Measurement of molecular orientation using longitudinal ultrasound and its first application in in-situ characterization

Zhao, Peng; Xia, Neng; Zhang, Jianfeng; Xie, Jun; Zhang, Chengqian; Fu, Jianzhong

doi:10.1016/j.polymer.2019.122092

Cited by 18 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lionetto and Maffezzoli [13] used ultrasonic dynamic mechanical analysis technology to monitor modulus changes of polymers associated with glass transitions, crystallization, cross-linking, and other chemical or physical transition properties. Zhao et al [14] used longitudinal ultrasound to measure the molecular orientation during the polymer melt injection molding process and established the qualitative relation between the ultrasonic wave speed and molecular orientation. However, the aforementioned researchers established the relation between ultrasonic signals and material properties based on the signal itself only rather than the physical interaction between ultrasound and fluid medium, because quantifying the influence of fluid viscoelasticity on ultrasonic propagation is often seen as a challenge.…”

Section: Introductionmentioning

confidence: 99%

Influence of viscoelasticity on ultrasonic propagation in poly(ethylene oxide) aqueous solution

Wu¹,

Huang²,

Liang³

et al. 2023

J. Phys. D: Appl. Phys.

View full text Add to dashboard Cite

Generally, ultrasonic propagation in ultrasonic non-destructive testing is regarded as a linear process, during which the interaction between ultrasonic and the measured material is rarely considered. In this study, the mechanisms of ultrasonic propagation in viscoelastic Poly(ethylene oxide) (PEO) aqueous solution and the interaction between ultrasound and fluid were investigated. A theoretical model of ultrasonic propagation in the viscoelastic fluid was established, which added the body force from ultrasonic waves to the fluid momentum equation, and the viscoelasticity of fluid was described by the Oldroyd-B model. The simulation results in the ultrasonic reflection coefficient at the solid-liquid boundary and attenuation coefficient based on the multi-physics coupling method were experiment verified. The results of this study show that the ultrasonic absorption coefficient at low frequencies from 2.25~7.5MHz exhibits frequency dependence through ultrasonic relaxation spectroscopy. The simulation results of the velocity of disturbed flow during ultrasonic propagation show that the viscosity of the solution has a greater impact on the disturbance than the relaxation time, and the phase angle difference between the stress and strain also shows that the viscosity is the main factor affecting the ultrasonic propagation.

show abstract

Section: Introductionmentioning

confidence: 99%

Influence of viscoelasticity on ultrasonic propagation in poly(ethylene oxide) aqueous solution

Wu¹,

Huang²,

Liang³

et al. 2023

J. Phys. D: Appl. Phys.

View full text Add to dashboard Cite

show abstract

“…Furthermore, some researchers have employed nondestructive ultrasonic sensing technology to measure changing melt pressure in the mold during the injection process, through which the melt pressure state can be monitored without damaging the mold structure and the various stages of the molding process can be identified [5].…”

Section: Introductionmentioning

confidence: 99%

Enhancement of multilayer perceptron model training accuracy through the optimization of hyperparameters: a case study of the quality prediction of injection-molded parts

Huang²

2021

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Injection molding has been broadly used in the mass production of plastic parts and must meet the requirements of efficiency and quality consistency. Machine learning can effectively predict the quality of injection molded part. However, the performance of machine learning models largely depends on the accuracy of the training. Hyperparameters such as activation functions, momentum, and learning rate are crucial to the accuracy and efficiency of model training. This research further analyzed the influence of hyperparameters on testing accuracy, explored the corresponding optimal learning rate, and provided the optimal training model for predicting the quality of injection molded parts. In this study, stochastic gradient descent (SGD) and stochastic gradient descent with momentum were used to optimize the artificial neural network model. Through optimization of these training model hyperparameters, the width testing accuracy of the injection product improved. The experimental results indicated that in the absence of momentum effects, all five activation functions can achieve more than 90% of the training accuracy with a learning rate of 0.1. Moreover, when optimized with the SGD, the learning rate of the Sigmoid activation function was 0.1, and the testing accuracy reached 95.8%. Although momentum had the least influence on accuracy, it affected the convergence speed of the Sigmoid function, which reduced the number of required learning iterations (82.4% reduction rate). Optimizing hyperparameter settings can improve the accuracy of model testing and markedly reduce training time.

show abstract

Hyperparameter optimization strategy of multilayer perceptron model for injection molding quality prediction

Huang,

Chang

2024

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Measurement of molecular orientation using longitudinal ultrasound and its first application in in-situ characterization

Cited by 18 publications

References 24 publications

Influence of viscoelasticity on ultrasonic propagation in poly(ethylene oxide) aqueous solution

Influence of viscoelasticity on ultrasonic propagation in poly(ethylene oxide) aqueous solution

Enhancement of multilayer perceptron model training accuracy through the optimization of hyperparameters: a case study of the quality prediction of injection-molded parts

Hyperparameter optimization strategy of multilayer perceptron model for injection molding quality prediction

Contact Info

Product

Resources

About