Abstract

Undesired stress hot spots caused by incorrect modeling practices often result in failed analyses, longer runtimes or inaccurate results. Technologies such as error indicators, discontinuous stress plots or stress hot spot detection can identify stress concentration but cannot classify it by the type or impact to the analysis. Hence requiring expensive mesh convergence analyses or expert revision that slows down the production process. In this Article we propose a novel technique where machine learning, paired with image recognition, is used to identify and classify stress concentration by analyzing the fine subtleties that exist between different stress field results. In particular, we focus on the problem of contact stress hot spots caused by contact point loads against smoothly distributed stress fields. Using a convolutional deep neural network (CNN) in a proof of concept scenario, we evaluate the technology under different stress concentration patterns with varying mesh densities and find a high level of accuracy (97.5%) at identifying stress concentration from contact point loads over 5-fold cross validation testing. The CNN is able to differentiate stress localization patterns generated from sharp corners or boundary localization from those associated with contact point loads. By using open source neural network libraries (e.g. Keras with Tensorflow) we also find that properly trained neural networks replace the complex rules needed for automatic post-processing (which are usually problem dependent) and provides a unified framework that is easy to maintain and expand. Finally, we discuss how the method can be applied as an assistant technology by experienced or inexperienced engineers for the early detection of underlying issues with FEA in simple or assembled components. Also, how some of the limitations imposed by the use of image recognition as feature mapping can be improved with a distortion grid mapping technique capable of analyzing stress in the bulk of the solid, as well as handling highly distorted geometries on complex parts.