| Abstract Scope |
The process for overcoming distortion in a manufacturing R&D environment, such as welding or additive manufacturing, typically relies on iterative weld development. Although this process is generally adequate for producing sound welds, it can be expensive, lengthy, and inflexible to changing part designs. Changes in geometry, material, or process require repeat experimental evaluation as there is no simple or direct correlation that can be used to predict weld distortion. Modern tools, such as finite element modeling (FEM), can be used to improve this process but are typically reserved for complex systems or for high fidelity studies. The goal of this work was to implement appropriate FEM and other computational tools into a manufacturing environment in order to improve the understanding of residual stresses and distortions in welds. By considering the applicable and most consequential physical phenomena and material properties, rather than all possible data, these models can reduce the costs and effort associated with weld development while maintaining the speed and flexibility need in a manufacturing environment. This talk will discuss the development of improved in situ data collection, distortion measurement techniques, and the unique computational tools that have been deployed. Experimentally, 5052 Aluminum and 304L Stainless Steel were welded with gas tungsten arc welding and electron beam welding to examine the effects of material properties and heat input effects on distortion. Computationally, methods and results from standalone FEM, Abaqus Welding Interface (AWI), and other modeling tools will be discussed. Comparisons between experimental and modeling results will be presented, along with an assessment of the model with respect to accuracy and usability. Although these tools are being incrementally integrated into the R&D process, continued adoption is expected to greatly decrease the iterations required for weld development. This talk will also provide context for future applications, such as additive manufacturing. |