PermeabilityNets: Comparing Neural Network Architectures on a Sequence-to-Instance Task in CFRP Manufacturing

Stieber, Simon; Schröter, Niklas; Fauster, Ewald; Schiendorfer, Alexander; Reif, Wolfgang

doi:10.1109/icmla52953.2021.00116

Cited by 5 publications

(11 citation statements)

References 6 publications

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“…The component can be regarded as acceptable if it does not exhibit excessive fluctuations in permeability and ϕ f . This paper substantially extends preliminary results discussed in [6] 1 : In order to collect real injection data, we conducted additional physical experiments. We used real data not only for evaluation but also for training purposes and measured the quality effects.…”

Section: Rtm and Permeability Measurementssupporting

confidence: 60%

“…Based on Stieber et al's work [6], which involved using networks trained on simulated data for inference on real data, we wanted to investigate the following research questions:…”

Section: Discussionmentioning

confidence: 99%

“…To obtain data from simulation runs, briefly termed the simulated data, a strategy comparable to previous works of the authors, in particular, [3] and [6], was followed involving an automated pipeline: Starting with a two-dimensional representation of a preform with anisotropic material properties, briefly termed preform, small patches (rectangular or circular) with varying in-plane permeability and ϕ f were randomly inserted. Subsequently, fluid flow through the preform was numerically predicted through a commercial RTM simulation software, resulting in a sequence of "label images", or briefly labels (see Fig.…”

Section: Data From Flow Simulation Runsmentioning

confidence: 99%

“…We improved all models compared to [6] by using leaky ReLU instead of ordinary ReLU activation functions in the image reconstruction part and by adding batch normalization to the network. These changes resulted in faster and more stable training.…”

Section: Modelsmentioning

confidence: 99%

“…Please note that the introductory section 1 of this article draws heavily from[6]. All other sections contain substantially revised material and novel experiments/models.…”

mentioning

confidence: 99%

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Inferring material properties from CFRP processes via Sim-to-Real learning

Stieber

Schröter

Fauster

et al. 2022

Preprint

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Carbon fiber reinforced polymers provide favorable properties such as weight-specific strength and stiffness that are central for certain industries, such as aerospace or automotive manufacturing. Liquid composite molding (LCM) is a family of often employed, inexpensive, out-of-autoclave manufacturing techniques. Among them, resin transfer molding (RTM), offers a high degree of automation. Herein, textile preforms are saturated by a fluid polymer matrix in a closed mold. Both impregnation quality and level of fiber volume content are of crucial importance for the final part quality. We propose to simultaneously learn three major textile properties presented as a three-dimensional map based on a sequence of camera images acquired in flow experiments. The three properties are fiber volume content and permeability in X and Y direction. Finally, we show how simulation-to-real transfer learning can improve a digital twin in CFRP manufacturing, compared to simulation-only models and models based on sparse real data. The best model, trained on the most realistic simulation data outperforms the same model trained on less sophisticated simulation data by 4 percent points and 0.34 points in intersection over union, more than tripling this metric.

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Section: Rtm and Permeability Measurementssupporting

confidence: 60%

“…Based on Stieber et al's work [6], which involved using networks trained on simulated data for inference on real data, we wanted to investigate the following research questions:…”

Section: Discussionmentioning

confidence: 99%

Section: Data From Flow Simulation Runsmentioning

confidence: 99%

Section: Modelsmentioning

confidence: 99%

“…Please note that the introductory section 1 of this article draws heavily from[6]. All other sections contain substantially revised material and novel experiments/models.…”

mentioning

confidence: 99%

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Inferring material properties from CFRP processes via Sim-to-Real learning

Stieber

Schröter

Fauster

et al. 2022

Preprint

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Experimental study on the mechanical property of carbon fiber reinforced polymer with the combined application of digital twin and reduced order algorithm

Ji,

Wang,

Gong

et al. 2024

Polymer Composites

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Recognizing the anisotropy of mechanical property transfer between carbon fiber reinforced polymer (CFRP) layers, a unidirectional reduced‐order model (UROM) is proposed to comprehensively express the mechanical properties of each layer. A crucial focus is on effectively analyzing the real‐time mechanical properties of CFRP specimens, particularly in accurately identifying and predicting the interlaminar mechanical states of anisotropic CFRP laminated specimens. Moreover, the multi fidelity surrogate (MFS) is employed to replace the computationally intensive model, amalgamating high‐fidelity sensor data with low‐fidelity data obtained from the UROM. The UROM MFS is used to reduce the amount of data in the same layer of CFRP laminates, while preserving the characteristics between different layers and preserving the interlaminar information of CFRP laminates. Finally, the stress prediction results of the UROM MFS and the unreduced MFS model were compared and analyzed. The real‐time response speed of the u‐ROM MFS digital twin (DT) model was increased by 658%. The UROM MFS DT model effectively captures the mechanical properties of each layer of CFRP laminates, enables quick calculation of model parameters, and provides accurate predictions.Highlights First, the interlayer mechanical property of CFRP is analyzed with the combined application of experimental data, DT model, and UROM MFS. Second, MFS is employed to merge high‐fidelity sensor data of CFPR laminate with low‐fidelity data obtained from UROM. Third, the digital twin framework for CFPR laminate with high response speed and calculation accuracy is proposed.

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Inferring material properties from FRP processes via sim-to-real learning

Stieber

Schröter

Fauster

et al. 2023

Int J Adv Manuf Technol

Self Cite

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Fiber reinforced polymers (FRP) provide favorable properties such as weight-specific strength and stiffness that are central for certain industries, such as aerospace or automotive manufacturing. Liquid composite molding (LCM) is a family of often employed, inexpensive, out-of-autoclave manufacturing techniques. Among them, resin transfer molding (RTM), offers a high degree of automation. Herein, textile preforms are saturated by a fluid polymer matrix in a closed mold.Both impregnation quality and level of fiber volume content are of crucial importance for the final part quality. We propose to simultaneously learn three major textile properties (fiber volume content and permeability in X and Y direction) presented as a three-dimensional map based on a sequence of camera images acquired in flow experiments and compare CNNs, ConvLSTMs, and Transformers. Moreover, we show how simulation-to-real transfer learning can improve a digital twin in FRP manufacturing, compared to simulation-only models and models based on sparse real data. The overall best metrics are: IOU 0.5031 and Accuracy 95.929 %, obtained by pretrained transformer models.

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PermeabilityNets: Comparing Neural Network Architectures on a Sequence-to-Instance Task in CFRP Manufacturing

Cited by 5 publications

References 6 publications

Inferring material properties from CFRP processes via Sim-to-Real learning

Inferring material properties from CFRP processes via Sim-to-Real learning

Experimental study on the mechanical property of carbon fiber reinforced polymer with the combined application of digital twin and reduced order algorithm

Inferring material properties from FRP processes via sim-to-real learning

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