Stress Field Prediction in Cantilevered Structures Using Convolutional Neural Networks

Nie, Zhenguo; Jiang, Haoliang; Kara, Levent Burak

doi:10.1115/1.4044097

Cited by 160 publications

(77 citation statements)

References 44 publications

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“…Moreover, in order to support the object-oriented modeling (i.e., constructing design by compositing elements) of morphing materials design, we take inspiration from the GCN in [3,26,34] and use graphical representations in this work. Unlike convolutional neural networks (CNN) [27] that require high-resolution voxelization/pixelization, GCN also takes advantage of the model's intrinsic topology to represent them with fewer yet more effective features and make ML models easier to train.…”

Section: Data-driven Simulationmentioning

confidence: 99%

SimuLearn

Yang

Qian

Liu

et al. 2020

Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology

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Morphing materials allow us to create new modalities of interaction and fabrication by leveraging the materials' dynamic behaviors. Yet, despite the ongoing rapid growth of computational tools within this realm, current developments are bottlenecked by the lack of an effective simulation method. As a result, existing design tools must trade-off between speed and accuracy to support a real-time interactive design scenario. In response, we introduce SimuLearn, a data-driven method that combines finite element analysis and machine learning to create real-time (0.61 seconds) and truthful (97% accuracy) morphing material simulators. We use mesh-like 4D printed structures to contextualize this method and prototype design tools to exemplify the design workflows and spaces enabled by a fast and accurate simulation method. Situating this work among existing literature, we believe SimuLearn is a timely addition to the HCI CAD toolbox that can enable the proliferation of morphing materials.

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Section: Data-driven Simulationmentioning

confidence: 99%

SimuLearn

Yang

Qian

Liu

et al. 2020

Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology

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“…The timestamp indicates the spatiotemporal extension of the image-variate information. Equations (19) to (23) show the kernel equations of convolutional LSTM described in [45].…”

Section: Neural Network Modelmentioning

confidence: 99%

“…Nie et al explored a deep learning strategy to accelerate the computation of constitutive relationships [ 23 ]. Convolutional neural networks (CNNs) are beneficial in determining the FEA stress field regarding a two-dimensional cantilevered structure.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Calibration of Static FEA Computations Based on Terrestrial Laser Scanning Reference

Bao

Chen

et al. 2020

Sensors

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The demand for efficient and accurate finite element analysis (FEA) is becoming more prevalent with the increase in advanced calibration technologies and sensor-based monitoring methods. The current research explores a deep learning-based methodology to calibrate FEA results. The utilization of monitoring reference results from measurements, e.g., terrestrial laser scanning, can help to capture the actual features in the static loading process. We learn the deviation sequence results between the standard FEA computations with the simplified geometry and refined reference values by the long short-term memory method. The complex changing principles in different deviations are trained and captured effectively in the training process of deep learning. Hence, we generate the FEA sequence results corresponding to next adjacent loading steps. The final FEA computations are calibrated by the threshold control. The calibration reduces the mean square errors of the FEA future sequence results significantly. This strengthens the calibration depth. Consequently, the calibration of FEA computations with deep learning can play a helpful role in the prediction and monitoring problems regarding the future structural behaviors.

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“…Nash et al 13 reviewed the most recent deep learning methods for detection, modeling, and planning for material deterioration. Nie et al 14 used Encoder-Decoder Structure based on Convolutional Neural Network (CNN) to generate the stress field in cantilevered structures. However, these methods do not consider temporal dynamics of the stress field or fracture within the material.…”

Section: Introductionmentioning

confidence: 99%

StressNet - Deep learning to predict stress with fracture propagation in brittle materials

et al. 2021

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Catastrophic failure in brittle materials is often due to the rapid growth and coalescence of cracks aided by high internal stresses. Hence, accurate prediction of maximum internal stress is critical to predicting time to failure and improving the fracture resistance and reliability of materials. Existing high-fidelity methods, such as the Finite-Discrete Element Model (FDEM), are limited by their high computational cost. Therefore, to reduce computational cost while preserving accuracy, a deep learning model, StressNet, is proposed to predict the entire sequence of maximum internal stress based on fracture propagation and the initial stress data. More specifically, the Temporal Independent Convolutional Neural Network (TI-CNN) is designed to capture the spatial features of fractures like fracture path and spall regions, and the Bidirectional Long Short-term Memory (Bi-LSTM) Network is adapted to capture the temporal features. By fusing these features, the evolution in time of the maximum internal stress can be accurately predicted. Moreover, an adaptive loss function is designed by dynamically integrating the Mean Squared Error (MSE) and the Mean Absolute Percentage Error (MAPE), to reflect the fluctuations in maximum internal stress. After training, the proposed model is able to compute accurate multi-step predictions of maximum internal stress in approximately 20 seconds, as compared to the FDEM run time of 4 h, with an average MAPE of 2% relative to test data.

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Stress Field Prediction in Cantilevered Structures Using Convolutional Neural Networks

Cited by 160 publications

References 44 publications

SimuLearn

SimuLearn

Intelligent Calibration of Static FEA Computations Based on Terrestrial Laser Scanning Reference

StressNet - Deep learning to predict stress with fracture propagation in brittle materials

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