simulation and optimization of large welded structures

SIMULATION AND OPTIMIZATION OF LARGE WELDED STRUCTURES Rakesh Goyal1,a, Satyam Sahay1, Eric Johnson2 and Mohamad El-Zein2 1

John Deere Asia Technology Innovation Center, Magarpatta city, Pune, India – 411013 2 Moline Technology Innovation Center, Deere and Company, Moline, IL 61265, USA a [email protected]

Abstract: Welding technology is one of the main joining techniques used for fabricating parts and assembly for agriculture, construction and forestry equipments. However, welding presents a number of technical challenges to the designer, manufacturer, and end-user of the welded structures. The complex thermal cycles from welding result in formation of residual stresses in the joint region, and distortion of the welded structure. Both weld residual stress and distortion can significantly impair the performance and reliability of the welded structures. For example, not including residual stress in the engineering stage could significantly reduce the fatigue life of a component, which is one of the dominant modes of failures of the welded structures. From the manufacturing perspective, fixture design becomes a major issue, which is generally designed through heuristic methods and experimental trails. Although, extensive research has been done on modeling and simulation on welding of simple structure, there has been very little work on simulating the large structures. In the present work, a simulation tool has been used to accurately capture the thermal-microstructure-stress changes during the welding process. This tool simulates the transient 3D temperature field, the evolution of microstructure in low alloy steel welds, transient 3D displacement, and stress and strain in the structure as it is being welded. This tool has been extensively validated for its prediction capabilities, including neutron diffraction. This article will detail on the validation effort as well as show the effectiveness of solving some of the industrial problems such as distortion, fatigue failures, welding sequence and welding fixture design for large structures. Finally, challenges and gap areas in this area will be highlighted. Keywords: Computational weld mechanics, welding simulation, large welded structures, residual stress, and distortion

1. Introduction: Fusion welding processes are widely used for fabrications in many engineering applications such as aerospace, automotive and shipbuilding industries. A metal inert gas welding process consists of heating,

melting and solidification of parent metals and a filler material in localized fusion zone by a transient heat source to form a joint between the parent metals. The heat source causes highly non-uniform temperature distributions across the joint and the parent metals. Therefore, the thermal expansion and contraction during heating and subsequently cooling as well as material plastic deformation at elevated temperatures result in inevitable distortions and residual stresses [1,2] in the joint and the parent metals, which greatly affects the fabrication tolerance and quality. In the current industrial practice, welding processes are developed largely based on trial and error experiments incorporating with engineer’s knowledge and experience of previous similar designs. Simulation tools based on finite element (FE) method are very useful to predict welding distortions and residual stresses [3] at the early stage of product design and welding process development. However, the complexity of welding processes and the complex geometry of real engineering components have made the prediction of welding distortions and residual stresses a very difficult task. FE methods such as coupled/decoupling thermal and mechanical analysis for local and global models of complex structures [4–6] have been developed to reduce solution time with sufficient accuracy. It is clear that the development of effective simulation tools requires an accurate analysis of thermal history during welding and a good understanding of the effect of process parameters on temperature distributions and variations. The research on welding heat source models dates back to early 1940s and Rosenthal [7] first proposed a mathematical model of the moving heat source under the assumptions of quasistationary state and concentrated point heating in the 3D analysis. In the late 1960s, Pavelec et al. [8] suggested a circular disc heat source model with Gaussian distribution of heat flux on the surface of the work piece. These heat source models and some simplified models have been widely used in welding simulation for prediction of the distortions and residual stresses [5,9,10]. The first welding numerical simulations made their appearance in the early 1970s, for example, in the work of Ueda et al [11] and Hibbit et al [12]. These analyses were greatly simplified relative to the real situations. In particular, the welding simulations were confined to analyses based on two-dimensional cross-sections. The

results gave indications of the welding residual stresses evolved in quasi-static, plane strain situations but did not give a picture of the total out-of-plane deformations. Following increases in computer power, more complex aspects of welding have now been successfully investigated in greater detail by many researchers. A detailed literature review of finite element analyses and welding simulations presented between 1976–1996 [13] and 1996–2001 [14] has been compiled by Mackerle. Optimization of the welding sequence and process is one way to limit the use of clamping tools to reduce the cost and facilitate the automation of assembly lines. However, experimental optimization requires prototyping and measurements which are extremely expensive and time consuming and finally, very few solutions can be used. Finite element simulations can be used in that aim but the difficulty is, on one hand, that welding processes involve complex physical phenomena and, on the other hand, that where local models are sufficient to predict stresses, only global 3D models can correctly evaluate distortions [15]. Residual stresses were modeled using commercial FEA package such as Ansys for butt welded plate [16]. Similar work was carried out to model residual stresses in weld joint of HQ130 grade high strength steel using Ansys [17]. Viorel developed similar methodology based on a thermo-elastic-plastic FE analysis and simulated simple butt welded joint using the ANSYS computer code [18]. Gery et al developed a C++ program in order to implement heat inputs into finite element thermal simulation of the simple butt joint, approach used moving heat source model based on Goldak’s double-ellipsoid heat flux distribution [19]. An uncoupled thermomechanical FE analysis, using the ABAQUS code was carried out to determine the distribution of residual stress at the cylinder-to-nozzle junction weld. Only the axissymmetric model was simulated due to impractically to run full model [20]. Drawback of these codes is that they do not have the capability to handle complex geometry from real structures and also they do not capture the micro-structural transformations and their effects during welding. Some of these proposed models were not proven against experiment for their capability. Rong-Hua Yeh et al. investigated the temperature distribution of aluminum plates welded by gas tungsten arc welding; attempt was limited to transient temperature estimation [21]. Simple Laboratory and shipyard Mock-up structures were simulated using 2D and 3D models [22]. Multipass welding of a 316L stainless steel pipe was simulated by Duranton et al. [23] Dike et al carried out three dimensional finite element simulations of thermal and mechanical response of a 304L stainless steel pipe subjected to a circumferential autogenous gas tungsten arc weld to predict residual stresses in the pipe [24]. Model used simple geometry and load history, excluded

material properties variation and didn’t made reference to real boundary conditions. Recent work by Camilleri et al. aimed to improve the applicability of computational distortion prediction by providing simple adaptable methodologies with an effort to produce economic and robust distortion simulation strategies. Basic approach involved uncoupled computational methods, whereby the thermal transient, thermo-elastic-plastic and overall structural stages of the thermo-mechanical welding process were treated separately. Also the transient problem was reduced to a static, single load-step analysis. Three efficient models were identified that reduce the transient analysis to a simple multi-load-step analysis and these were applied to sample butt-welded plates. Less attention was given to simulation of residual stress fields as it was observed that a full transient thermo-elastoplastic analysis is required if such information is required [25]. Souloumiac et al. developed a local/global approach in order to determine the welding residual distortions of large structures. It is assumed that plastic strains induced by the welding process are located close to the welding path and only depend on local thermal and mechanical conditions. The plastic strains obtained by the local model are then projected to a complete shell of whole structure as initial strain. Drawbacks of this method is that due to many different weld joint configurations in a large welded structure, it requires large number of local models, further how the effects of welding fixture on large structure is transferred from local models is not well understood [26]. In the present work, a welding simulation model has been developed to predict temperature, microstructure, phases, residual stresses and distortion during welding process which accounts properly for any variables. The analysis accounts for transient thermal effects because of the localized, non-uniform and dynamic nature of the heat input. The heat distribution, heating and cooling rates which affect the microstructure of the weld and the heataffected-zone are accounted. The thermal and microstructure history which, in turn, affects the stress distribution in the model are also accounted. This model has the capability to account for many variables. These include accurately defining the material properties, welding parameters, welding sequence and boundary conditions that include tack welds and constraints. It also provides the capability to create a mesh and define time stepping in a way that can accurately capture the thermal, microstructure and stress history of the welding process. Model has been validated extensively for its prediction capabilities with the literature benchmark, experimental set up at lab scale and measurements from large real life welded structures. Further, this model has been successfully used at design stage for robust product design to eliminate the issues which could turn out

expensive at later stage. Also this has been used to help designers to select appropriate design, welding sequence and welding fixtures.

2. Welding Simulation Model: Simulation tool solves the coupled equations for the conservation of energy, mass and momentum for a structure being welded. Complex equations are solved by using the mathematics of transient non-linear FEM and the evolution of microstructure. Simulation tool uses the double ellipsoidal power density distribution of heat source model [19] below the welding arc, which can accurately simulate different types of welding processes with shallow and deep penetration.

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This tool enables a designer to simulate the transient 3D temperature field, the evolution of microstructure in low alloy steel welds, transient 3D displacement, and stress and strain in the structure as it is being welded. Inputs for the simulation include stereolithographic (STL) files for the parts being welded, the set of weld procedures and the weld path for each joint and material properties for the materials welded and the boundary conditions. For thermal analysis the boundary conditions are chosen from prescribed temperatures, prescribed power density, prescribed thermal fluxes and convection cooling applied during the welding process. Microstructures are modeled using the algorithms described in Watt et al [27] and Henwood et al [28]. For stress analysis, the boundary conditions are prescribed displacement constraints (vice and tack welds) [29].

b) Fig. 1 Thermal validation on double fillet T-joint: a) Transient temp measurement b) Comparison of thermal profile - simulation vs. measured

3. Model validation: Welding simulation tool was validated for it prediction capabilities. Validation was done in three stages for thermal, distortion and residual stresses. Thermal validation involved measuring the full transient temperature field on the backside of base plate of a double fillet T-joint. Thermocouples mounted on the back side of base plate recorded online temperature history during the welding as well as cooling of the weld joint. Same test conditions were simulated with tool and thermal predictions were found to correlate well with the measurements, Fig. 1. During this work model equation was developed for transient heat transfer co-efficient, which is an integral part of the software. Distortion validation was conducted by measuring distortion during and after the welding on the backside of base plate of a single fillet T-Joint, using ARAMIS camera which is 3D optical deformation analysis system. Simulated distortion matched pretty well with the measurements, Fig. 2.

Fig. 2 Distortion validation on single fillet T-joint: Comparison of deformation - measured vs. simulation

Distortion validation was also done for the real life large welded structure, because on the one hand it’s easier to measure distortion compared to temperature or residual stress profile, on the other hand design and shop floor personals believe in the computation techniques better when it’s proven for real structures rather than just for lab samples, which are done in a more controlled environment. Big frame around 4 m in length was selected for this purpose, having around 50 no. of welds. Max distortion of around 6 mm was simulated near the tip of the frame, which was exactly the value found using CMM measurement, Figs. 3a and 3b. Further, distortion was measured at 20 different locations across the length, width and height of this fame using CMM and was very well correlated with simulated distortion data, Fig. 3c.

4. Case study 1 - Design using welding simulation: A large frame was selected for this case study. Successful effort was made to arrive at simulation assisted design reducing 70% of the distortion from the initial design. This structure involved more than 100 welds. Welding simulation was used to evaluate the different designs from distortion prospective, which is major concern for such component. Fig. 4 shows distortion results for two different designs; only difference in the two designs is that, four spokes around the central hub are rotated by 45 degrees. It can be observed that such a design change will not only results in different value of distortion, but also shift the maximum distortion location from corners to one of the sides, which is not acceptable for this component. It can be clearly seen the amount of efforts, resources, cost and time which can be saved towards building prototypes to arrive at such a niche design. Simulation provides the capability to test as many designs sufficient for fulfilling the design and manufacturing needs, at much lower cost. Validation of the tool shows that not only this can be used for relative comparisons of various designs, but for absolute design purposes.

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c) Fig. 3 Distortion validation on large welded structure a) Total distortion plot-mesh represents original geometry b) Simulated distortion at the frame tip vs. time c) Distortion comparison - Simulation vs. measurement

Fig. 4 Distortion prediction for large welded structure – Simulation assisted design selection

5. Case study 2 - Welding sequence optimization: Welding sequence plays an important role from distortion point of view for the large and complex welded structures. For illustration purposes, a simple welded pulley with two 360 degree welds is considered here in this paper, figure 5 (a). As pulley has only two welds, maximum of two different welding sequences are feasible (assuming that there is no change of welding direction). Simulation showed that second welding sequence resulted in 35% less distortion compared to first one, 0.168mm vs. 0.259mm; Figs. 5b and 5c.

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Comparison presented here clearly shows the need of optimizing welding sequence, especially for the large, complex and critical weldments. Optimizing welding sequence through simulations provides an excellent opportunity at much lower cost and time.

6. Challenges and gap areas: Biggest challenge for doing full 3D transient simulation is the simulation run time. For large welded structures having hundreds of welds with more than 60,000 elements, it may take around two weeks of simulation time, which could be justified for new product development, but for the productions parts, it is not acceptable time. Currently efforts are going on to make the simulation faster by using different strategies such as having the capability to run the simulation with the help of parallel processors.

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Other challenges include collecting the accurate inputs for running welding simulation. As mentioned earlier, it is required to have temperature dependent material properties, not only for the basic material but also for its different phases at which it exists at different temperatures. Currently such a data base is not available, even for most of the commonly used materials and there is need to develop such types of data bases.

c) Fig. 5 Effect of welding sequence on distortion: a) Welded pulley with two 360 degree welds b) Sequence1 max distortion - 0.259mm c) Sequence2 max distortion - 0.168mm

7. Summary: Welding simulation has matured from research era to real application stage. In coming years, this will be used as regular tool in industries, during early design cycle due to numerous advantages offered by the technology. Welding simulation is not limited to researchers now, for simulating simple welded joint but this can be used to design large welded structures at the shop floor. It is very helpful for reduction in physical trials and prototypes using virtual designs and hence resulting into savings of energy, money, efforts and accelerated product design. Improvement in quality by distortion reduction can be achieved, minimizing the assembly problems. Safer and robust designs with better confidence can be built by including residual stress in life-cycle prediction. It provides the possibility of process parameter optimization. It is feasible now to ascertain optimum processing regime. Although this technology offers few challenges such as, unavailability of accurate inputs e.g. temperature dependent material properties, difficult to use optimization techniques because of long simulation time for large welded structures. Continuous research efforts are being put into this area to reduce the solve time.

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