Generic approach to plausibility checks for structural mechanics with deep learning

The simulation of product behavior is a vital part in virtual product development, but currently there is no tool or method available that can examine the quality of FE simulations and decide automatically on whether a simulation is plausible or non-plausible. In the paper a method is presented that enables automatic plausibility checks on basis of empirical simulation datasets. Nodal simulation data is transformed to numerical arrays, of fixed size, using virtual spherical detector surfaces. Afterwards the arrays are used to train a Deep Convolutional Neural Network (AlexNet). The Neural Network can then be used for plausibility checks of FE simulations (structural mechanics). In a first application a Deep Convolutional Neural Network is trained with simulation data of a demonstrator part, the rail of speed inline skates. After the GPU training of the Neural Network, further simulations are evaluated with the net. These simulations were not part of the training data and are used to calculate the prediction quality of the Neural Network. This approach is to support development engineers during design accompanying FEA in virtual product development.

[1]  W S McCulloch,et al.  A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.

[2]  Thomas A. Runkler,et al.  Data Mining : Methoden und Algorithmen intelligenter Datenanalyse ; mit 7 Tabellen , 2010 .

[3]  Geoffrey Zweig,et al.  An introduction to computational networks and the computational network toolkit (invited talk) , 2014, INTERSPEECH.

[4]  Jürgen Adamy.  Fuzzy Logik, neuronale Netze und evolutionäre Algorithmen , 2005 .

[5]  K. Tischler.  Informationsfusion für die kooperative Umfeldwahrnehmung vernetzter Fahrzeuge , 2013 .

[6]  Tobias C. Spruegel,et al.  CONCEPT AND APPLICATION OF AUTOMATIC PART-RECOGNITION WITH ARTIFICIAL NEURAL NETWORKS FOR FE SIMULATIONS , 2015 .

[7]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[8]  Geoffrey E. Hinton,et al.  To recognize shapes, first learn to generate images. , 2007, Progress in brain research.

[9]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[10]  Yoshua Bengio,et al.  Convolutional networks for images, speech, and time series , 1998 .

[11]  ImageNet Classification with Deep Convolutional Neural , 2013 .

[12]  Li Deng,et al.  A tutorial survey of architectures, algorithms, and applications for deep learning , 2014, APSIPA Transactions on Signal and Information Processing.

[13]  Georg Jacobs,et al.  Dynamic simulation of cylindrical roller bearings , 2014 .

[14]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[15]  David M. W. Powers,et al.  Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation , 2011, ArXiv.

[16]  Kurt Wiener,et al.  Anatomie und Physiologie , 2005, Archiv für Dermatologie und Syphilis.

[17]  Bernard Widrow,et al.  30 years of adaptive neural networks: perceptron, Madaline, and backpropagation , 1990, Proc. IEEE.

[18]  Gabriele Taentzer,et al.  Modeling with Plausibility Checking: Inspecting Favorable and Critical Signs for Consistency between Control Flow and Functional Behavior , 2011, FASE.

[19]  Henning Beck,et al.  Faszinierendes Gehirn : eine bebilderte Reise in die Welt der Nervenzellen , 2018 .

[20]  Beat Fasel,et al.  Robust face analysis using convolutional neural networks , 2002, Object recognition supported by user interaction for service robots.

[21]  Olof Bryngdahl.  Größenschätzung des rezeptiven Feldzentrums der menschlichen Retina , 2004, Pflüger's Archiv für die gesamte Physiologie des Menschen und der Tiere.