Automotive structures are primarily made of flexible sheet metal assemblies. Flexible assemblies are prone to manufacturing variations like springback which may be caused due to non-isotropic material properties from cold rolling, springback in the forming process, and distortion from residual stresses when components are clamped, and spot welded. This paper describes the curation of a large data set for machine learning. The domain is that of flexible assembly manufacturing in multi stages: component stamping, configuring components into sub-assemblies, clamping and joining. The dataset is generated by nonlinear FEA. Due to the size of the data set, the simulation workflow has been automated and designed to produce variety and balance of key parameters. Simulation results are available not just as raw FE deformed (sprung back) geometries and residual stresses at different manufacturing stages, but also in the form of variation zones and fits. The NUMISHEET 1993 U-draw/bending was used a reference for tooling geometry and verification of the forming process. Additional variation in the dataset is obtained by using multiple materials and geometrical dimensions. In summary, the proposed simulation method provides a means of generating a design space of flexible multi-part assemblies for applications such as dataset generation, design optimization, and machine learning.
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Flange wrinkling is often seen in deep-drawing process when the applied blankholding force is too small. This paper investigates the plastic wrinkling of flange under a constant blankholding force. A series of deep-drawing experiments of AA1100-O blanks are conducted with different blankholding forces. The critical cup height and wrinkling wave numbers for each case is established. A reduced-order model of flange wrinkling is developed using the energy method, which is implemented to predict the flange wrinkling of AA1100-O sheet by incrementally updating the flange geometry and material hardening parameters during the drawing process. A deep-drawing finite element model is developed in ABAQUS/standard to simulate the flange wrinkling of AA1100-O blanks under constant blankholding force. The predicted cup height and wave numbers from the finite element model and reduced-order model are compared with the experimental results, which demonstrates the accuracy of the reduced-order model, and its potential application in fast prediction of wrinkling in deep-drawing process.more » « less
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Abstract There are many sources of manufacturing variations in sheet metal assemblies, such as automotive bodies. These include non-isotropic material properties from cold rolling, springback in stamping, and distortion from residual stresses when components are clamped and spot welded. FE simulations have been used to predict these variations in order to better design tooling and processes. Such simulations require expertise in complex, multi-stage nonlinear analysis. We are investigating the feasibility of training machine learning algorithms in order to democratize these types of analyses. This requires the curation of large, validated, and balanced data sets. To this end, we have developed a multi-stage finite element simulation workflow encompassing component stamping and joining with a focus on examining deformations due to springback in two-part assemblies. Three connected simulations comprise the workflow: (1) component stamping with capture of springback, (2) assembly clamping, and (3) assembly joining, then release. The workflow utilizes explicit dynamic finite element analysis (FEA) and includes the transfer of intermediate solutions (geometries/stresses), as well as extraction of key geometric parameters of springback from both component- and assembly-level simulations. The NUMISHEET 1993 U-draw/bending benchmark was referenced for its tooling geometry and utilized for verification of the forming process simulation; variations of material and geometry were also simulated. In summary, this work provides a means of generating a design space of flexible two-part assemblies for applications such as dataset generation, design optimization, and machine learning.
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null (Ed.)Abstract In this paper, results for SS316 L microtube experiments under combined inflation and axial loading for single and multiloading segment deformation paths are presented along with a plasticity model to predict the associated stress and strain paths. The microtube inflation/tension machine, utilized for these experiments, creates biaxial stress states by applying axial tension or compression and internal pressure simultaneously. Two types of loading paths are considered in this paper, proportional (where a single loading path with a given axial:hoop stress ratio is followed) and corner (where an initial pure loading segment, i.e., axial or hoop, is followed by a secondary loading segment in the transverse direction, i.e., either hoop or axial, respectively). The experiments are designed to produce the same final strain state under different deformation paths, resulting in different final stress states. This difference in stress state can affect the material properties of the final part, which can be varied for the intended application, e.g., biomedical hardware, while maintaining the desired geometry. The experiments are replicated in a reasonable way by a material model that combines the Hill 1948 anisotropic yield function and the Hockett–Sherby hardening law. Discussion of the grain size effects during microforming impacting the ability to achieve consistent deformation path results is included.more » « less
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null (Ed.)Manufacturers invested in a diverse array of industries, ranging from automotive to biomedical, are seeking methods to improve material processing in an effort to decrease costs and increase efficiency. Many parts produced by these suppliers require forming operations during their fabrication. Forming processes are innately complex and involve a multitude of parameters affecting the final part in several ways. Examples of these parameters include temperature, strain rate, deformation path, and friction. These parameters influence the final part geometry, strength, surface finish, etc. Previous studies have shown that varying the deformation path during forming can lead to increased formability. However, a fundamental understanding of how to control these paths to optimize the process has yet to be determined. Adding to the complexity, as the forming process is scaled down for micromanufacturing, additional parameters, such as grain size and microstructure transformations, must be considered. In this paper, an analytical model is proposed to calculate strain-paths with one or two loading segments and their associated stress-paths. The model is created for investigations of stainless steel 316L using a microtube inflation/tension testing machine. This machine allows for the implementation of two segment strain-paths through biaxial loading consisting of applied force and internal pressure. The model can be adjusted, based on the desired forming process or available equipment, to output the appropriate parameters for implementation, such as force, displacement, and pressure.more » « less