Parameter optimization of dissimilar resistance spot welding on ultra-high strength hot-stamped steel and mild steel by numerical simulation

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1 Available online at Acta Metall. Sin.(Engl. Lett.)Vol.25 No.6 pp December 2012 Parameter optimization of dissimilar resistance spot welding on ultra-high strength hot-stamped steel and mild steel by numerical simulation Wen ZHENG 1), Min WANG 1), Liang KONG 1), Xuanting CHENG 2) and Ming LEI 3) 1) Shanghai Key Laboratory of Materials Laser Processing and Manufacture, Shanghai Jiao Tong University, Shanghai , China 2) Shanghai Yirui Car Technology Co., LTD, Shanghai , China 3) Bao Steel Technology Center, Shanghai , China Manuscript received 31 May 2012; in revised form 4 July 2012 In this study, a coupled axisymmetric finite element model (FEM) was built to simulate the resistance spot welding (RSW) process between ultra-high strength hotstamped (UHSS) and mild steel by SORPAS software. Via simulating this process, the temperature distribution and dynamic temperature curves of the welding area were studied, and welding spatter phenomena were predicted and validated by comparing them with experimental results. By adjusting the welding parameters in numerical simulation, appropriate welding parameters were achieved. Moreover, the mechanical properties of the welding joints under the optimized conditions were also compared with those of not optimized. The study results have already been applied in a manufacturing production. It can also provide guidance for the RSW on UHSS and mild steel. KEY WORDS Ultra-high strength hot-stamped steel; Resistance spot welding; Numerical simulation; SORPAS 1 Introduction Resistance spot welding (RSW) process is widely used for assembling thin sheet steels in the automotive industry due to its advantages in welding efficiency and suitability for automatic production. With the trend of vehicle body structure lightweighting in recent years, ultra-high strength hot-stamped steel (UHSS), which has its capacity of lightening the mass without reducing the strength of vehicle at the same time, is increasingly applied in the complex structures such as bumper, A-, B-, and C-pillars [1]. During RSW process on the UHSS, deformation, stress and strain will be generated and changed in the workpieces. These mechanical features have great influence on the properties of the weld joint, including the formation of the weld nugget and the failure strength, etc. Therefore, it is very important to understand the temperature changes, deformation Corresponding author. Professor; Tel: ; Fax: address: wang-ellen@sjtu.edu.cn (Min WANG)

2 488 and phase transformation of the metallographic structure in the welding process. However, RSW is a sophisticated process coupled with interactions such as thermal-electricalmechanical fields. It is very difficult to obtain insightful information of the welding process through the experiments alone. On the other hand, finite element numerical modeling and simulation provide a valuable way in studying the spot welding process [2 8]. Through building a transient axisymmetric numerical model to simulate the RSW process with a FEM program, ANSYS, Wang et al [3] successfully predicted the spot welding nugget diameter and thickness of the zinc-coated steel. Furthermore, new codes were typed into the procedure by the authors in order to use some advanced functions of the ANSYS software, making the mechanical analysis and thermoelectric analysis coupled in this model, which enhanced the simulative accuracy by 200%. Hou et al [4] developed and analyzed a two-dimensional axisymmetric thermal-elasticplastic FEM model in ANSYS. Through the analysis, the following behaviors of the mechanical feature during the RSW process were obtained: the distribution and change of the contact pressure at the electrode workpiece interface and faying surface of the steel plates, the distribution of the stress and strain, the deformation of the weldment, and the displacement of the electrode. Eisazadeh et al [5] built an FEM model to study the phenomenon of the nugget formation and the temperature of the faying surface. Using this analysis, shape and size of weld nuggets were computed and validated by comparing them with experimental results from published articles. The methodology developed in the paper provided a prediction of quality and shape of the weld nuggets with variation of each process parameter. Utilizing this methodology in adjusting welding parameters can avoid costly experimental work. Galler et al [7] predicted unit area electrical contact resistance curves at the electrodeworkpiece and faying interface of galvanized DP600 steel with the FEM program SYSWELD. The numerical optimization procedure obtained in SYSWELD was cited the experimental resistance values. The newly predicted curves were used in an RSW simulation and produced good matching to an experimental cross section regarding the shape and radius of the molten and the heat affected zones. On the issue of the numerical simulation of RSW on dissimilar steels, Feulvarch et al [9] proposed a general finite element formulation of the thermoelectric contact to accurately account for the relative displacements of contact surfaces. Unlike most else studies which thought that contact surfaces were close enough to be considered as a normal flux exchange, the study assumed with a model that contact conditions were ensured through elements linking two nodes face to face, so that only small relative displacements were considered. A finite element simulation of the welding on three dissimilar steel sheets was presented based on the assumption. The results of the finite element analysis presented a correct correlation with experiments in terms of the heat affected zone (HAZ) size as well as the nugget shape. Ma and his group [10,11] developed an FEM software named JWRIAN with coupling of electrical field, thermal field and mechanical field. The nugget sizes and its formation process were predicted by JWRIAN. The results agreed very well with experimental results. The welding conditions (current, cycles and force) to produce sound nuggets for two pieces and three pieces of high strength steel sheets were both accurately estimated by the simulation.

3 489 Shen et al [12] predicted the weld nugget formation process of RSW on a multiple stacks of the steel sheets SAE1004(galvanized) and SAE1004(galvanized) and dual-phase steel(dp600) by numerical simulation. It was found that the weld nugget on the faying interface of DP600 formed earlier than that on the other interface, which agreed well with the experimental results. That means the nugget will form earlier at the area of the higher strength steel plate. Although UHSS has the advantages of high strength and hard deformation, automotive producers still prefer assembling vehicles with mild steel as the major and part structures with UHSS considering some weaknesses of UHSS, such as unsuitable for car s shape design requirement, immature of research and applications at present, and the higher cost of production than the mild steel. Therefore, the RSW process of dissimilar steels on UHSS and mild steel is necessary in the production. Although so many researches have been conducted, we are still lack of numerical simulation information of the RSW process on UHSS and mild steel. More detailed analyses about the inherent process of RSW are needed. The objective of this study was focused on developing a model to simulate the RSW process of the dissimilar steels on UHSS and mild steel based on an FEM software, SOR- PAS. The simulative results were compared to the experimental results to verify the accuracy of the model, then modifying and optimizing the original welding parameters, and obtained the suitable welding parameters for the UHSS and mild steel finally. The numerical simulation can reduce the experiment cost unnecessary and the weld parameters finally got can provide a guidance for further study. 2 SORPAS and Computational Model 2.1 SORPAS and its FEM model Simulation and optimization of resistance projection and spot welding processes (SOR- PAS), which was developed by Dr. Zhang and co-workers [13] from Denmark, is an FEM software for analyzing the resistance welding process especially for spot welding and projection welding. SORPAS has now been widely applied in many companies in Europe including DaimlerChrysler, Volkswagen, PSA Peugeot Citroen, VOLVO, Siemens, ABB, etc [14]. Unlike other FEM software, users do not need to collect all the mechanical properties before simulation in the SORPAS operation. There are four active databases in SORPAS to ease the definition of physical properties in the RSW process simulation. The users can edit, modify and add new items in the databases for material data, geometries of electrodes and workpieces and welding machine properties [15]. In this study, a two-dimensional axisymmetric model related to the experiment was built by SORPAS software shown in Fig. 1 illustrating the spot welding process. There is a contact layer between each electrode-workpiece and faying surface of the steels to represent the contact performance. Some boundary conditions for simulation are given: (1) Heat transfer to the surrounding air was by convection and the convective heat transfer coefficient was 25 W/(m 2 K) in the calculation; Air temperature was assumed to be at 20 C. (2) Water temperature inside the welding electrode was assumed to be at temperature 21 C; Water flow rate was 2 L/min.

4 490 Figure 2 shows how the FEM model was meshed in SORPAS. Elements were coupled with 4-nodes in the meshing. The weld heating area was fine meshed while the base metal and the low temperature heating area were coarse meshed. In this meshing method, simulation can keep higher computational accuracy as well as increase computing efficiency and save time. RSW process was simulated coupling with four numerical models: an electrical, a thermal, a metallurgical and mechanical model. More mathematic equations and boundary conditions about the numerical models of SORPAS have already been given in Ref. [14] in detail. Fig. 1 Boundary conditions for simulation Fig. 2 FEM model of the spot welding 2.2 Parameters of the welded materials The steel sheets in this study were UHSS (BR1500HS) and mild steel (HC260LAD+Z), Z means galvanized. As lacking of original material properties of BR1500HS in SORPAS software database, we choose another UHSS (USIBOR1500) with similar chemical component and mechanical properties in numerical simulation. The chemical compositions of BR1500HS, USIBOR1500 and HC260LAD+Z are shown in Table 1. Electrode material is Cr-Zr-Cu with a face diameter of 6 mm. Electrode force in the welding is 3 kn and 1 welding cycle is defined as 20 ms (1 cyc=20 ms). Table 1 Chemical compositions (wt pct) of BR1500HS, USIBOR1500 [1] and HC260LAD+Z Grade of steel C Si Mn P S Al Ti BR1500HS USIBOR1500 [1] HC260LAD+Z Results and Discussion 3.1 Welding process in original welding parameters Based on the original parameters in Table 2, a 2.0 mm thick UHSS plate and a 1.5 mm thick mild steel plate were spot welded. To remind firstly, the squeeze time in the study

5 491 Table 2 Original welding parameters of UHSS and mild steel No. Current pulse I 1(preheat)/kA I 2(weld)/kA I 3(temper)/kA 1-1 Monopulse With preheated Triple-pulse Note: weld time t 1(preheat)=t 2(weld)=t 3(temper)=6 cyc was 15 cyc (300 ms). The time recorded in the study were all including the squeeze time. In the welding process, it is generally acknowledged that the temperature in the welding area was higher than melting points of the base metal, and form the weld nugget after cooling. If the temperature of some local area was much higher than the melting point, welding spatter phenomenon would happen. Welding spatter will strongly harm the weld quality and increase the depth of weld indentation. It has to be avoided in production. Based on the original welding parameters from No. 1-1 to No. 1-3, SORPAS simulated the welding processes of the dissimilar steels, and the peak temperature distribution results are shown in Fig. 3. We can find that the peak temperatures for three cases are all in the center of the welding area. Their values (>2100 C) are much higher than the melting point of UHSS or mild steel ( C). It can be predicted that welding spatter would happen in the welding process. Fig. 3 Peak temperature distribution of the welding area with original welding parameters: (a) monopulse; (b) with preheated; (c) triple-pulses After applying the weld current, the temperature of the workpieces would rise by the generation of the joule heat. Figures 4a 4d show the weld nugget evolution based on welding parameter Nos. 1 3, an electrode force of 3 kn and the weld times of 0.5th cycle, 1st cycle, 2.5th cycle and 3.5th cycle, respectively. As shown in Figs. 4a and 4b, when the preheating cycle began, the temperature of top-to-middle sheet interface increased slower than that of middle-to-bottom sheet interface. That means the heating rate was not the same for each steel plate. Heating rate in the UHSS side was faster than that in the mild steel. Figure 4c shows the results when the preheating time was 2.5th cycle. A weld nugget profile formed at the side of UHSS but it was still heating at the side of mild steel. It can be also assumed that when the cooling time started, weld nugget would form faster in the UHSS side. The weld nugget in the workpieces at last was asymmetric and migrated. Nugget migration phenomenon was also referred in Ref. [5]. Figure 4c also shows that the welding spatter did happen

6 492 Fig. 4 Weld nugget evolution, Para.1 3: (a) 0.5th cycle, preheating; (b) 1st cycle, preheating; (c) 2.5th cycle, preheating; (d) 3.5th cycle, preheating soon in preheating cycle. The spatter was still happened at the 3.5th cycle, at which the whole nugget profile had already formed completely. The steels faced serious possibility of weld penetration situation. The experimental weld nugget and simulative result are shown in Fig. 5. The nugget offset is obvious. Nugget size at the UHSS side is as much as double of that at the mild steel side in Fig. 5. However, the depth lost of the steels caused by welding spatter was not calculated in the simulation by SORPAS, so that the simulative nugget size in Fig. 5 was bigger than experimental size. Fig. 5 Comparison between the experimental weld nugget and the simulative result Taking other welding parameters of Table 2 into simulation, the experimental results showed that big welding spatters all occurred in every welding process of dissimilar steels.

7 493 The RSW process on UHSS and mild steel is failed. At the same time, we can still conclude that the numerical simulation results matched the experimental results. In fact, such unqualified welding parameters can be weeded out and experimental time and cost can be saved through simulating the temperature distribution of the welding process by SORPAS. 3.2 Modification and optimization of parameters It can be seen that the excess of preheating current will cause welding spatter at the preheating period. Welding spatter phenomenon should be avoided and enough thermal input should be ensured in the welding process. The original welding parameters need to be modified and optimized. Table 3 shows the finally confirmed weld parameters of the dissimilar steels after continuous simulation tests. In Table 3, it can be seen that the preheating and tempering current (I 1,I 3 ) are set as nearly 40% and 60% of the welding current (I 2 ) respectively. The welding and tempering time are prolonged. Table 3 Modified welding parameters of UHSS and mild steel No. I 1(preheat) I 2(weld) I 3(temper) t 2(weld) t 3(temper) Current pulse ka ka ka cyc cyc With preheated Triple-pulse Note: Weld time: t 1(preheat)= 6 cyc; weld current: I 1 40%I 2, I 3 60%I Nodal temperature curves after modifying In order to achieve the inherent temperature change conditions, an FEM model of the welding area was created by SORPAS based on the Para.2-1. As mentioned before, the peak temperature point in the welding process was in the center of the nugget. Node 909 (the center of the welding area) and Node 950 (the contact point of upper electrode and the mild steel) were chosen to record the temperature curves of both nodes. The nodes in the FEM model and the temperature curves are shown in Fig. 6. It can be seen from Fig. 6 that the highest temperature of the center welding area was 1839 C from the temperature curve of Node 909, which was lower about 300 C than the peak temperature in the welding process based on original welding parameters. It can be predicted that the welding spatter phenomenon will also be improved greatly Spatter phenomena in optimized welding The welding temperature distribution (based on Para.2-1) was simulated by SORPAS. Figure 7 recorded the exact time when the welding spatter happened in simulation (in red arrow, 1 cyc=20 ms). It is clearly seen that welding spatter occurred at 708 ms, the 12.4th cycle of the t 2 welding time (There was a pause of 2 cyc between t 1 and t 2 ), at which the welding time was coming to an end. The situation of welding spatter happened at preheating period was prevented. Experimental processes showed that the welding spatters were much smaller than before and proved that the welding spatter just happened at the final time of the welding cycle. The experimental weld nugget and simulative result based on Para.2-1 are compared in Fig. 8. The nugget sizes are very close compared to each other.

8 494 Fig. 6 Temperature curves of Node 909 and Node 950: (a) chosen nodes; (b) temperature curves Fig. 7 Temperature distribution at 707 ms and 708 ms, Para. 2-1: (a) 707 ms of the welding process; (b) 708 ms of the welding process As for Para.2-2, there is a third welding cycle, tempering cycle, in the welding process besides preheating and welding cycle. As the spatter phenomenon motioned in Para.2-1, we assume that there will be welding spatter in the welding cycle even in the tempering cycle of Para.2-2. The weld quality in Para.2-2 should be worse than that in Para Mechanical property comparison Mechanical properties of the weld nuggets were tested after RSW process on the Fig. 8 Comparison between experimental weld nugget and simulative result

9 495 dissimilar steels based on different parameters. Table 4 shows the shear strength and the cross-section strength of the weld nuggets before and after taking the optimized welding parameters. The strength figures in Table 4 testified that no matter which parameters were compared, the mechanical properties of the weld nuggets after taking the optimized parameters were higher. Taking Para.2-1 for a look, the welding shear strength of No. 2-1 was 17.8% and 5.3% higher than original parameters No. 1-2 and No. 1-3 respectively; cross-section strength was 51.9% and 15.3% higher than No. 1-2 and No. 1-3 respectively. Furthermore, welding shear strength of No. 2-1 and No. 2-2 were close (less than 0.6%), but the cross-section strength of No. 2-1 was 8.9% higher than that of No The tempering cycle added on the Para.2-2 was not good for the weld nugget quality. We can conclude that the welding parameter of No. 2-1 was the most suitable parameter for RSW process on UHSS and mild steel in this study. Table 4 Mechanical property comparison of the weld joints No. Current Shear strength Cross-section Note pulse kn strength/kn 1-2 With preheated Triple-pulse Original parameters: big spatter 2-1 With preheated Triple-pulse Optimized parameters: small spatter Microstructure of the weld joint Macroscopic metallograph of the weld joints at the parameters No. 1-2 and No. 2-1 were compared (shown in Fig. 9). It is easy to find that in Fig. 9a, as excessive welding heat input in the No. 1-2 welding process, welding spatter happened and led to shrinkage cavity in the center of the weld nugget. Figure 9b shows the metallograph of the qualified weld nugget with a good elliptic shape at welding parameter No Fig. 9 Macroscopic metallograph of the welding joints: (a) weld nugget at parameter No. 1-2; (b) weld nugget at parameter No. 2-1 Figure 10 shows the microstructures of the weld nugget at welding parameter No.2-1. It can be seen that the microstructures of the weld joint consist of HAZs, soften zones and base metals in the mild steel and in the UHSS respectively and the weld nugget. Figure 10b

10 496 Fig. 10 Microstructures of the weld nugget at parameter No. 2-1: (a) metallographic structure of the weld nugget; (b) weld nugget zone (I in Fig. 10a); (c) HAZ in mild steel (II in Fig. 10a); (d) HAZ in UHSS (III in Fig. 10a); (e) softened zone in mild steel (IV in Fig. 10a); (f) softened zone in UHSS (V in Fig. 10a); (g) base metal of mild steel (VI in Fig. 10a); (h) base metal of UHSS (VII in Fig. 10a)

11 497 shows that the weld nugget zone is mainly composed of lath martensite and residual austenite with columnar crystals. It can be seen from Fig. 10c that HAZ in the mild steel is between the base metal and the nugget. The quantity of the welding heat is reduced along with the distance far from the welding heat source. As a result, the fine grained region in the HAZ is near the base metal for its less welding heat in RSW processand the coarse grained region composed of fine martensite is near the nugget area for more welding heat. The microstructures of the soften zone and the base metal in the mild steel are tempered sorbite and ferrite respectively from Figs. 10e and 10g. As shown in Fig. 10d, HAZ in the UHSS is mainly composed of fine martensite. The soften zone in the UHSS (Fig. 10f) retains large amount of ferrite, sorbite and carbide precipitation, which reduce the mechanical properties and hardness in this area. The cracks of the weld joints in the shear strength and cross-section strength tests always firstly occurred in the soften zone. The base metal microstructure of the UHSS was lath martensite shown in Fig. 10h. 4 Conclusions (1) In this study, a two-dimensional axisymmetric FEM model coupled with thermalmechanical-electric fields was developed to simulate the RSW process on the dissimilar steels of the ultra-high strength hot-stamped steel (BR1500HS) and the mild steel (HC260LAD+Z) by using the FEM software, SORPAS. (2) The simulation results have been validated by the experiments test results. The distribution and change history of the temperature field in the weldments were obtained through the analysis. The welding spatter phenomenon and the nugget offset were predicted precisely. The unnecessary welding cost and time can be saved by getting rid of the incompetent weld parameters through the early numerical simulation. (3) Considering the disadvantages of the original parameters brought, the weld parameters were optimized and modified. The optimized experimental and simulative results both showed that the nugget welding spatter phenomenon was prevented very well. The mechanical properties of the weld joints after optimizing were much better than the original. Finally the welding parameter of No. 2-1 was taken as the most suitable welding parameter in this study. The welding parameter after optimizing has been taken in a practical automotive production and can provide an important guidance for further RSW process on the UHSS and the mild steel. (4) Metallograph of the weld joints show that the microstructure of the qualified weld joint consists of seven areas: HAZs, soften zones and base metals in the mild steel and in the UHSS, respectively, and the weld nugget zone. Weld joint shape is a good elliptic shape. Acknowledgements This work was supported by Shanghai Automotive Industry Technology Development Foundation, China (No. 1102). REFERENCES [1] X.X. Liu, Welding & Joining (5) (2010) 62 (in Chinese) [2] H. Huh and W.J. Kang, J Mater Process Technol 63 (1997) 672

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