Optimization of laser welding of DP780 to Al5052 joints for weld width and lap-shear force using response surface methodology

Optics and Laser Technology 126 (2020) 106072

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Optimization of laser welding of DP780 to Al5052 joints for weld width and lap-shear force using response surface methodology

T

Guiqian Liua, Xiangdong Gaoa, , Cong Penga, Yijie Huanga, Haiji Fanga, Yanxi Zhanga, Deyong Youa, Zhang Nanfenga,b ⁎

a b

Guangdong Provincial Welding Engineering Technology Research Center, Guangdong University of Technology, Guangzhou 510006, China Huangpu Customs, Guangzhou 510730, China

HIGHLIGHTS

welding is applied to joint DP780 to Al5052 plates. • Laser types of steel-aluminum welded joint and fracture model are summarized. • Four response surface model of welding parameters are analyzed. • The • The laser power and welding speed are significant effect on lap shear strength. ARTICLE INFO

ABSTRACT

Keywords: Laser penetration welding Steel-Al dissimilar alloys Response surface methodology

A laser welding of DP780 steel to Al5052 joint has been investigated to optimize the weld width and lap-shear force. Four types of steel-Al welded joints and fracture model have been proposed. Response surface methodology and central composite design were applied to develop mathematical relationships between the control parameters and the responses. Then, the validation and optimization experiments were carried out to check the models adequacy. The results show that there exists good consistency between the predicted and actual value. This indicates the developed models could predict the weld width and lap-shear force within the limits of the process parameters.

1. Introduction In recent years, high power laser technology have developed rapidly. With the need of engineering increasing, the components made of different materials are receiving more and more attention in many industries including shipbuilding, aerospace and automotive industry. However, the connection between aluminum and steel is a huge challenge due to differences in thermal conductivity, coefficient of thermal expansion, electrical resistivity and almost zero solid solubility, especially in the formation of brittle Fe-Al metal compounds that are formed under high temperatures [1]. At present, a variety of connection methods have been applied to the study of aluminum-steel connections, such as laser welding-brazing [2–5], laser penetration welding [6–8], laser–arc hybrid welding [9–11], friction welding [12], explosion welding [13], magnetic pulse welding [14,15], pulse MIG welding [16], and dual beam laser keyhole welding [17,18]. The effects of processing parameters, power distribution ratio, double beam laser distance of the

⁎

weld shape, micro structure of the inter metallic compound layer, and micro hardness and tensile strength of the steel/aluminum joint were investigated [17]. Laser welding of duplex steel to aluminum alloy was studied in experimental and computational modeling. Three different laser energy densities to study the effect of heat input on micro structure changes and strength [19]. The application of the magnetic field can change the weld appearance and micro structure of the weld through the Lorentz force and the thermal electromagnetic force caused by the molten pool. The steel/aluminum in the reaction zone is reduced due to the diffusion of Al atoms, which could reduce the microhardness and improves tensile strength [20]. Hybrid laser arc welding is used to connect aluminum alloy intermediate structure inserts between advanced high-strength steel slabs with explosion-welded transition joints, and optimizes the interface generated by explosive welding for the welding process. In the tensile test, the weld was broken in the heat affected zone on the aluminum side. Studies shows that the presence of tempered marten-site is near the weld zone, which is the reason of the

Corresponding author. E-mail address: [email protected] (X. Gao).

https://doi.org/10.1016/j.optlastec.2020.106072 Received 27 March 2019; Received in revised form 25 November 2019; Accepted 11 January 2020 0030-3992/ © 2020 Elsevier Ltd. All rights reserved.

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softening of the heat affected zone [21]. Due to the local energy input, laser welding shortens the reaction time of Fe and Al atoms which contributes to the residence time. Compared with other technologies, laser welding has a fast welding source and a short processing time. Most importantly, laser welding parameters can be optimized to cover a wide range of different metal combinations. However, to date, the number of details of the process parameters has not been studied. According to published literature on the subject, the purpose of this work is to optimize the process parameters in the steel-aluminum welding configuration. Response surface methodology (RSM) is defined as the relationship between the controllable input welding process parameters and the desired response [22]. It is used because it requires a minimum of experimental runs compared to other methods. It will provide information on the interactions between various welding process parameters and how it affects the output response [23]. In the current work, laser welding parameters of duplex steels and aluminum alloys have been optimized to determine the optimum process parameters for obtaining the desired response. The effects of process parameters on weld width and lap-shear force were investigated. The optimization solution involving process parameters determined by RSM has been experimentally verified.

by a six-axis servo motor, which is shown in Fig. 1b. The movement of the test piece is driven by the precision servo motor of the workbench. During the welding process, the aluminum liquid floats up to the surface of the weld, and the aluminum has a high laser reflectivity. To prevent equipment from being damaged, the laser beam is set at 10° in the welding direction, and the argon tube is placed at 45° to the vertical. The weld specimen is fixed by welding fixture through shims and press plates. The gas flow rate is set at 20 L/min, the wavelength of laser is 1070 nm and the focus spot diameter is 400 μm. An ultraviolet and visible sensing camera is used to obtain the features of metal vapor and spatters, and the sampling frequency is 4000 frames per second. The materials of specimen are DP780 dual phase steel and 5052 aluminum alloy, and the size of DP780 and Al5052 is 150 × 110 × 2 mm. The schematic illustration of tensile shear testing is shown in Fig. 1c. The photograph of welded sample and test sample is shown in Fig. 1d. The experiment is designed as a three-factor five-level central composite rotatable design, which is proposed in references [24]. The experiment process parameters are identified as: laser power (A), welding speed (B) and laser focal position defocus (C). The range of parameters is determined by whether there are obvious defects on the weld surface and whether the joint can be connected. Statistical software design-expert v8.0 is used to code the parameters and design the matrix. The response surface method is used to obtain the regression equation, and the statistics and response graphs are generated. The critical values, units and abbreviations of the selected process parameters are given in Table 1.

2. Experimental setup The experimental setup is performed by using 4 kW fiber laser welding system, which is shown in Fig. 1a. The welding robot is driven

Fig. 1. Schematic illustration of (a) experimental setup of laser welding, (b) photographic view of experimental set up, (c) tensile shear testing and (d) photograph of welded sample and test sample. 2

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the fifth row of the Table 3 and the fracture mode of the sixth row that the metal vapor oscillation of the weak weld is stable, the amount of spatter is small, the penetration of the aluminum side is small, and bonding strength of steel and aluminum is low, and the fracture usually occurs in steel and aluminum joint welds. The number of spatters in strong welds has increased, the surface of the weld is slightly lower than the surface of the base metal, the penetration depth of the aluminum side is larger than that of the weak weld and the joint strength of steel and aluminum is higher, and the joint fracture position usually occurs in combination. The number and size of spatters in narrow welds is increased compared to weak welds and strong welds. The welds have large depressions. The penetration in the aluminum side is deeper than weak welds and strong welds, and the fracture location occur generally in steel and the heat-affected zone of the side. The weld has high joint strength due to the large penetration depth of the steel-aluminum joint surface. And the strength of the heat-affected zone is lower than that of the steel-aluminum joint weld due to the influence of the depression on the steel side. The brittle weld is produced with the high laser energy input, resulting in the welding penetration fully of the aluminum plate, while the surface metal vapor size is reduced, the amount of spatter is relatively small, and a large amount of spatters is sprayed from the bottom, the aluminum side of the weld is severely oxidized. The weld is produced with defects such as depressions, cracks, spatters, etc., which lead to poor bonding strength between steel and aluminum, and the fracture location usually occurs at the cracks in the middle of the weld.

Table 1 The experiment parameters and levels. Parameters

Units

Abbreviations

Level

Laser power Welding speed Defocus

kW m/min mm

A B C

−2 2.1 2.45 −2

−1 2.2 2.8 −1

0 2.3 3.15 0

+1 2.4 3.5 1

+2 2.5 3.85 2

Table 2 The design matrix and measured responses. Experimental information

Responses

Std order

Weld-seam width (mm)

Lap-shear force (N)

1.096 1.072 1.104 1.048 1.33 1.185 1.266 1.169 1.137 1.395 0.766 1.08 0.919 1.306 1.225 1.161 1.185 1.25 1.112 1.145

1721.33 1613.90 1171.42 1610.26 1974.89 1776.72 1263.91 1464.23 1484.27 1824.77 1767.55 1213.40 1794.87 1487.42 2040.20 1908.31 1732.32 1944.34 1872.38 1777.10

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Run order

13 4 19 17 6 7 11 9 5 2 16 14 3 8 20 15 1 10 18 12

Welding parameters A(kW)

B(m/ min)

C(mm)

2.2 2.4 2.2 2.4 2.2 2.4 2.2 2.4 2.1 2.5 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3

2.8 2.8 3.5 3.5 2.8 2.8 3.5 3.5 3.15 3.15 2.45 3.85 3.15 3.15 3.15 3.15 3.15 3.15 3.15 3.15

−1 −1 −1 −1 1 1 1 1 0 0 0 0 −2 2 0 0 0 0 0 0

3.2. Analysis of the models 3.2.1. Analysis of weld-seam width The fitting of the weld seam width shows that the weld seam width has a quadratic relationship when the additional items are significant and the model is not aliased. The adequacy measure R2, adjusted R2, predicted R2, adequate precision and the ANOVA table of the model is given in Table 4. The Model F-value of 3.15 implies the model is significant. In this case, the defocus (C) and welding speed (B2) are significant model terms. The other model terms are not significant, and significant lack of fit is bad. The “Lack of Fit F-value” of 6.47 implies there is only a 3.06% chance that a “Lack of Fit F-value” this large could occur due to noise. Thus, the backward elimination is applied to make the lack of fit to be not significant. The ANOVA table of the model after backward elimination is shown in Table 5. The adequacy measure R2, adjusted R2, predicted R2 and adequate precision are 0.6569, 0.5925, 0.1381 and 12.252, which indicate adequate model. The model reduction results show that the model has obvious significance. The final mathematical model before and after backward elimination that can be used to predict weld width in the same design space is as follows, respectively:

3. Result and discussion 3.1. Analysis of the four types welded joint and fracture model A universal testing machine is used for shear strength testing. The lap-joints are tested with a speed of 1 mm/min and the shear strength is calculated as the maximum breaking force. The weld seam width of each test piece is observed at the center of the weld-seam under a microscope. Both the weld seam width and the lap-shear force are averaged from at least three results, as shown in Table 2. As shown in the Table 3, the characterization of steel-aluminum welds can be divided into four types according to the joint strength and fracture mode characteristics: weak welds, strong welds, narrow welds and brittle welds. The second row of the Table 3 is four types, the third row is the partial view of the weld surface, the fourth row is the metal steam and spatter behavior during the welding process, the fifth row is the welding process schematic, and the sixth row is the joint fracture mode. As shown in the third row of the Table 3, the partial micrograph of the weld surface, the weld width of the weak weld is large, and the flow of the weld pool can be clearly seen from the weld surface. The surface of the strong weld has protrusions and the width is reduced. The welding process of strong welds produces more metal spatters, and the unstable flow of the weld pool causes the weld surface to bulge. The molten pool of the narrow weld is more unstable because the large amount of molten aluminum and steel liquid reacts violently, and a large amount of spatters is sprayed from the surface, and cause the molten pool to stay, causing the weld to be depressed. Brittle welds produce defects such as sunken, cracks, and spatters. It can be seen from the schematic diagram of the welding process in

(a) Final equation in terms of coded factors:

Weld seam width = 1.19 + 0.012A + 0.033B + 0.088C + 0.002AB +

0.025A2

0.061B2

0.020AC

0.008BC (1)

0.013C 2

(b) Final equation in terms of actual factors:

Weld seam width = 9.37531

11.59534A + 3.08377B + 0.6255C + 0.057143AB

0.20250AC

0.022857BC + 2.50795A2

0.49527B2

0.013295C 2 (2)

(c) Final equation in terms of coded factors after backward elimination:

Weld seam width = 1.20 + 0.033B + 0.088C

0.063B2

(3)

(d) Final equation in terms of actual factors after backward 3

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Table 3 The four types of steel-aluminum welded joint and fracture model. Steel-aluminum welded joint and fracture model Std order

7

18

2

11

Four types

Weak welds

Strong welds

Narrow welds

Brittle welds

Weld surface

Metal vapor and spatters behavior

Schematic diagram of welding process

Fracture mode

Table 4 The ANOVA analysis for the weld-seam width model. Sum of Source Model A B C AB AC BC A2 B2 C2 Residual Lack of Fit Pure Error Cor Total Std. Dev. Mean C.V. % PRESS

Squares 0.28 2.35E-03 0.018 0.12 3.20E-05 3.28E-03 5.12E-04 0.016 0.093 4.44E-03 0.098 0.085 0.013 0.38 0.099 1.15 8.64 0.73

df 9 1 1 1 1 1 1 1 1 1 10 5 5 19

Table 5 The ANOVA analysis for the weld-seam width model after backward elimination.

Mean

F

p-value

Square 0.031 2.35E-03 0.018 0.12 3.20E-05 3.28E-03 5.12E-04 0.016 0.093 4.44E-03 9.84E-03 0.017 2.63E-03

Value 3.15 0.24 1.8 12.53 3.25E-03 0.33 0.052 1.61 9.41 0.45

Prob > F 0.044 0.6354 0.2096 0.0054 0.9556 0.5764 0.8241 0.2335 0.0119 0.5167

6.47

0.0306

Significant

R-Squared Adj R-Squared Pred R-Squared Adeq Precision

0.7395 0.505 −0.9305 6.185

Sum of Significant

Source Model B C B2 Residual Lack of Fit Pure Error Cor Total Std. Dev. Mean C.V. % PRESS

4.18425 + 3.32214B + 0.087750C

df 3 1 1 1 16 11 5 19

F

p-value

Square 0.083 0.018 0.12 0.11 8.10E-03 0.011 2.63E-03

Value 10.21 2.18 15.22 13.23

Prob > F 0.0005 0.1588 0.0013 0.0022

4.02

0.0682

Not significant

R-Squared Adj R-Squared Pred R-Squared Adeq Precision

0.6569 0.5925 0.1381 12.252

Significant

defocus (C2) are significant model terms. The adequacy measure R2, adjusted R2, predicted R2 and adequate precision are 0.8496, 0.7142, 0.1177 and 8.628, which indicate adequate model. The final mathematical model that can be used to predict lap-shear force could be given as follows:

elimination:

Weld seam width =

Squares 0.25 0.018 0.12 0.11 0.13 0.12 0.013 0.38 0.09 1.15 7.84 0.33

Mean

0.51224B2

(a) Final equation in terms of coded factors:

(4)

Lap

shear force

= 1862.43 + 63.41A

3.2.2. Analysis of lap-shear force The fitting of the lap-shear force shows that the weld width has a quadratic relationship when the additional items are significant and the model is not aliased. The adequacy measure R2, adjusted R2, predicted R2, adequate precision and the ANOVA table of the model is given in Table 6. The “Lack of Fit F-value” of 1.91 implies the Lack of Fit is not significant relative to the pure error. There is a 24.73% chance that a “Lack of Fit F-value” this large could occur due to noise. Non-significant lack of fit is good, values of “Prob > F” less than 0.0500 indicate model terms are significant. In this case, the welding speed (B), laser power and welding speed (AB), laser power (A2), welding speed (B2),

58.74BC

167.83B

64.49A2

15.75C + 118.10AB

105.50B2

41.16AC

67.83C 2

(5)

(b) Final equation in terms of actual factors:

Lap =

shear force 16298.29670 + 19669.17273A + 3374.14286AB 861.20315B2

4

411.5750AC 67.82989C 2

2814.47013B + 1459.52875C 167.82857BC

6448.61364A2

(6)

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Table 6 The ANOVA analysis for the lap-shear force with model. Sum of Source Model A B C AB AC BC A2 B2 C2 Residual Lack of Fit Pure Error Cor Total Std. Dev. Mean C.V. % PRESS

Squares 1.04E + 06 64333.25 4.51E + 05 3970.89 1.12E + 05 13551.52 27603.1 1.05E + 05 2.80E + 05 1.16E + 05 1.84E + 05 1.21E + 05 63056.22 1.22E + 06 135.47 1672.18 8.1 1.08E + 06

df 9 1 1 1 1 1 1 1 1 1 10 5 5 19

Mean

F

p-value

Square 1.15E + 05 64333.25 4.51E + 05 3970.89 1.12E + 05 13551.52 27603.1 1.05E + 05 2.80E + 05 1.16E + 05 18352.74 24094.23 12611.24

Value 6.28 3.51 24.56 0.22 6.08 0.74 1.5 5.7 15.25 6.3

Prob > F 0.0041 0.0907 0.0006 0.6518 0.0334 0.4103 0.2481 0.0382 0.0029 0.0309

1.91

0.2473

not significant

R-Squared Adj R-Squared Pred R-Squared Adeq Precision

0.8496 0.7142 0.1177 8.628

Significant

3.2.3. Validation of the developed models The Fig. 2 shows the relationship between the actual value and the predicted value of the weld width and the lap-shear force. It can be seen from the Fig. 2 that the error between the actual value and the predicted value is small, which indicates that the established model is sufficient and the actual value is basically consistent with the predicted value. In order to verify the response surface equation derived from multiple regression analysis, a verification test of three randomly selected welding conditions was carried out. The experimental range was derived from the equation. The actual results, predicted values and calculation errors of the verification test are shown in the Table 7. It can be seen from the verification test that the percentage error between the predicted value and the actual value is small, which indicates that the result of the model calculation is accurate.

Table 7 The validation test results.

3.3. Effects of process parameter on the response

increases until the welding speed reaches a center value, and then the weld width decreases as the welding speed increases. This is because at a lower welding speed, the laser energy input per unit time is too high, and a large amount of mixed spatter of molten steel and aluminum liquid is generated, which causes unstable depressions on the surface of the weld, resulting in formation of narrow welds and brittle welds. When the welding speed increase, the laser energy input per unit time will reduce and the depression on the surface of the weld becomes smaller. So the weld width increase. At higher welding speeds, although

3.3.1. Weld seam width The Fig. 3 shows the impact change chart of each process parameter in the design space. It can be seen from the Fig. 3 that the weld width increases with the increase of the defocus. This is because as the amount of defocus increases, the spot size acting on the surface of the material increases, thereby causing the laser energy to act on a larger area and forming a wider weld. The weld width increases as the welding speed

Experiment

Laser power A(kW)

Welding speed B(m/min)

Defocus C(mm)

1

2.3

2.8

−2

2

2.2

3.5

−2

3

2.5

3.5

−2

Fig. 2. Plot of actual vs. predicted value of (a) weld seam width and (b) lap-shear force. 5

Actual Predicted Error Actual Predicted Error Actual Predicted Error

Weld seam width (mm)

Lap-shear force (N)

1.02 0.92 9.8% 1.03 0.99 3.88% 1.08 0.99 8.33%

1737.48 1770.96 1.92% 1299.06 1341.95 3.30% 1799.55 1939.95 7.92%

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Fig. 3. The perturbation of the effect of factors on the weld seam width when A = 2.30, B = 3.15, C = 0.

Fig. 5. The perturbation of the effect of factors on the lap-shear force when A = 2.30, B = 3.15, C = 0.

there are no dent defects due to a large amount of spatter, the laser energy input per unit time is lower, and the width of the weld is also reduced. It can also be seen from the Fig. 3 that the effect of laser power on the weld width is relatively stable. It can be seen from the Fig. 4a and Fig. 4b that at lower welding speeds, the weld width increases as the welding speed increases, and at higher welding speeds, the weld width decreases as the laser power increases. A larger weld width can be obtained when the welding speed is at the center value. It can be seen from the Fig. 4c that the larger the defocus, the larger the weld width, and the increase of the laser power increases the weld width at the same defocus. Although the power density decreases at constant laser power, the increase of defocus increasing the area of interaction, resulting in a wider weld. The Fig. 4d shows the interaction effect of welding speed and defocusing amount on the weld width. It can be seen from the Fig. 4e and Fig. 4f that as the amount of defocus increases, the welding speed increases, and the weld will reach an extreme value after reaching the center point. With welding speed with continue to increase, the weld width will decrease.

3.3.2. Lap-shear force The Fig. 5 shows the effect of all factors on the tensile strength at the center point of the design space. It can be clearly seen from the Fig. 5 that the welding speed has a negative influence on the lap-shear force. This is because the laser deep penetration welding of steel and aluminum and the thermal diffusion of the bonding surface can fully react, mainly depending on the laser energy density and irradiation time, while the higher welding speed reduces the irradiation time, resulting in low heat input into the steel-aluminum bond zone and fewer aluminum sides are melted. Increasing the laser power can effectively increase the laser energy density, thereby increasing the weld melt volume and weld strength. The Fig. 5 also shows that the lap-shear force increases as the distance from the defocus increases until the center value is reached, and the lap-shear force begins to decrease when the defocus exceeds the center value. It can be clearly seen from the Fig. 6a and Fig. 6b that as the welding speed decreases and the laser power increases, the lap-shear force tends to increase. Because the laser power increases with the increase of the

Fig.4. The contours and response surface plot on the weld seam wide when (a) and (b) showing the effect of A and B at C = 0, (c) and (d) showing the effect of A and C when B = 3.15, (e) and (f) showing the effect of B and C when A = 2.3. 6

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Fig. 6. The contours and response surface plot on the lap-shear force when (a) and (b) showing the effect of A and B at C = 0, (c) and (d) showing the effect of A and C when B = 3.15, (e) and (f) showing the effect of B and C when A = 2.3.

welding speed, the energy input per unit time is stable, thus forming a good bond weld. The low energy input will result in poor penetration, poor heat transfer and no sufficient reaction between steel and aluminum, which results in poor soldering. For thinner plates, excessive laser power will cause the reaction of steel and aluminum in the molten pool to be severe, and the pressure in the keyhole will increase sharply and cause a large amount of spatters, resulting in welding defects such as depressions and protrusions. Combining appropriate laser power with welding speed, the weld seam has no dent defect at low welding speed and laser power, and the best welding strength can be obtained at low energy input. As shown in the Fig. 6c and Fig. 6d, the influence of laser power and defocus on the lap-shear force can be seen from the contour map and the response surface map. As the laser power increases, the defocus increases to the center value. The shear strength tends to increase, and further increasing the amount of defocus reduces the lap-shear force. In the case where the welding speed is constant, a combination of a higher welding power and a lower defocus is easier to obtain a better lap-shear force. The Fig. 6e shows the influence of welding speed and defocus on the lap-shear force. It can be seen from the Fig. 6f that the best lap-shear force can be obtained when the lower welding speed and the defocus are close to the center value. Increasing welding speed will reduce the joint strength.

desirability function could be set as three steps: Step 1: Set goals of each response, including the weld parameters, weld seam width and lap shear force; Step 2: Set minimum or maximum limits for each response; Step 3: Enter the desired operating conditions and discover the predicted response. The optimization criteria in this study are shown in Table 8. All welding parameters have an influence in weld bead and the lap-shear force. The welding speed is important, and its importance set as 3, taking into production efficiency. The laser power’s importance and the defocus’ importance are set as 2 and 1, respectively. For the responses, lap-shear force is more important than weld seam width. Therefore, maximizing and optimizing the lap-shear force is paramount and the importance is set as maximum 5. The importance of weld seam width is set as 4. The results are given in the form of a superimposed contour map. Overlays enable quick visual inspection of areas of feasible response values in the process variable space to select the best combination of process parameters. The contour map of desirability is shown in Fig. 7. The best parameter range can be seen in the contour plot. The determined process parameters produce weld geometry with maximum lap-shear force, large weld width and faster welding speed. 4. Conclusions A study of laser welding of DP780 to Al5052 joints has been Table 8 The optimization criteria in this work.

3.4. Optimization using desirability approach

Constraints Name

Simultaneously studying multiple responses requires first establishing an appropriate response surface model for each response and then trying to find a set of optimized operating conditions; it can optimize all responses or at least keep them within the required range. Starting using this method to solve multiple response optimization problems, using a method to combine multiple responses into a dimensionless performance metric called the overall satisfaction function, E = (E1.E2 … .Em)/m, where m is the number of responses. The higher the value of E, the better the system's maximum functionality and it will be considered as the best solution [22]. The optimization using

A: Laser power (kW) B: Welding speed (m/min) C: Defocus (mm) Weld seam width (mm) Lap-shear force (N)

7

Goal

Lower Limit

Upper Limit

Lower Weight

Upper Weight

Importance

In range

2

2.6

1

1

2

In range

2.1

4

1

1

3

In range In range

−2 0.766

2 1.395

1 1

1 1

1 4

Maximize

1171.42

2040.2

1

1

5

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Fig. 7. The desirability of the optimization when C = 0.27.

conducted. The following conclusions can be reached from this study: 1. Four types of steel-aluminum welded joint and fracture model are summarized. Response surface methodology has been found to be effective for the prediction of weld seam width and lap-shear force in laser welding process. 2. The welding speed and defocus have been found to be significant in ANOVA analysis of the weld seam width, while all the three process parameters have been found to be significant in controlling the lapshear force. 3. The combination of laser power and welding speed was found to be significant effect on lap-shear force. Laser power from 2.2 to 2.4 kW, welding speed from 2.8 to 3.1 m/min at the defocus equal to 0.27 mm are identified as the optimization to get the desired weld with high strength in laser welding of DP780 and Al5052. CRediT authorship contribution statement Guiqian Liu: Data curation, Writing - original draft, Formal analysis. Xiangdong Gao: Project administration, Funding acquisition. Cong Peng: Investigation, Data curation. Yijie Huang: Resources, Software. Haiji Fang: Data curation. Yanxi Zhang: Writing - review & editing. Deyong You: Investigation. Zhang Nanfeng: Resources. Declaration of Competing Interest The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted. Acknowledgement This work was partly supported by the National Natural Science Foundation of China (51675104), the Guangzhou Municipal Special Fund Project for Scientific and Technological Innovation and Development (202002020430100004), and the Innovation Team Project, Department of Education of Guangdong Province, China (2017KCXTD010). Many thanks are given to Zhengrong Zhang laboratory of Guangdong University of Technology, for their assistance of experiments.

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