Industrial Mooney viscosity prediction using fast semi-supervised empirical model

Zheng, Wenjian; Gao, Xuejin; Liu, Yi; Wang, Limei; Yang, Jianguo; Gao, Zengliang

doi:10.1016/j.chemolab.2017.10.009

Cited by 29 publications

(23 citation statements)

References 26 publications

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“…Machine learning is not only proven to be a powerful tool for learning the material properties of the experimental data as well as to predict the unlearned data (Mueller et al, 2016;Zhang and Friedrich, 2003), but is also widely used in the study of the MR materials (Imaduddin et al, 2017;Wang and Liao, 2005). The methods can be selected from the existing studies, such as backpropagation artificial neural network (BP-ANN) (Shahriar and Nehdi, 2011;Vani et al, 2015), support vector regression (SVR) (Liu and Chen, 2013;Liu et al, 2014), extreme learning machine (ELM) (Jin et al, 2017;Zheng et al, 2017), and deep learning (DL) (Liu et al, 2017(Liu et al, , 2018. ELM (Jin et al, 2017;Zheng et al, 2017) is known for its shorter training time as well as its better accuracy (Li et al, 2016;Zheng et al, 2018) and generalization levels than the conventional SVR and BP-ANN methods (Huang et al, 2011(Huang et al, , 2015.…”

Section: Introductionmentioning

confidence: 99%

Prediction of field-dependent rheological properties of magnetorheological grease using extreme learning machine method

Bahiuddin

Wahab

Shapiai

et al. 2019

Journal of Intelligent Material Systems and Structures

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Magnetorheological grease is seen as a promising material for replacing the magnetorheological fluid owing to its higher stability and the lesser production of leakage. As such, it is important that the rheological properties of the magnetorheological grease as a function of a composition are conducted in the modeling studies of a magnetorheological grease model so that its optimum properties, as well as the time and cost reduction in the development process, can be achieved. Therefore, this article had proposed a machine learning method-based simulation model via the extreme learning machine and backpropagation artificial neural network methods for characterizing and predicting the relationship of the magnetorheological grease rheological properties with shear rate, magnetic field, and its compositional elements. The results were then evaluated and compared with a constitutive equation known as the state transition equation. Apart from the shear stress results, where it had demonstrated the extreme learning machine models as having a better performance than the other methods with R 2 more than 0.950 in the training and testing data, the predicted rheological variables such as shear stress, yield stress, and apparent viscosity were also proven to have an agreeable accuracy with the experimental data.

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Section: Introductionmentioning

confidence: 99%

Prediction of field-dependent rheological properties of magnetorheological grease using extreme learning machine method

Bahiuddin

Wahab

Shapiai

et al. 2019

Journal of Intelligent Material Systems and Structures

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“…It has been widely used for product specication and quality control of non-vulcanized rubbery materials. 34,35 The Mooney viscosity of the elastomeric compounds is a measure of the owand process-ability and is determined by the structure and composition of the rubber. The effect of plasticizer N90 and N98 loading on the Mooney viscosity of the compounds is shown in Fig.…”

Section: Mooney Viscosity Of Compoundsmentioning

confidence: 99%

Lubrication and plasticization behavior of large-size micro-spherical structured SiO₂ for natural rubber

et al. 2018

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“…, is omitted and not utilized. Alternatively, the semi-supervised soft sensors, such as semi-supervised ELM (SELM), enhance the Sensors 2020, 20, 695 2 of 10 prediction results (e.g., compared with ELM) by suitably modeling of both the labeled dataset S l and the unlabeled dataset S u = X u [17]. To further improve the prediction accuracy, both supervised and semi-supervised soft sensors are further combined with the ensemble learning or just-in-time learning strategies in different scenarios [5,[18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Robust Soft Sensor with Deep Kernel Learning for Quality Prediction in Rubber Mixing Processes

Zheng

Liu

et al. 2020

Sensors

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Although several data-driven soft sensors are available, online reliable prediction of the Mooney viscosity in industrial rubber mixing processes is still a challenging task. A robust semi-supervised soft sensor, called ensemble deep correntropy kernel regression (EDCKR), is proposed. It integrates the ensemble strategy, deep brief network (DBN), and correntropy kernel regression (CKR) into a unified soft sensing framework. The multilevel DBN-based unsupervised learning stage extracts useful information from all secondary variables. Sequentially, a supervised CKR model is built to explore the relationship between the extracted features and the Mooney viscosity values. Without cumbersome preprocessing steps, the negative effects of outliers are reduced using the CKR-based robust nonlinear estimator. With the help of ensemble strategy, more reliable prediction results are further obtained. An industrial case validates the practicality and reliability of EDCKR.

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Industrial Mooney viscosity prediction using fast semi-supervised empirical model

Cited by 29 publications

References 26 publications

Prediction of field-dependent rheological properties of magnetorheological grease using extreme learning machine method

Prediction of field-dependent rheological properties of magnetorheological grease using extreme learning machine method

Lubrication and plasticization behavior of large-size micro-spherical structured SiO₂ for natural rubber

Robust Soft Sensor with Deep Kernel Learning for Quality Prediction in Rubber Mixing Processes

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Industrial Mooney viscosity prediction using fast semi-supervised empirical model

Cited by 29 publications

References 26 publications

Prediction of field-dependent rheological properties of magnetorheological grease using extreme learning machine method

Prediction of field-dependent rheological properties of magnetorheological grease using extreme learning machine method

Lubrication and plasticization behavior of large-size micro-spherical structured SiO2 for natural rubber

Robust Soft Sensor with Deep Kernel Learning for Quality Prediction in Rubber Mixing Processes

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Product

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Lubrication and plasticization behavior of large-size micro-spherical structured SiO₂ for natural rubber