Artificial Neural Networks-Based Material Parameter Identification for Numerical Simulations of Additively Manufactured Parts by Material Extrusion

Meißner, Paul; Watschke, Hagen; Winter, Jens; Vietor, Thomas

doi:10.3390/polym12122949

Cited by 28 publications

(31 citation statements)

References 40 publications

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“…The value networks are the same as the actor network except that the output size is one. All parameters for the actor and value networks were initialized by He Uniform [ 25 ] and then updated iteratively by the Adam optimizer [ 26 ]. In our simulations, B and

were set to 10 and 0.9, respectively, and

and

were set to 10 and 100, respectively.…”

Section: Numerical Resultsmentioning

confidence: 99%

A New Cache Update Scheme Using Reinforcement Learning for Coded Video Streaming Systems

Kim¹,

Lee

Ryu

et al. 2021

Sensors

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As the demand for video streaming has been rapidly increasing recently, new technologies for improving the efficiency of video streaming have attracted much attention. In this paper, we thus investigate how to improve the efficiency of video streaming by using clients’ cache storage considering exclusive OR (XOR) coding-based video streaming where multiple different video contents can be simultaneously transmitted in one transmission as long as prerequisite conditions are satisfied, and the efficiency of video streaming can be thus significantly enhanced. We also propose a new cache update scheme using reinforcement learning. The proposed scheme uses a K-actor-critic (K-AC) network that can mitigate the disadvantage of actor-critic networks by yielding K candidate outputs and by selecting the final output with the highest value out of the K candidates. The K-AC exists in each client, and each client can train it by using only locally available information without any feedback or signaling so that the proposed cache update scheme is a completely decentralized scheme. The performance of the proposed cache update scheme was analyzed in terms of the average number of transmissions for XOR coding-based video streaming and was compared to that of conventional cache update schemes. Our numerical results show that the proposed cache update scheme can reduce the number of transmissions up to 24% when the number of videos is 100, the number of clients is 50, and the cache size is 5.

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were set to 10 and 0.9, respectively, and

and

were set to 10 and 100, respectively.…”

Section: Numerical Resultsmentioning

confidence: 99%

A New Cache Update Scheme Using Reinforcement Learning for Coded Video Streaming Systems

Kim¹,

Lee

Ryu

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Each neuron in a neural network works independently, and the network’s overall activity is the product of the actions of numerous neurons. In other words, neurons in a cooperative process correct each other [ 33 , 34 , 35 ].…”

Section: Methodsmentioning

confidence: 99%

Artificial Neural Network to Forecast Enhanced Oil Recovery Using Hydrolyzed Polyacrylamide in Sandstone and Carbonate Reservoirs

2021

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Polymer flooding is an important enhanced oil recovery (EOR) method with high performance which is acceptable and applicable on a field scale but should first be evaluated through lab-scale experiments or simulation tools. Artificial intelligence techniques are strong simulation tools which can be used to evaluate the performance of polymer flooding operation. In this study, the main parameters of polymer flooding were selected as input parameters of models and collected from the literature, including: polymer concentration, salt concentration, rock type, initial oil saturation, porosity, permeability, pore volume flooding, temperature, API gravity, molecular weight of the polymer, and salinity. After that, multilayer perceptron (MLP), radial basis function, and fuzzy neural networks such as the adaptive neuro-fuzzy inference system were adopted to estimate the output EOR performance. The MLP neural network had a very high ability for prediction, with statistical parameters of R2 = 0.9990 and RMSE = 0.0002. Therefore, the proposed model can significantly help engineers to select the proper EOR methods and API gravity, salinity, permeability, porosity, and salt concentration have the greatest impact on the polymer flooding performance.

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“…In contrast to this common method, a rather novel method for PI based on machine learning can alternatively be used in a direct inverse process to determine the MPs that can best reproduce the experimentally determined material behavior [13][14][15]. An essential advantage of this approach comprises the storage of information in the form of the weights and biases in the neural network structure during the training process, allowing the reusability of trained NNs to determine MPs for modified materials with similar material characteristics.…”

Section: Introductionmentioning

confidence: 99%

Methodology for Neural Network-Based Material Card Calibration Using LS-DYNA MAT_187_SAMP-1 Considering Failure with GISSMO

2022

Self Cite

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A neural network (NN)-based method is presented in this paper which allows the identification of parameters for material cards used in Finite Element simulations. Contrary to the conventionally used computationally intensive material parameter identification (MPI) by numerical optimization with internal or commercial software, a machine learning (ML)-based method is time saving when used repeatedly. Within this article, a self-developed ML-based Python framework is presented, which offers advantages, especially in the development of structural components in early development phases. In this procedure, different machine learning methods are used and adapted to the specific MPI problem considered herein. Using the developed NN-based and the common optimization-based method with LS-OPT, the material parameters of the LS-DYNA material card MAT_187_SAMP-1 and the failure model GISSMO were exemplarily calibrated for a virtually generated test dataset. Parameters for the description of elasticity, plasticity, tension–compression asymmetry, variable plastic Poisson’s ratio (VPPR), strain rate dependency and failure were taken into account. The focus of this paper is on performing a comparative study of the two different MPI methods with varying settings (algorithms, hyperparameters, etc.). Furthermore, the applicability of the NN-based procedure for the specific usage of both material cards was investigated. The studies reveal the general applicability for the calibration of a complex material card by the example of the used MAT_187_SAMP-1.

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Artificial Neural Networks-Based Material Parameter Identification for Numerical Simulations of Additively Manufactured Parts by Material Extrusion

Cited by 28 publications

References 40 publications

A New Cache Update Scheme Using Reinforcement Learning for Coded Video Streaming Systems

A New Cache Update Scheme Using Reinforcement Learning for Coded Video Streaming Systems

Artificial Neural Network to Forecast Enhanced Oil Recovery Using Hydrolyzed Polyacrylamide in Sandstone and Carbonate Reservoirs

Methodology for Neural Network-Based Material Card Calibration Using LS-DYNA MAT_187_SAMP-1 Considering Failure with GISSMO

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