# Evaluation and Optimization of a Hybrid Manufacturing Process Combining Wire Arc Additive Manufacturing with Milling for the Fabrication of Stiffened Panels

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## Abstract

**:**

## Featured Application

**This paper proposes a hybrid manufacturing process combining wire arc additive manufacturing with milling, which provides a cost-effective and efficient way to fabricate stiffened panels that have wide applications in aviation, aerospace, and automotive industries, etc.**

## Abstract

## 1. Introduction

## 2. System Description

## 3. Evaluation of Surface Quality, Material Utilization, and Efficiency

#### 3.1. Evaluation of Surface Quality

_{FR}), travel speed (T

_{S}), and welding voltage (W

_{V}). The key milling parameters affecting the surface quality in Step 2 and Step 3 mainly include spindle speed (S

_{S}), tool-feed rate (T

_{FR}), and cutting depth (C

_{D}). Only the side surface’s quality in Step 3 is concerned in this study because the top surface will be covered by subsequent layers. Unlike other independent milling processes, the cutting depth in Step 3 is directly determined by the bead geometry produced in Step 1, as shown in Figure 3d. The larger the bead width (B

_{W}) than the target width (T

_{W}), the larger the cutting depth. The bead width is determined by the deposition parameters and the target width is a constant value for a specific stiffened panel. Therefore, the surface quality (represented by surface roughness, R

_{a}) achieved by HWMP is a result of both the deposition and the milling parameters, as seen in Figure 4.

#### 3.1.1. Identifying Predominant Factors Affecting the Response and Determining Their Limits

#### 3.1.2. Generating Experimental Design Matrix and Conducting the Experiments

#### 3.1.3. Developing and Validating the Regression Models

_{W}on W

_{FR}, T

_{S}and W

_{V}is obtained with the aid of the software Design-Expert (Version 6.0, State-Ease, Minneapolis, MN, USA, 2005) as follows:

_{0.05}(9, 10) = 3.179, indicating that this model is significant at a 95% confidence level, whereas F-value of lack of fit is 1.52, lower than F

_{0.05}(5, 5) = 5.05, indicating that lack of fit is not significant. Moreover, the coefficient of determination R

^{2}is very close to 1, i.e., R

^{2}= 0.9679, which means that the model clarifies 96.79% of all deviations. Thus, we can conclude that this obtained regression model is credible and accurate. At a 95% confidence level, only p-values of T

_{S,}W

_{FR}, and T

_{S}

^{2}term are all lower than 0.05, which indicate that only their effects on B

_{W}are significant. After omitting the insignificant terms, this regression model is simplified to:

_{a}on S

_{S}, T

_{FR}and C

_{D}is also obtained as follows:

_{S}, T

_{FR}, C

_{D}, S

_{S}

^{2}and T

_{FR}

^{2}have significant effects on R

_{a}at 95% confidence level and as a result the simplified regression model is

#### 3.1.4. Developing Surface Roughness Model

_{D}in Equation (4) with Equation (5) and combining Equation (2), the final surface roughness model is obtained:

#### 3.2. Evaluation of Material Utilization

_{U}) is defined as the ratio of the final part’s mass (m

_{part}) to the raw material’s mass (m

_{raw_material}) as follows:

_{plate}) and the stiffeners (m

_{stiffener}), whereas the raw material’s mass is the sum of the masses of the plate and the beads (m

_{bead}). From Figure 3d, we also know that m

_{bead}/m

_{stiffener}is approximately equal to B

_{W}/T

_{W}, neglecting the removed mass in Step 2. Therefore, Equation (7) can be converted to:

#### 3.3. Evaluation of Efficiency

_{C}), it is related to two main process parameters, i.e., travel speed and tool-feed rate, the former determining the deposition time (T

_{deposition}), whereas the latter determining the milling time (T

_{milling}). Additionally, the cooling time (T

_{cooling}) for the part to cool down to room temperature before next deposition or milling and the tool-changing time (T

_{tool-changing}) for switching the welding torch and the milling tool should also be considered. Therefore, the construction time that the deposition and the milling processes alternate once (i.e., N = 6) can be calculated as follows:

#### 3.4. Effects of Process Parameters on the Performances

_{S}> W

_{FR}> T

_{FR}> T

_{S}.

## 4. Parameter Optimization

_{a}(W

_{FR}, T

_{S}, Ss, T

_{FR}), min 1/M

_{U}(W

_{FR}, T

_{S}) and min T

_{C}(T

_{S}, T

_{FR})

_{FR}≤ 5.1 m/min

_{S}≤ 0.6 m/min

_{S}≤ 8000 rpm

_{FR}≤ 5 mm/s

_{a}, min 1/M

_{U}, and min T

_{C}) to a single objective (i.e., min (w

_{1}R

_{a}+ w

_{2}/ M

_{U}+ w

_{3}T

_{C})) [31]. w

_{1}, w

_{2}, and w

_{3}are the weight coefficients and w

_{1}+ w

_{2}+ w

_{3}= 1. These parameters should be normalized before being converted as follows:

_{1}is set to 0.6, w

_{2}is set to 0.2 and w

_{3}is set to 0.2 in this study.

_{plate}/m

_{stiffener}, etc., are the same as those in Equation (10). From Table 4, we can observe that for any target width, a high travel speed, and a high spindle speed are recommended because the surface roughness is a decreasing function of the two process parameters according to Figure 6a. Higher travel speed also results in both higher material utilization and efficiency according to Figure 6b,c. Though a low tool-feed rate is beneficial for achieving a low surface roughness according to Figure 6a, it also increases the construction time according to Figure 6c. Therefore, a moderate tool-feed rate is recommended to obtain a good balance between surface roughness and construction time. With regard to wire-feed rate, different target widths correspond to different wire-feed rates, which can be explained through the constraint function (Equation (12)), i.e., larger target width required larger bead width and therefore larger wire-feed rate. In all, a high travel speed, a target width dependent wire-feed rate, a high spindle speed and a moderate tool-feed rate are required to maximize the performances of HWMP. It is interesting to find that the optimized values for the surface roughness, the material utilization and the construction time are basically the same regardless of the target width. The surface roughness remains around 1.3 μm and the material utilization ranges from 96% to 98%.

## 5. Case Study

_{FR}= 3.8 m/min, T

_{S}= 0.6 m/min, S

_{S}= 6694 rpm and T

_{FR}= 3.1 mm/s according to Table 4. Furthermore, note that the height of each stiffener is 16 mm, which means that the deposition and the milling processes need to alternate twice here.

_{finishing}= 14.88 mm

^{3}/s. That is to say, the achieved surface quality here is assumed to be the same as that in HWMP. The MRR for the roughing stage, i.e., MRR

_{roughing}, is assumed to be 10 times that of MRR

_{finishing}, i.e., 148.8 mm

^{3}/s. The machining time for each stage can be calculated by dividing the volume to be removed by the corresponding MRR. The tool-changing time is not considered here.

## 6. Conclusions and Future Work

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

- Gao, W.; Zhang, Y.; Ramanujan, D.; Ramani, K.; Williams, C.B. The status, challenges, and future of additive manufacturing in engineering. Comput. Aided Des.
**2015**, 69, 65–89. [Google Scholar] [CrossRef] - Singh, S.; Ramakrishna, S.; Singh, R. Material issues in additive manufacturing: A review. J. Manuf. Process.
**2017**, 25, 185–200. [Google Scholar] [CrossRef] - Frazier, W.E. Metal Additive Manufacturing: A Review. J. Mater. Eng. Perform.
**2014**, 23, 1917–1928. [Google Scholar] [CrossRef] - Ding, D.; Pan, Z.; Cuiuri, D.; Li, H. Wire-feed additive manufacturing of metal components: Technologies, developments and future interests. Int. J. Adv. Manuf. Technol.
**2015**, 81, 465–481. [Google Scholar] [CrossRef] - Kumbhar, N.N.; Mulay, A.V. Post processing methods used to improve surface finish of products which are manufactured by additive manufacturing technologies: A review. J. Inst. Eng.
**2016**, 1–7. [Google Scholar] [CrossRef] - Flynn, J.M.; Shokrani, A.; Newman, S.T.; Dhokia, V. Hybrid additive and subtractive machine tools—Research and industrial developments. Int. J. Mach. Tools Manuf.
**2016**, 101, 79–101. [Google Scholar] [CrossRef][Green Version] - Zhu, Z.; Dhokia, V.; Nassehi, A.; Newman, S.T. A review of hybrid manufacturing processes-state of the art and future perspectives. Int. J. Comput. Integr. Manuf.
**2013**, 26, 596–615. [Google Scholar] [CrossRef] - Manogharan, G.; Wysk, R.A.; Harrysson, O.L.A. Additive manufacturing–integrated hybrid manufacturing and subtractive processes: Economic model and analysis. Int. J. Comput. Integr. Manuf.
**2016**, 29, 473–488. [Google Scholar] [CrossRef] - Kapil, S.; Legesse, F.; Kulkarni, P.; Joshi, P.; Desai, A.; Karunakaran, K.P. Hybrid-layered manufacturing using tungsten inert gas cladding. Prog. Addit. Manuf.
**2016**, 1, 79–91. [Google Scholar] [CrossRef] - Xiong, X.; Zhang, H.; Wang, G.; Wang, G. Hybrid plasma deposition and milling for an aeroengine double helix integral impeller made of superalloy. Robot. Comput. Integr. Manuf.
**2010**, 26, 291–295. [Google Scholar] [CrossRef] - Song, Y.; Park, S.; Choi, D.; Jee, H. 3D welding and milling: Part I—A direct approach for freeform fabrication of metallic prototypes. Int. J. Mach. Tools Manuf.
**2005**, 45, 1057–1062. [Google Scholar] [CrossRef] - Zhu, Z.; Dhokia, V.; Newman, S.T.; Nassehi, A. Application of a hybrid process for high precision manufacture of difficult to machine prismatic parts. Int. J. Adv. Manuf. Technol.
**2014**, 74, 1115–1132. [Google Scholar] [CrossRef][Green Version] - Pan, Z.; Ding, D.; Wu, B.; Cuiuri, D.; Li, H.; Norrish, J. Arc Welding Processes for Additive Manufacturing: A Review. In Transactions on Intelligent Welding Manufacturing; Chen, S., Zhang, Y., Feng, Z., Eds.; Springer: Singapore, 2017; ISBN 978-981-10-5355-9. [Google Scholar]
- Wu, Q.; Ma, Z.; Chen, G.; Liu, C.; Ma, D.; Ma, S. Obtaining fine microstructure and unsupported overhangs by low heat input pulse arc additive manufacturing. J. Manuf. Process.
**2017**, 27, 198–206. [Google Scholar] [CrossRef] - Ding, D.; Pan, Z.; Cuiuri, D.; Li, H. A tool-path generation strategy for wire and arc additive manufacturing. Int. J. Adv. Manuf. Technol.
**2014**, 73, 173–183. [Google Scholar] [CrossRef] - Cong, B.; Qi, Z.; Qi, B.; Sun, H.; Zhao, G.; Ding, J. A Comparative study of additively manufactured thin wall and block structure with Al-6.3%Cu alloy Using cold metal transfer process. Appl. Sci.
**2017**, 7, 275. [Google Scholar] [CrossRef] - Wu, B.; Ding, D.; Pan, Z.; Cuiuri, D.; Li, H.; Han, J.; Fei, Z. Effects of heat accumulation on the arc characteristics and metal transfer behavior in wire arc additive manufacturing of Ti6Al4V. J. Mater. Process. Technol.
**2017**, 250, 304–312. [Google Scholar] [CrossRef] - Xu, X.; Ding, J.; Ganguly, S.; Diao, C.; Williams, S. Oxide accumulation effects on wire + arc layer-by-layer additive manufacture process. J. Mater. Process. Technol.
**2017**, 252, 739–750. [Google Scholar] [CrossRef] - Williams, S.W.; Martina, F.; Addison, A.C.; Ding, J.; Pardal, G.; Colegrove, P. Wire + arc additive manufacturing. Mater. Sci. Technol.
**2016**, 7, 641–647. [Google Scholar] [CrossRef] - Moreira, P.M.G.P.; Richtertrummer, V.; Castro, P.M.S.T.D. Lightweight stiffened panels fabricated using emerging fabrication technologies: Fatigue behaviour. Adv. Struct. Mater.
**2010**, 8, 151–172. [Google Scholar] - Öktem, H. An integrated study of surface roughness for modelling and optimization of cutting parameters during end milling operation. Int. J. Adv. Manuf. Technol.
**2009**, 43, 852–861. [Google Scholar] [CrossRef] - Jin, Y.; Du, J.; He, J. Optimization of process planning for reducing material consumption in additive manufacturing. J. Manuf. Syst.
**2017**, 44, 65–78. [Google Scholar] [CrossRef] - Zhao, C.; Li, J. The manufacturing technology of integral panel on spacecraft. Aerosp. Manuf. Technol.
**2006**, 4, 44–48. [Google Scholar] - Sproesser, G.; Chang, Y.J.; Pittner, A.; Finkbeiner, M.; Rethmeier, M. Environmental energy efficiency of single wire and tandem gas metal arc welding. Weld. World
**2017**, 61, 733–743. [Google Scholar] [CrossRef] - Cukor, G.; Jurkovi, Z.; Sekuli, M. Rotatable central composite design of experiments versus Taguchi method in the optimization of turning. Metalurgija
**2011**, 50, 17–20. [Google Scholar] - Palanikumar, K. Modeling and analysis for surface roughness in machining glass fibre reinforced plastics using response surface methodology. Mater. Des.
**2007**, 28, 2611–2618. [Google Scholar] [CrossRef] - Ding, T.; Zhang, S.; Wang, Y. Empirical models and optimal cutting parameters for cutting forces and surface roughness in hard milling of AISI H13 steel. Int. J. Adv. Manuf. Technol.
**2010**, 51, 45–55. [Google Scholar] [CrossRef] - Wang, Z.H.; Yuan, J.T.; Liu, T.T.; Huang, J.; Qiao, L. Study on surface roughness in high-speed milling of AlMn1Cu using factorial design and partial least square regression. Int. J. Adv. Manuf. Technol.
**2015**, 76, 1783–1792. [Google Scholar] [CrossRef] - Palanisamy, P.; Rajendran, I.; Shanmugasundaram, S. Optimization of machining parameters using genetic algorithm and experimental validation for end-milling operations. Int. J. Adv. Manuf. Technol.
**2007**, 32, 644–655. [Google Scholar] [CrossRef] - Zain, A.M.; Haron, H.; Sharif, S. Application of GA to optimize cutting conditions for minimizing surface roughness in end milling machining process. Expert Syst. Appl.
**2010**, 37, 4650–4659. [Google Scholar] [CrossRef] - Marler, R.T.; Arora, J.S. The weighted sum method for multi-objective optimization: New insights. Struct. Multidiscip. Optim.
**2010**, 41, 853–862. [Google Scholar] [CrossRef] - Xiong, J.; Zhang, G.; Zhang, W. Forming appearance analysis in multi-layer single-pass GMAW-based additive manufacturing. Int. J. Adv. Manuf. Technol.
**2015**, 80, 1767–1776. [Google Scholar] [CrossRef]

**Figure 1.**Processing technologies for stiffened panels. (

**a**,

**b**) Applications of stiffened panels [23]; (

**c**) riveting; (

**d**) welding; (

**e**) machining; and (

**f**) HWMP.

**Figure 3.**(

**a**–

**c**) Work principle of HWMP for fabricating stiffened panels; and (

**d**) the relation between cutting depth and bead width.

**Figure 6.**(

**a**) The effects of single process parameter on surface roughness; (

**b**) the effects of single process parameter on material utilization; and (

**c**) the effects of single process parameter on construction time.

Symbol | Factor | Unit | Level | ||||
---|---|---|---|---|---|---|---|

−1.68 | −1 | 0 | 1 | 1.68 | |||

Regression model 1 (Response: bead width) | |||||||

W_{FR} | Wire-feed rate | m/min | 3.4 | 3.7 | 4.3 | 4.8 | 5.1 |

T_{S} | Travel speed | m/min | 0.35 | 0.4 | 0.48 | 0.55 | 0.6 |

W_{V} | Welding voltage | V | 16.6 | 17.3 | 18.4 | 19.5 | 20.2 |

Regression model 2 (Response: surface roughness) | |||||||

S_{S} | Spindle speed | rpm | 1000 | 2400 | 4500 | 6600 | 8000 |

T_{FR} | Tool-feed rate | mm/s | 1 | 1.8 | 3 | 4.2 | 5 |

C_{D} | Cutting depth | mm | 1 | 1.4 | 2 | 2.6 | 3 |

Regression Model 1 | Regression Model 2 | ||||
---|---|---|---|---|---|

Exp. No. | Coding (W _{FR} T_{S} W_{V}) | Bead Width (mm) | Exp. No. | Coding (S _{S} T_{FR} C_{D}) | Roughness (μm) |

1 | (−1 −1 −1) | 9 | 1 | (−1 −1 −1) | 1.74 |

2 | (1 −1 −1) | 11.9 | 2 | (1 −1 −1) | 1.41 |

3 | (−1 1 −1) | 8.4 | 3 | (−1 1 −1) | 1.99 |

4 | (1 1 −1) | 10.8 | 4 | (1 1 −1) | 1.47 |

5 | (−1 −1 1) | 9.5 | 5 | (−1 −1 1) | 1.97 |

6 | (1 −1 1) | 11.7 | 6 | (1 −1 1) | 1.58 |

7 | (−1 1 1) | 8.3 | 7 | (−1 1 1) | 2.15 |

8 | (1 1 1) | 10 | 8 | (1 1 1) | 1.81 |

9 | (−1.682 0 0) | 8 | 9 | (−1.682 0 0) | 2.41 |

10 | (1.682 0 0) | 12.5 | 10 | (1.682 0 0) | 1.52 |

11 | (0 −1.682 0) | 11.5 | 11 | (0 −1.682 0) | 1.79 |

12 | (0 1.682 0) | 9.5 | 12 | (0 1.682 0) | 1.86 |

13 | (0 0 −1.682) | 9.9 | 13 | (0 0 −1.682) | 1.68 |

14 | (0 0 1.682) | 10.1 | 14 | (0 0 1.682) | 1.78 |

15 | (0 0 0) | 9.6 | 15 | (0 0 0) | 1.65 |

16 | (0 0 0) | 9.5 | 16 | (0 0 0) | 1.64 |

17 | (0 0 0) | 9.6 | 17 | (0 0 0) | 1.67 |

18 | (0 0 0) | 10 | 18 | (0 0 0) | 1.53 |

19 | (0 0 0) | 9.5 | 19 | (0 0 0) | 1.52 |

20 | (0 0 0) | 10.1 | 20 | (0 0 0) | 1.59 |

Regression Model 1 | Regression Model 2 | ||||
---|---|---|---|---|---|

Source | F-Value | p-Value | Source | F-Value | p-Value |

A-W_{FR} | 234.16 | <0.0001 | A-S_{S} | 66.73 | <0.0001 |

B-T_{S} | 52.82 | <0.0001 | B-T_{FR} | 5.11 | 0.0473 |

C-W_{V} | 0.058 | 0.8147 | C-C_{D} | 8.20 | 0.0169 |

AB | 1.42 | 0.2607 | AB | 0.23 | 0.6390 |

AC | 2.79 | 0.1260 | AC | 0.16 | 0.7020 |

BC | 2.05 | 0.1830 | BC | 0.080 | 0.7827 |

A^{2} | 2.30 | 0.1604 | A^{2} | 16.72 | 0.0022 |

B^{2} | 7.01 | 0.0244 | B^{2} | 5.13 | 0.0469 |

C^{2} | 0.15 | 0.7086 | C^{2} | 1.04 | 0.3318 |

Model | 33.54 | <0.0001 | Model | 11.21 | 0.0004 |

Lack of Fit | 1.52 | 0.3275 | Lack of Fit | 4.33 | 0.0669 |

R^{2} | 0.9679 | R^{2} | 0.9099 |

T_{W} (mm) | W_{FR} (m/min) | T_{S} (m/min) | S_{S} (rpm) | T_{FR} (mm/s) | R_{a} (μm) | M_{U} | T_{C} (min) |
---|---|---|---|---|---|---|---|

6 | 3.5 | 0.6 | 6694 | 3.1 | 1.31 | 96% | 48.35 |

7 | 3.8 | 0.6 | 6694 | 3.1 | 1.29 | 97% | 48.35 |

8 | 4.2 | 0.6 | 6695 | 3.1 | 1.29 | 97% | 48.35 |

9 | 4.6 | 0.6 | 6695 | 3.1 | 1.29 | 98% | 48.35 |

10 | 5.0 | 0.6 | 6695 | 3.1 | 1.29 | 98% | 48.35 |

Traditional Machining | HWMP | ||
---|---|---|---|

Thick plate’s mass | 5.35 kg | Thin plate’s mass | 1.46 kg |

Metal wire’s mass | 0.55 kg | ||

Final part’s mass | 1.82 kg | Final part’s mass | 1.82 kg |

Material utilization | 34% | Material utilization | 91% |

Deposition time | 24 min | ||

Roughing time | 144 min | Milling time | 38.7 min |

Finishing time | 22 min | ||

Cooling, etc. | 39.3 min | ||

Construction time | 166 min | Construction time | 102 min |

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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**MDPI and ACS Style**

Li, F.; Chen, S.; Shi, J.; Tian, H.; Zhao, Y. Evaluation and Optimization of a Hybrid Manufacturing Process Combining Wire Arc Additive Manufacturing with Milling for the Fabrication of Stiffened Panels. *Appl. Sci.* **2017**, *7*, 1233.
https://doi.org/10.3390/app7121233

**AMA Style**

Li F, Chen S, Shi J, Tian H, Zhao Y. Evaluation and Optimization of a Hybrid Manufacturing Process Combining Wire Arc Additive Manufacturing with Milling for the Fabrication of Stiffened Panels. *Applied Sciences*. 2017; 7(12):1233.
https://doi.org/10.3390/app7121233

**Chicago/Turabian Style**

Li, Fang, Shujun Chen, Junbiao Shi, Hongyu Tian, and Yun Zhao. 2017. "Evaluation and Optimization of a Hybrid Manufacturing Process Combining Wire Arc Additive Manufacturing with Milling for the Fabrication of Stiffened Panels" *Applied Sciences* 7, no. 12: 1233.
https://doi.org/10.3390/app7121233