An Integrated Nanocomposite Proximity Sensor: Machine Learning-Based Optimization, Simulation, and Experiment

This paper utilizes multi-objective optimization for efficient fabrication of a novel Carbon Nanotube (CNT) based nanocomposite proximity sensor. A previously developed model is utilized to generate a large data set required for optimization which included dimensions of the film sensor, applied exci...

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Bibliographic Details
Published in:Nanomaterials (Basel, Switzerland) Switzerland), 2022-04, Vol.12 (8), p.1269
Main Authors: Moheimani, Reza, Gonzalez, Marcial, Dalir, Hamid
Format: Article
Language:English
Subjects:
artificial neural network
Artificial neural networks
capacitance
carbon nano tubes
Carbon nanotubes
Datasets
Electric fields
Electrodes
Energy consumption
Experiments
Fabrication
genetic algorithm
Genetic algorithms
Learning theory
Machine learning
Mathematical models
Minimum cost
multi-objective optimization
Multiple objective analysis
Nanocomposites
Nanoparticles
Neural networks
Optimization
Pareto optimization
proximity sensor
Sensitivity
Sensors
Two dimensional models
Variables
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Summary:This paper utilizes multi-objective optimization for efficient fabrication of a novel Carbon Nanotube (CNT) based nanocomposite proximity sensor. A previously developed model is utilized to generate a large data set required for optimization which included dimensions of the film sensor, applied excitation frequency, medium permittivity, and resistivity of sensor dielectric, to maximize sensor sensitivity and minimize the cost of the material used. To decrease the runtime of the original model, an artificial neural network (ANN) is implemented by generating a one-thousand samples data set to create and train a black-box model. This model is used as the fitness function of a genetic algorithm (GA) model for dual-objective optimization. We also represented the 2D Pareto Frontier of optimum solutions and scatters of distribution. A parametric study is also performed to discern the effects of the various device parameters. The results provide a wide range of geometrical data leading to the maximum sensitivity at the minimum cost of conductive nanoparticles. The innovative contribution of this research is the combination of GA and ANN, which results in a fast and accurate optimization scheme.
ISSN:2079-4991
2079-4991
DOI:10.3390/nano12081269