Multivariate regression (MVR) and different artificial neural network (ANN) models developed for optical transparency of conductive polymer nanocomposite films

Demirbay, Barış; Kara, Duygu Bayram; Uǧur, Şaziye

doi:10.1016/j.eswa.2022.117937

Cited by 15 publications

(3 citation statements)

References 81 publications

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“…AI is an advanced technology supporting material design [99][100][101][102][103][104][105][106][107][108][109][110][111][112][113][114], material production sustainability [115][116][117][118][119][120][121], and material data processing, usable to generate new polymeric materials [122][123][124][125][126][127][128][129][130][131][132][133][134][135][136]. In Table 3 are listed some selected works about AI application in the material fabrication process.…”

Section: (Ket Iii) Artificial Intelligencementioning

confidence: 99%

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Intelligent Materials and Nanomaterials Improving Physical Properties and Control Oriented on Electronic Implementations

Massaro

2023

Electronics

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The review highlights possible research topics matching the experimental physics of matter with advances in electronics to improve the intelligent design and control of innovative smart materials. Specifically, following the European research guidelines of Key Enabling Technologies (KETs), I propose different topics suitable for project proposals and research, including advances in nanomaterials, nanocomposite materials, nanotechnology, and artificial intelligence (AI), with a focus on electronics implementation. The paper provides a new research framework addressing the study of AI driving electronic systems and design procedures to determine the physical properties of versatile materials and to control dynamically the material’s “self-reaction” when applying external stimuli. The proposed research framework allows one to ideate new circuital solutions to be integrated in intelligent embedded systems formed of materials, algorithms and circuits. The challenge of the review is to bring together different research concepts and topics regarding innovative materials to provide a research direction for possible AI applications. The discussed research topics are classified as Technology Readiness Levels (TRL) 1 and 2.

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Section: (Ket Iii) Artificial Intelligencementioning

confidence: 99%

“…Hydrogels for wearable applications and other new electrically conductive materials [132][133][134][135][136] ML accelerating polymer discovery…”

Section: High-performing Electrically Conductive Materialsmentioning

confidence: 99%

Intelligent Materials and Nanomaterials Improving Physical Properties and Control Oriented on Electronic Implementations

Massaro

2023

Electronics

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“…The relative importance of mixing parameters was further analyzed using two different methods such as Olden's and Garson's algorithms [8][9]. These methods were used to evaluate the influence of multiple significant variables based on the weighting matrixes of input-hidden and output-hidden obtained from the BP-ANN model.…”

Section: Relative Importance Of Parametersmentioning

confidence: 99%

Artificial Neural Network Model for Predicting the Energy Loss of Natural Rubber Foam

Kaewpradit

Uthaipan

Dechwayukul

2023

IOP Conf. Ser.: Mater. Sci. Eng.

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In this work, a back-propagation artificial neural network model was optimally developed based on 25 experimental datasets for predicting the energy loss percentage of natural rubber foam. The foam specimens were prepared in a Banbury internal mixer at various conditions of mixing temperature (40-80°C), rotor speed (40-80 rpm), and mastication time (1-5 min). Stress-strain loops were analyzed by applying compressive force at aspeed test of 500 mm/min with the capacity of load cell 2.5 kN, the energy loss was further calculated. In model development, the experimental datasets were randomly divided into 70:15:15 for training, validation, and testing respectively. Levenberg-Marquardt algorithm was used as a training function was used because of its fast convergence. The prediction results revealed that the average prediction accuracy of the three models is higher than 90%. From a material design point of view, the developed model could be implemented to find the proper mixing conditions to obtain the material with the maximum energy dissipation.

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A novel air pollution prediction system based on data processing, fuzzy theory, and multi-strategy improved optimizer

Tian

Mei

2023

Environ Sci Pollut Res

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Multivariate regression (MVR) and different artificial neural network (ANN) models developed for optical transparency of conductive polymer nanocomposite films

Cited by 15 publications

References 81 publications

Intelligent Materials and Nanomaterials Improving Physical Properties and Control Oriented on Electronic Implementations

Intelligent Materials and Nanomaterials Improving Physical Properties and Control Oriented on Electronic Implementations

Artificial Neural Network Model for Predicting the Energy Loss of Natural Rubber Foam

A novel air pollution prediction system based on data processing, fuzzy theory, and multi-strategy improved optimizer

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