A Model to Simulate Welding Process (or Additive Manufacturing) (2)

Description

Before starting to talk about welding and additive manufacturing modelling we need to know what are those.

Details about Additive Manufacturing and Welding:

Here we provide a brief description of additive manufacturing and welding to have a better understanding of the process.

Welding:

Many metallic structures in the industry are assembled through some kind of welding process which is composed of heating, melting and solidification using a heat source such as arc, laser, torch or electron beam. The highly localized transient heat and strongly nonlinear temperature fields in both heating and cooling processes cause non-uniform thermal expansion and contraction and thus result in plastic deformation in the weld and surrounding areas. As a result, residual stress, strain and distortion are permanently produced in the welded structures[1]:

High tensile residual stresses are known to promote fracture and fatigue, while compressive residual stresses may induce undesired, and often unpredictable, global or local buckling during or after the welding. It is particularly evident with large and thin panels, as used in the construction of automobile bodies and ships. These adversely affect the fabrication, assembly, and service life of the structures. Therefore, prediction and control of residual stresses and distortion from the welding process are extremely important in the shipbuilding and automotive industry [1]:

Additive manufacturing and WAAM:

Additive manufacturing (AM) of large-scale components requires an insight into the complicated microstructural features. Wire + arc additive manufacturing (WAAM) is by far the most efficient AM process with a relatively higher deposition rate (1-4 kg/h). This enables the manufacturing of large parts up to several metres. This manufacturing process incorporates either gas metal arc welding (GMAW), plasma arc welding or gas tungsten arc welding techniques for metal deposition. Compared to its counterpart powder-based AM techniques, such as selective laser melting (SLM), the WAAM process offers a lower
production cost due to its relatively high deposition rate [2].

A wide range of weldable metals and alloys such as steel nickel and titanium alloys can be employed in this technology. WAAM facilitates the efficient manufacturing of Ti alloys such as Ti-6Al-4V alloy, which is extensively important in the aerospace industry. A substantial reduction in the buy-to-fly ratio from around 10-20 for a conventionally machined component to 1 could be achieved using this process. WAAM incorporates a multi-pass arc welding process, resulting in the efficient manufacturing of
large-scale parts with acceptable mechanical properties at a lower production cost [2].

More details about this product:

There are more than 300 parameters, including power, speed, and printing strategy, which can affect the quality of the as-built part. Expensive and time-consuming trial-and-error is common to determine optimal conditions for the process. An efficient finite element analysis (FEA) tool is helpful in accurately predicting the actual manufacturing process. This reduces the number of costly experimental iterations. Thus, modelling is an important skill in fields related to additive manufacturing and welding processes.

What we offer here:

In this video, you will learn how to use Abaqus for welding and additive manufacturing modelling. This requires to model the thermal model first which was explained in another video. In order to link the thermal model to the mechanical model, we need to define the predefined field. Then, the mechanical model uses the nodal temperature in thermal results as external load. Please note if the process requires a long healing process, we might need to use Python scripting to define the predefined field. We can use the UTEMP subroutine to modify the thermal results as well.  To use subroutine we need to use FORTRAN code and also Abaqus needs to be linked with Intel FORTRAN. For adding material we use the element birth technique by using Python code.

In this video, you will learn how to create a simple welding model to predict the residual stress in part. The video is short so you can learn this in less than 15 minutes and you will have the output and Abaqus CAE file.

We avoid giving too many details so you can easily use the product. Here, you can find the following files:

Abaqus files: CAE, ODB, INP, Python, and JNL

Video files: How to create this model.

For more information please send me an Email:

rezatangestani@hyperlyceum.com

[1] Zhu, X. K., and Y. J. Chao. “Effects of temperature-dependent material properties on welding simulation.” Computers & Structures 80.11 (2002): 967-976.

[2] Tangestani, Reza, et al. “Effects of Vertical and Pinch Rolling on Residual Stress Distributions in Wire and Arc Additively Manufactured Components.” Journal of Materials Engineering and Performance (2020): 1-12.