Welding process finite element analysis (FEA) enables users to understand and predict “as manufactured” geometry, process induced residual stresses, strains and material states. It also enables users to virtually optimize metal forming and welding processes and design-for-manufacturability to achieve desired component characteristics after the manufacturing is complete.
Here, the importance of making welding simulation software more accessible to welding and manufacturing engineers is explained. Additionally, potential effects of integrating welding process FEA into the product development and launch process are discussed.
Welding is the predominant joining method utilized in metallic automotive and industrial applications. A wide range of technologies make welding suitable for many applications. Typically, sheet metal components are formed into their desired shapes in a metal forming process and then subsequently assembled using a welding or joining process. Each successive manufacturing process influences the resulting assembly’s physical characteristics, which may affect its ability to fit and function as desired.
The welding process by nature can have an undesirable effect on the welded assembly including resulting distortions, residual stresses and metallurgical states. The melting and subsequent cooling of weld metal results in residual stress fields in and around the weld.1 These residual stresses result in localized plastic deformation that contributes to global distortion. In sheet metal applications, weld-induced distortion is a critical consideration during the manufacturing engineering process as it can lead to a loss of dimensional control, expensive rework and production delays.2
Distortion and residual stress within welded sheet metal assemblies are largely influenced by welding process parameters, including energy input, weld sequence, heat source travel speed, clamping/fixturing approach, etc. Accordingly, residual stress and distortion mitigation requires simultaneous optimization of multiple parameters. By trial and error-based experiments, this process can be expensive and time consuming and does not guarantee success.3
Understanding the effects and interdependence of each parameter enables the user to more effectively manage the conditions that drive residual stress and distortion. However, the tools available to the majority of engineers to evaluate the complex transient and nonlinear phenomenon inherent in welding processes are very limited.4
Accessibility of FEA
Welding process FEA is a software tool that enables users to evaluate proposed welding process designs in order to understand how they would contribute to the characteristics of a welded assembly. Furthermore, welding process FEA can be used to improve the component and welding process design while reducing the required number of physical tryouts and prototypes. Much the same way as stamping and metal forming simulation software has improved the way engineers perform die and forming process design, welding simulation has the capability to significantly improve the product development and manufacturing (welding) engineering process.
In general, welding process FEA has been limited to academia and research organizations. This is largely because the only tools capable of performing the analysis are general-purpose FEA solvers such as Abaqus, Ansys or MSC Marc. These general-purpose FEA solvers require advanced modeling techniques performed by an FEA expert. As a result, only a relatively few highly technical analysts with several years of FEA experience can effectively utilize general purpose solvers to simulate welding.
Over the past several years, new software tools like Simufact Welding have significantly improved accessibility to welding simulation. While still operating with the MSC Marc general-purpose solver at its core to maintain the capability to consider the complete physics, the graphical user interface (GUI) was designed to be accessible to the manufacturing process expert. This improved accessibility is made possible through the utilization of intuitive templates for each part of the manufacturing process found on the shop floor, including heat sources, weld trajectories, weld filler geometry, clamps, fixtures, etc.
More technical aspects of FEA are also streamlined in Simufact Welding. Whereas previous FEA tools required coincident nodes, prescriptive contact and friction conditions, and manual mesh refinement, this modern software reduces the amount of manual user input by applying advanced algorithms to semi-automate or fully automate those functionalities.
Making workflow easier
The improved software usability consequently reduces the requirement for users to have advanced education, training and experience in order to effectively employ the software. This also shortens the learning curve and reduces the cost of training new employees to use the tool. The streamlined workflow and modeling capability significantly reduces the modeling time required as compared to general-purpose solver-based welding simulation software.
By making welding process FEA more accessible, it puts the tool in the hands of welding engineers, who are the best-equipped to effectively utilize the information the tool provides. Instead of requiring the welding engineer to explain to the FEA analyst how to interpret and apply welding concepts like heat source geometry, weld sequencing and clamping strategies, the welding engineer can apply these concepts directly within the intuitive user interface that is designed to mirror how the process is set up on the manufacturing shop floor.
The effect is a reduction or elimination of communication “loops” that were previously required between the welding engineer and the FEA analyst. Increasing accessibility of welding process FEA also means that welding engineers can now take their simulation results directly to the shop floor without support from the FEA analyst. The ability to expose engineers, welders and technicians directly to the results of welding simulation shortens the technical learning curve associated with becoming an experienced welding industry professional.
It does so by enabling these individuals to visualize residual stress propagation and resulting distortion as it relates to weld sequence and clamping conditions. It also allows them to see the effects of the welding parameters on the heat-affected zone (HAZ) and local metallurgy and material properties. Learning that traditionally occurred over a period of decades solely through experience and observation of countless welding jobs can now be accelerated due to the availability of welding simulation tools.
In a period where the skilled trades workforce is facing a skills gap as the aging population approaches retirement, simulation can help the younger generation of welders and engineers gain the required proficiency.
Integrating into product development
Integrating manufacturing process FEA into the product development cycle has been widely accepted as the standard for several years in the metal forming industry. Products like Simufact Forming, Autoform and Deform, are now common place in most companies conducting metal forming operations.
Those companies that have incorporated welding simulation have been able to gain understanding about the effects of the welding process on assembly characteristics and the ability of those assemblies to fit and function as desired. To better understand the implications of incorporating welding process FEA into product development, it is useful to compare traditional physical validation with virtual validation approaches.
In Figure 1, the red curve represents the degree of manufacturing process validation available from a traditional validation approach throughout product development. The degree of manufacturing process validation is relatively low through the concept phase and into prototype. It is only at the end of prototype and into pilot series that the degree of validation ramps up.
The challenge is that by nature, the prototype process is an expensive and time-consuming activity. A basic prerequisite for prototype weld assemblies is the availability of formed metal parts to join together. Depending on when feedback from the prototype assembly process is available, the cost of making design changes can be very high.5 In some cases, the design becomes effectively “locked” and changes become unfeasible. In this case, engineers are forced to deal with the issues identified during the prototype process in other less effective ways.
Performing virtual validation through simulation enables engineers to attain a higher degree of manufacturing process validation earlier in product development. There are many examples in various industries that demonstrate the ability of simulation tools to improve the product development process.
The green curve in Figure 1 depicts the degree of validation available through the application of virtual validation throughout product development. Most notably, the degree of validation is higher when entering the prototype process. At this point in product development, making changes to designs is much less expensive and painful than if the change was initiated later.
Figure 2 illustrates this point with a case study showing a Volkswagen auto body side. In this example, welding process FEA indicated that welding the b-pillar to the bodyside would result in a distortion of approximately 1.5 mm from nominal. Based on this feedback, engineers were able to cut the b-pillar stamping die geometry to compensate for the expected weld induced distortion. Upon welding, the b-pillar then distorted into the desired final shape.
In addition to mitigating negative effects of the manufacturing process, welding process FEA provides a detailed understanding of the “as manufactured” part. This enhances the engineers’ ability to perform advanced engineering tasks, including exploiting positive effects of the manufacturing process and incorporating accurate component characteristics into subsequent functional analysis.
Welding process FEA has advanced significantly in the last several years. Software tools are now available that can enable users without extensive FEA experience to utilize simulation to improve the manufacturing process. These tools can provide engineers insight previously unavailable within the constraints of a typical commercial product development cycle. A simple and efficient welding process FEA workflow makes it feasible for engineers to incorporate the analysis into product development.
By chaining the results of multiple manufacturing processes with a functional analysis, engineers can reduce the number of assumptions required when conducting functional analysis. As a result, a more accurate and realistic assessment of how parts and assemblies will behave when put into service is possible. For the automotive industry and others like it, this capability will support the continuous drive for lighter weight and improved performance.
- Radaj, D., 2002, Eigenspannungen und Verzug beim Schweißen – Rechen- und Messverfahren. DVS-Verlag, Düsseldorf.
- Michaleris, P., 2011, Minimization of Welding Distortion and Buckling, Woodhead Publishing.
- Islam, M., Rohbrecht, J., Buijk, A., Namazi, E., Liu, B. Kurosu, T., Motoyama, K., 2013, Computational Optimization of Arc Welding Parameters Using Coupled Genetic Algorithms and Finite Element Method, ASME Proceedings of IMECE, San Diego.
- Schafstall, H., 2013, The Virtual Weld, Blechnet Magazine.
- Wiethop P., Thater R., 2015, Schweißverzugssimulation im Karosseriebau; Proceedings 16. Round Table Simulating Manufacturing, Marburg.
- Omboko Y., Böhm S., Verhoeven H., Weigert P., Kurz H., 2013, Numerisches Kompensationsverfahren zur Ermittlung der Vorhaltung von Schweißverzügen, Proceedings 14. RoundTable Simulation in der Umformtechnik, Wiesloch.