Long-term creep behavior prediction of polymethacrylimide foams using artificial neural networks

Zhong, Jianlan; Yang, Chunhao; Ma, Wuning; Zhang, Zhendong

doi:10.1016/j.polymertesting.2020.106893

Cited by 18 publications

(5 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To verify the fitting and prediction performance of the model hyper-parameters above, the creep master curve data of PMI material in Ref. [ 27 ] were used for comparison. The creep curves at three temperatures were set to be the training set, and the creep curve at the other temperature was set to be the prediction set, respectively.…”

Section: Results and Discussionmentioning

confidence: 99%

“…The lines refer to the master curve processed by the test, Ref. [ 27 ], the model of present work, while the shaded area refers to the envelope range of the prediction after 100 training groups of Ref. [ 27 ].…”

Section: Results and Discussionmentioning

confidence: 99%

“…[ 27 ], the model of present work, while the shaded area refers to the envelope range of the prediction after 100 training groups of Ref. [ 27 ]. It can be seen that the three machine learning models with optimized hyper-parameters have good fitting and prediction performance for the creep curve of materials and can be used for the creep performance prediction.…”

Section: Results and Discussionmentioning

confidence: 99%

“…Furthermore, the remaining challenges and possible future directions were discussed. Zhong et al [ 27 ] built a long-term creep behavior prediction model of PMI materials using the ANN technique. The effects of different activation functions, hidden layer structures, and other super-parameters on the prediction performance were investigated.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Comparative Study of Machine Learning Approaches for Predicting Creep Behavior of Polyurethane Elastomer

Yang

Zhong

et al. 2021

Polymers

Self Cite

View full text Add to dashboard Cite

The long-term mechanical properties of viscoelastic polymers are among their most important aspects. In the present research, a machine learning approach was proposed for creep properties’ prediction of polyurethane elastomer considering the effect of creep time, creep temperature, creep stress and the hardness of the material. The approaches are based on multilayer perceptron network, random forest and support vector machine regression, respectively. While the genetic algorithm and k-fold cross-validation were used to tune the hyper-parameters. The results showed that the three models all proposed excellent fitting ability for the training set. Moreover, the three models had different prediction capabilities for the testing set by focusing on various changing factors. The correlation coefficient values between the predicted and experimental strains were larger than 0.913 (mostly larger than 0.998) on the testing set when choosing the reasonable model.

show abstract

Section: Results and Discussionmentioning

confidence: 99%

Section: Results and Discussionmentioning

confidence: 99%

Section: Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparative Study of Machine Learning Approaches for Predicting Creep Behavior of Polyurethane Elastomer

Yang

Zhong

et al. 2021

Polymers

Self Cite

View full text Add to dashboard Cite

show abstract

“…Polymethacrylimide (PMI) foams are usually isotropic rigid with a closed cell rate of up to 100% and possess superior mechanical and thermodynamic properties thanks to their ubiquitous six-membered imide rings [1][2][3][4][5]. They have found a wide range of applications in many fields including civil aviation and can be used as spherical sealing frames for Airbus A380 and A340 super jumbos [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Flame-retardant polymethacrylimide foams modified by grafting with amino-terminated phosphorous polyborosiloxane

Zhu

Chen

Dong

et al. 2023

Mater. Res. Express

View full text Add to dashboard Cite

To improve the flame retardancy of Polymethacrylimide (PMI) foam, in this study, a series of flame-retardant PMI foams were prepared by grafting with flame retardant amino-terminated phosphorous polyborosiloxane (N-PBSi), using tert-butanol (TBA) as the foaming agent. The structure, mechanical properties, thermal behavior, and flame retardancy of the resultant PMI foams were characterized. These as-prepared foams tended to be more compact in structure as N-PBSi content increased. The tensile, compressive, and bending strength of these PMI foams therefore was greatly enhanced, which were about 2 times, 9 times, and 3 times that of pure PMI foam, respectively. Besides, the introduction of N-PBSi also attributed to PMI foams better performance in char forming, especially in the air, which is beneficial for obtaining better retardancy against flame. Their advantages in flame retardancy and smoke inhabitation were confirmed by limiting oxygen index (LOI) and cone calorimeter tests (CCT). The total heat release (THR) and the peak of smoke generation rate (pSPR) of PMI/N-PBSi-20 were reduced by 23.1 % and 69.9 %, respectively. The N-PBSi incorporated were thought to not only generate phosphorus-containing fragments to capture free radicals in gas phase, but also rearrange in the solid phase to form a denser carbon layer to provide a better barrier between external heat and internal flammable pyrolysis gases. Given these, it can be envisaged that the flame-retardant PMI foams modified by N-PBSi may be more attractive in a wider range of applications.

show abstract

Simulation and Prediction of Creep Rate, Activation Energy, and Rupture Time of Sn94Sb5Ag1 Lead-Free Solder Alloy Using Artificial Neural Network Modeling

Lebda,

Habashy,

Mousa

2024

J. Electron. Mater.

View full text Add to dashboard Cite

Long-term creep behavior prediction of polymethacrylimide foams using artificial neural networks

Cited by 18 publications

References 23 publications

Comparative Study of Machine Learning Approaches for Predicting Creep Behavior of Polyurethane Elastomer

Comparative Study of Machine Learning Approaches for Predicting Creep Behavior of Polyurethane Elastomer

Flame-retardant polymethacrylimide foams modified by grafting with amino-terminated phosphorous polyborosiloxane

Simulation and Prediction of Creep Rate, Activation Energy, and Rupture Time of Sn94Sb5Ag1 Lead-Free Solder Alloy Using Artificial Neural Network Modeling

Contact Info

Product

Resources

About