Correlation of Process Parameters and Porosity in Laser Welding of 7A52 Aluminum Alloy using Response Surface Methodology

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Procedia Manufacturing 37 (2019) 294–298 Procedia Manufacturing 00 (2017) 000–000 www.elsevier.com/locate/procedia

9th International Conference on Physical and Numerical Simulation of Materials Processing (ICPNS’2019) 9th International Conference on Physical and Numerical Simulation of Materials Processing (ICPNS’2019)

Correlation of Process Parameters and Porosity in Laser Welding of Correlation of Process Parameters and Porosity in Laser Welding of 7A52 Aluminum using Response Surface Methodology Manufacturing EngineeringAlloy Society International Conference 2017, MESIC 2017, 28-30 June 7A52 Aluminum Alloy using Response Surface Methodology 2017, Vigo (Pontevedra), Spain a,b b a Chaoqun Songa,b a,b, Shiyun Donga,b, Peng He*, Shixing Yanb, Xuan Zhaoa Chaoqun Song , Shiyun Dong , Peng He*, Shixing Yan , Xuan Zhao

Key Laboratory Welding and Joining, Harbin Institutein of Technology, Harbin 4.0: 150001, China CostingState models forof Advanced capacity optimization Industry Trade-off State KeyKey Laboratory of Advanced Welding andAcademy Joining, of Harbin Institute of Technology, National Laboratory for Remanufacturing, Armored Forces Engineering,Harbin Beijing150001, 100072,China China National Key Laboratory for Remanufacturing, Academy Forces Engineering, Beijing 100072, China between used capacity andof Armored operational efficiency a

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A. Santana , P. Afonso , A. Zanin , R. Wernke Abstract Abstract a University of Minho, 4800-058 Guimarães, Portugal The article presents an experimental investigation of the effect of process parameters b Unochapecó, 89809-000 Chapecó, SC, Brazil(i.e. laser power, welding speed and defocus The articleonpresents of the effect parameters (i.e. laser power, welding speeddesign and defocus distance) porosityaninexperimental laser weldinginvestigation of 7A52 aluminum alloy.ofAprocess three-factor, three-level factorial Box–Behnken (BBD) distance) on surface porositymethodology in laser welding of 7A52 aluminum alloy. Athe three-factor, three-level Box–Behnken design (BBD) of response (RSM) was used to complete design matrix with thefactorial objective of optimizing the process of response Multiple surface methodology (RSM)were wasestablished used to complete the the design matrix between with the the objective of process optimizing the process parameters. regression models to predict correlation selected parameters and parameters. regression models wereusing established the (ANOVA). correlation The between theshowed selectedthat process and porosity, andMultiple then were tested for adequacy analysistoofpredict variance results macroparameters pores in laser Abstract porosity, and then tested for adequacy analysis susceptibility of variance (ANOVA). The results showed that macro in laser welding seam werewere mainly keyhole induced,using and porosity was mostly dominated by welding speed.pores The optimal welding seamconcept mainly keyhole induced, and power porosity susceptibility mostly byand welding speed. The optimal combination ofwere process is4.0", the laser ofprocesses 3.5kW, welding ofdominated 10.0mm/s, the defocus distance of Under the of parameters "Industry production willwas bespeed pushed to be increasingly interconnected, combination of process parameters is the laser power of 3.5kW, welding speed of 10.0mm/s, and the defocus distance of +4.0mm. Under the optimal process parameters, superior weld seams without macro pore were produced. The predicted porosity information based on a real time basis and, necessarily, much more efficient. In this context, capacity optimization +4.0mm. Under the optimal process parameters,tosuperior weld seams without macro pore were produced. The experiments. predicted porosity area fraction with RSM models was confirmed be in good agreement with the measured values of validation goes beyond the traditional aim of capacity maximization, contributing also for organization’s profitability and value. area fraction with RSM models was confirmed to be in good agreement with the measured values of validation experiments. Indeed, lean management and continuous improvement approaches suggest capacity optimization instead of © 2019 The Authors. Published by Elsevier B.V. maximization. The Published study capacity and costing models is an important research topic that deserves © 2019 2019 The Authors. by Elsevier B.V. © The Authors. Published by Elsevier B.V. This is an open access articleof under the CCoptimization BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) contributions from both the practical and theoretical perspectives. paper presents and on discusses mathematical This is an open access article underofthe BY-NC-ND license of (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility theCC scientific committee the 9th This International Conference Physicala and Numerical Peer-review under responsibility ofbased the scientific committee of the 9th International ConferenceAongeneric Physical and Numerical model for capacity management on different costing models (ABC and TDABC). model has been Peer-review under responsibility of the scientific committee of the 9th International Conference on Physical and Numerical Simulation on Materials Processing Simulation on Materials Processing Simulation on Processing developed andMaterials it was used to analyze idle capacity and to design strategies towards the maximization of organization’s a

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Keywords: laser welding; response methodology;vs process parameter;efficiency 7A52 aluminum alloy; porosityand it is shown that capacity value. The trade-off capacitysurface maximization operational is highlighted Keywords: laser might welding;hide response surface methodology; process parameter; 7A52 aluminum alloy; porosity optimization operational inefficiency.

© 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference 2017. Keywords: Cost Models; ABC; TDABC; Capacity Management; Idle Capacity; Operational Efficiency

Introduction *1.Corresponding author. Tel.: +86-0451-86402787. * E-mail Corresponding Tel.: +86-0451-86402787. address:author. [email protected] E-mail address: [email protected] The cost of idle capacity is a fundamental information for companies and their management of extreme importance 2351-9789 2019 The Authors. Published by Elsevier in modern©production systems. In general, it isB.V. defined as unused capacity or production potential and can be measured 2351-9789 © 2019 Thearticle Authors. Published by Elsevier B.V. This is an open access the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) in several ways: tons ofunder production, available hours of manufacturing, etc. The management of the idle capacity This is an open access article under CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 9th International Conference on Physical and Numerical Simulation on * Paulo Afonso. Tel.: +351 253 510 761; fax: +351 253 604 Peer-review under responsibility of the scientific committee of741 the 9th International Conference on Physical and Numerical Simulation on Materials Processing E-mailProcessing address: [email protected] Materials 2351-9789 Published by Elsevier B.V. B.V. 2351-9789 ©©2017 2019The TheAuthors. Authors. Published by Elsevier Peer-review underaccess responsibility of the scientific committee oflicense the Manufacturing Engineering Society International Conference 2017. This is an open article under the CC BY-NC-ND (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 9th International Conference on Physical and Numerical Simulation on Materials Processing 10.1016/j.promfg.2019.12.050

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1. Introduction 7A52 is a heat treatable Al-Zn-Mg alloy which is widely used in light armored vehicle body due to its light weight, good corrosion resistance, high strength and toughness, etc [1]. The plates of vehicle body are mainly jointed by welding. Laser welding is a promising technology to weld 7A52 aluminum alloy because of the low heat input, narrow heat affected zone, high welding speed, and the ease of automation [2,3]. However, porosity is one of the most serious problem in laser welding of aluminum alloy, resulting in a significant decrease in weld strength and mechanical properties [4]. The pores can be generally divided into microuniform hydrogen pores and macro-irregular keyhole induced pores caused by the collapse of the keyhole [5-7]. The influence of various process parameters on weld bead profile, mechanical properties and porosity defects in laser welding of aluminum alloys has been reported, including laser power, welding speed, defocus amount, and shielding gas [8-11]. Optimization of process parameters that would produce an excellent weld joint is the main challenge for laser welding of aluminum alloys. However, the accurate relationship between process parameters and porosity sensitivity has not been established to predict porosity for laser welding of 7A52 aluminum alloy. In the presented study, the effects of laser process parameters (laser power, welding speed and defocus distance) on porosity susceptibility of 7A52 aluminum alloy joints has been investigated through experiments, and RSM was employed to develop mathematical models to optimize the process parameters. 2. Experimental procedure The material used in this research is 7A52 aluminum alloy with the mass fraction was 2.58%Mg, 4.6%Zn, 0.35%Mn, 0.2%Cr, 0.14%Ti, 0.12%Zr, 0.1%Si, 0.086%Cu, 0.15%Fe, and the rest was Al. The experimental set-up for the laser welding consisted of an IPG YLR-4000 fiber laser with the wavelength of 1060 nm and some auxiliary equipment, such as laser welding head, six-axis robot, and shielding gas nozzle. The laser welding experiment was carried out on 10mm thick 7A52 plates under conditions of argon shield with a constant rate of 15L/min. The laser power, welding speed and defocus distance were selected as parameters, which are reported to be the main process parameters of laser welding, and their coded levers with Box-Behnken design（BBD） are presented in Table 1. Table 1. Process parameters and their coded levels. Parameters

Code

-1

0

1

Laser power (kw)

A

3.25

3.50

3.75

Welding speed (mm/s)

B

5

10

15

Defocus distance (mm)

C

0

+4

+8

In order to analyze porosity in the joint, the laser welded samples were sectioned transverse to the welding direction. The concept of porosity area fraction (f), area ratio of total pores and the whole weld beam, was adopted as the process response to evaluate the susceptibility of pores [12]. A typical laser welding joint with keyhole induced pores is shown in Fig.1. The weld seam zone and pore zone were extracted separately, and their area ratio f was calculated with the help of digital measuring software. The design matrix with the measured response values is given in Table 2.

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Fig. 1. Typical laser welding joint (a) actual morphology; (b) extracted image. Table 2. Box-Behnken design matrix and experimental results Run Factors

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

A

1

0

-1

0

0

1

0

0

0

-1

0

1

1

-1

0

-1

0

B

1

-1

1

-1

0

0

0

0

0

0

1

0

-1

0

1

-1

0

C

0 2.26

-1 0.51

0 0.8

1 0

0 0

-1 0.27

0 0

0 0

0 0

1 0

-1 0.39

1 0

0 1.12

-1 1.15

1 0

0 2.99

0 0

Porosity (%)

3. Results and Discussion 3.1. Development of mathematical model Design-Expert 8.0 software is employed for the analysis. Table 3 presents the results for the ANOVA and probable interactions. The value of determination coefficient (R2) was calculated to be 0.9864, indicated that the developed model was very significant. The predicted second-order polynomial model was given as follow f=-0.16A-0.15B-0.29C+0.83AB+0.22AC+0.03BC+0.96A2+0.83B2-0.61C2 According to the mathematical model, laser power (A), welding speed (B), defocus distance (C), interactions AB, AC and quadratic A2, B2, C2 are significant terms, while defocus distance plays the most effective role on porosity. Table 3. ANOVA analysis for the porosity area fraction. Source

Sum of squares

Mean square

F-value

P-value

Source

A-laser power

0.21

0.21

8.59

0.0220

Significant

B-welding speed

0.17

0.17

7.07

0.0326

Significant

C-defocus distance

0.67

0.67

27.78

0.0012

Significant

AB

2.77

2.77

114.47

< 0.0001

Significant

AC

0.19

0.19

7.99

0.0255

Significant

BC

3.60E-3

3.6E-3

0.15

0.7113

A2

3.89

3.8

160.65

< 0.0001

Significant

2

2.91

2.91

120.13

< 0.0001

Significant

C2

1.55

1.51

63.90

< 0.0001

Significant

Model

12.28

1.36

56.35

< 0.0001

Significant

B

3.2. Optimization of process parameters using RSM Three-dimensional response surface plots were constructed to optimize process parameters based on the mathematical model. The interaction effect of defocus distance-laser power and defocus distance-welding speed on porosity are not significant; whereas laser power-welding speed has a significant interaction effect. The minimum

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porosity area fraction was obtained at 3.5kW and 10.0mm/s at the fixed defocus distance of +4mm as shown in Fig.2c. c a b

Actual Factor B: Weld speed = 10.00

Porosity area fraction (%)

X1 = A: Laser power X2 = C: Defocus distance

0

1

Warning! 1 design point outside Y axis range

Design-Expert?Software Factor Coding: Actual

Porosity area fraction (%) Warning! Surface truncated by selected response (Y) range

Design points above predicted value Design points below predicted value 2.99

X1 = B: Weld speed X2 = C: Defocus distance

0.5

Actual Factor A: Laser power = 3500.00

0

-0.5

-1

3750

8 3625

6 3500

4

C: Defocus distance (mm)

3375

2 0

Design points above predicted value Design points below predicted value 2.99 0

1

A: Laser power (W)

3250

Porosity area fraction (%)

0

Design-Expert?Software Factor Coding: Actual

Porosity area fraction (%) Warning! Surface truncated by selected response (Y) range

3

X1 = A: Laser power X2 = B: Weld speed

0.5

Actual Factor C: Defocus distance = 4.00

0

-0.5

-1

Porosity area fraction (%)

Design-Expert?Software Factor Coding: Actual Porosity area fraction (%) Design points above predicted value Design points below predicted value 2.99

15

8

13

6

11 4

C: Defocus distance (mm)

9 2

7 0

5

B: Weld speed (mm/s)

2

1

0

-1

15

3750 13

3625 11

3500

9

B: Weld speed (mm/s)

3375

7 5

A: Laser power (W)

3250

Fig. 2. Response surface plots showing the effects of different variables on the porosity area fraction.

3.3. Validation of the optimal parameters. The accuracy of the optimal process parameters (i.e. laser power of 3.5kW, welding speed of 10.0mm/s, and the defocus distance of +4.0mm) was tested by three confirmation experiments. Fig. 3 shows the excellent joints without macro pores obtained under the optimal process, indicating that the porosity area fraction agreed well with the predicted ones.

Fig. 3. Cross-section morphology of laser welding joints produced under the optimum process parameters.

4. Conclusion A study of fiber laser welding of 7A52 aluminum alloy has been conducted. The effect of laser power, welding speed and defocus distance on porosity susceptibility has been studied, developing an RSM model between the process parameters and porosity area fraction. Defocus distance was found to be the most significant parameter. Laser power of 3.5kW, welding speed of 10.0mm/s, and the defocus distance of +4.0mm are identified as optimum parameters to achieve the desired weld seam without macro pores. Acknowledgements The work was supported by the National Key Research Project (Grant No. 2016YFE0201300) and National Key Research and Development of China (Grant No. 2016YFB1100205). References [1] Y. H. Feng, J. H. Chen, W. Qiang, et al, Microstructure and mechanical properties of aluminium alloy 7A52 thick plates welded by robotic double-sided coaxial GTAW process, Materials Science and Engineering: A, 673 (2016) 8-15. [2] Z. H. Zhang, S. Y. Dong, Y. J. Wang, et al, Microstructure characteristics of thick aluminum alloy plate joints welded by fiber laser, Materials & Design, 84 (2015) 173-177. [3] Z. H. Zhang, S. Y. Dong, Y. J. Wang, et al, Study on microstructures and mechanical properties of super narrow gap joints of thick and high strength aluminum alloy plates welded by fiber laser, International Journal of Advanced Manufacturing Technology, 82 (2016) 99-109. [4] O. O. Oladimeji, E. Taban, Trend and innovations in laser beam welding of wrought aluminum alloys, Welding in the World, 60 (2016) 415457.

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