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Abstract Sheet metal stamped and welded assemblies, such as the ones used in automotive body-in-white (BIW) structures, have various sources of manufacturing variations during stamping and assembly processes. One of the major contributors to these variations is the springback on clamping release due to elastic recovery. Mitigating these variations requires expert knowledge of mechanical behavior, tooling, and process design. No analytical models can be used for the variety of geometries. Nonlinear FEA is also being used to predict springback, but it is time-consuming and requires specialized expertise, which makes it difficult to use in design exploration. Machine learning holds the promise of democratizing such complex analyses. This paper presents several case studies for data curation/generation, ML training, and validation. The prediction and quantification of the effects of springback are done on two levels: (i) low granularity, which involves predicting variations in certain parameters that are critical to measuring and understand spring back, and (ii) high granularity, predicting the shape of the component while taking into account the effects of springback and the stresses in the components. The data required to train, test, and validate the ML models were generated previously using an automated, integrated multi-stage simulation approach that was necessary to produce large datasets. Stamping simulations were validated against NUMISHEET benchmarks and also compared to test results published by other researchers. Subsequently, machine learning models were trained on the curated dataset to predict 2D stamped component shapes after springback and stress distributions across these shapes. For the assembly dataset, parameters such as unconstrained planar minimum zone magnitudes, angles between component planes, and twist angles are predicted using machine learning models, including linear and polynomial regression, decision trees, gradient boosting regression, support vector regression, and fully connected neural networks, and compared for their performance using consistent metrics. Hyper-parameter tuning is performed to optimize model performance, with artificial neural networks demonstrating promising capabilities in understanding variations in forming and multi-stage assembly processes. 

Abstract PurposeTo demonstrate speech‐production real‐time MRI (RT‐MRI) using a contemporary 0.55T system, and to identify opportunities for improved performance compared with conventional field strengths. MethodsExperiments were performed on healthy adult volunteers using a 0.55T MRI system with high‐performance gradients and a custom 8‐channel upper airway coil. Imaging was performed using spiral‐based balancedSSFPand gradient‐recalled echo (GRE) pulse sequences using a temporal finite‐difference constrained reconstruction. Speech‐production RT‐MRI was performed with three spiral readout durations (8.90, 5.58, and 3.48 ms) to determine trade‐offs with respect to articulator contrast, blurring, banding artifacts, and overall image quality. ResultsBoth spiral GRE and bSSFP captured tongue boundary dynamics during rapid consonant‐vowel syllables. Although bSSFP provided substantially higher SNR in all vocal tract articulators than GRE, it suffered from banding artifacts at TR > 10.9 ms. Spiral bSSFP with the shortest readout duration (3.48 ms, TR = 5.30 ms) had the best image quality, with a 1.54‐times boost in SNR compared with an equivalent GRE sequence. Longer readout durations led to increased SNR efficiency and blurring in both bSSFP and GRE. ConclusionHigh‐performance 0.55T MRI systems can be used for speech‐production RT‐MRI. Spiral bSSFP can be used without suffering from banding artifacts in vocal tract articulators, provide better SNR efficiency, and have better image quality than what is typically achieved at 1.5 T or 3 T. 

Abstract 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. 

Abstract Fetal magnetic resonance imaging (MRI) is an important adjunct modality for the evaluation of fetal abnormalities. Recently, low-field MRI systems at 0.55 Tesla have become available which can produce images on par with 1.5 Tesla systems but with lower power deposition, acoustic noise, and artifact. In this article, we describe a technical innovation using low-field MRI to perform diagnostic quality fetal MRI.