t). The vibration vector ${\mathit{Q}}_{s}\left(t\right)$ is defined by the modal displacement (${\mathbf{\Gamma}}_{s}\left(t\right)$) and the mass normalized mode (

**U**). ${\mathit{\zeta}}_{s}$ is the modal damping ratio matrix and ${\mathit{\omega}}_{s}$ is the diagonal FRF matrix, both matrices having the dimension of ${m}_{s}\times {m}_{s}$.

_{s}_{s}(t) correspond to the cutting forces and is calculated following the force prediction section but, in this case, chip thickness (${h}_{i,j}$) and axial immersion angle ($\mathsf{\kappa}$) should consider the dynamic interaction:

_{j−1}is the time interval between two following flutes. V is the tool axis vector and P is the relative position vector to the instant $t$, both of which depend on the instant relative position between the part and the tool (Figure 6).

#### 3.3. Deflection Model

## 4. Computational Solutions

^{®}[148] or DEFORMTM [77]. They are used as inputs for the initial conditions of the workpiece. Then, self-developed or commercial software such as ANSYSTM [20,24,34,71] or ABAQUS [29,35,53,58,71,150] are used to obtain the FRF of the system, the dynamic behavior or its deflection.

#### 4.1. Vibration Prediction

#### 4.1.1. Chatter

#### 4.1.2. Amplification

#### 4.2. Dimensional Error Prediction

## 5. Industrial Approach

#### 5.1. Parameters Selection

#### 5.1.1. Database Models

_{z}had an influence on surface roughness, but only when down milling strategies were used. Borojevic et al. [92] optimized the machining time based on the machining strategy and the cutting parameters. Bolar et al. [93] and Jiang et al. [7] detected three different areas of study for roughness when flank milling of thin-wall components was performed. The first area (initial engagement) and the last one (final disengagement) are more unstable than the center of the part. Both surface and residual stresses are increased due to the forced vibrations produced by the tool on those two areas. Yan et al. [66] implemented an experimental method that allow setting the maximum depth of cut as a function of the cutting force, thus its effect does not produce any displacement on the part.

#### 5.1.2. Virtual Twins

#### 5.2. Adaptive Control

#### 5.2.1. Monitoring

#### 5.2.2. Measurements

#### 5.3. Fixtures, Workholdings and Stiffening Devices

#### 5.3.1. Fixtures and Workholdings

^{®}[128]. The workholding is specially designed to compensate the cutting energy all over the part. The position is defined using simulations, and the supports are applied at the most flexible positions. The system usually has embedded sensors, thus it is possible to change the behavior of the workholding, depending on the operation and to register historical data to feed new databases.

#### 5.3.2. Active Damping Actuators

_{z}, S, and a

_{p}), achieving a reduction of the machining vibrations of up to 84%.

#### 5.3.3. Stiffening Devices

^{®}ISD112) was used to place the tuned damper blocks. The viscoelastic tape thickness, its weight and the position of the tuned mass were optimized using FEM. The dynamic response of the workpiece with and without dampers was simulated and the predicted responses were validated by impact hammer tests. The efficacy of dampers blocks was evaluated by machining undamped casing and damped casing and their use provided a significant reduction in vibration in terms of root mean square error. The author affirmed this solution can be rapidly adapted to other workpiece geometries using the FEM model they developed.

## 6. Conclusions

- Virtual twins development integrates CAM and simulation to predict the machining behavior, the future real position of the surface to cut, and improve the machining efficiency by selecting the proper cutting parameter, toolpath, or prediction [152].
- Adaptive control, which is used in production to improve the part quality and to feed the database models, detects the process instabilities or deformation through signal analysis and changing on-line the machining parameters.
- Fixture systems design and new approaches try to include adaptive control on the workholding or the stiffening devices to increase the product efficiency, allowing to use more aggressive cutting parameters.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 2.**Examples of thin-wall parts: (

**a**) frame; (

**b**) rib; (

**c**) impeller; (

**d**) blisk; (

**e**) sample parts; (

**f**) bulkhead; and (

**g**) fuselage skin.

**Figure 5.**Flexibility of the system categorized as: (

**a**) rigid cutter–flexible workpiece system; (

**b**) rigid workpiece–flexible cutter system; and (

**c**) double flexible system.

**Figure 8.**Schematic SLD presenting stable and unstable areas and the possible improvement of the SLD limit curve.

**Figure 9.**Average deflection obtained for (

**a**) the first mode, (

**b**) the second mode and (

**c**) the third mode of a pocket structure [55].

**Figure 10.**Deformation of a blade during one period rotation of the spindle [78].

**Figure 11.**Deformation of a thin-wall part: (

**a**) not considering the residual stress; and (

**b**) following a quasi-symmetric machining reducing the residual stress [88].

**Figure 12.**Part deformation obtained with a traditional machining sequence (upper) and with an adaptive machining system selecting an optimized sequence (lower) [106].

Models | ||

Thin-wall dynamic problems | Chatter and self-exciting aspects | [14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42] |

Resonance and amplification | [33,41,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60] | |

Thin-wall deformation | Quasi-static models | [36,49,61,62,63,64,65,66,67,68,69,70] |

FEM modeling | [51,61,62,65,71,72,73,74,75,76,77,78] | |

Residual Stresses | [79,80,81,82,83,84,85,86,87,88] | |

Industrial Approach | ||

Parameter selection | Statistic and machine learning models | [62,89,90,91,92,93,94,95] |

Virtual Twins | [66,78,96,97,98,99] | |

Active solutions | Monitoring | [32,41,95,100,101,102,103,104,105,106,107,108,109,110,111] |

Measurements | [106,112,113,114,115,116] | |

Fixture and clamping | Fixtures | [83,116,117,118,119,120,121,122,123,124,125,126] |

Workholding | [19,75,127,128,129,130,131] | |

Active damping actuators | [132,133,134,135] | |

Stiffening devices | [136,137,138,139,140] |

**Table 2.**Cutting parameters effect on residual stress, forces, deflection and roughness. S, Spindle Speed; f, feed rate; Ap, depth of cut; NP, Nº of paths; MRR, Material Removal Rate; RS, Residual Stress; F, Forces; Def, Deflection; Rg, Surface Roughness.

RS | F | Def | Rg | |
---|---|---|---|---|

S | ||||

f | / _{1} | |||

Ap | ||||

NP | ||||

MRR |

^{1}Down milling strategies increase the deflection of the part while up milling decrease it.

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**MDPI and ACS Style**

Del Sol, I.; Rivero, A.; López de Lacalle, L.N.; Gamez, A.J.
Thin-Wall Machining of Light Alloys: A Review of Models and Industrial Approaches. *Materials* **2019**, *12*, 2012.
https://doi.org/10.3390/ma12122012

**AMA Style**

Del Sol I, Rivero A, López de Lacalle LN, Gamez AJ.
Thin-Wall Machining of Light Alloys: A Review of Models and Industrial Approaches. *Materials*. 2019; 12(12):2012.
https://doi.org/10.3390/ma12122012

**Chicago/Turabian Style**

Del Sol, Irene, Asuncion Rivero, Luis Norberto López de Lacalle, and Antonio Juan Gamez.
2019. "Thin-Wall Machining of Light Alloys: A Review of Models and Industrial Approaches" *Materials* 12, no. 12: 2012.
https://doi.org/10.3390/ma12122012