Recomander System of Conductive Ink of Printed Electronics Application Using Deep Neural Networks
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Date
2024-10
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Abstract
Printed Electronics (PE) is a growing subfield in the field of electronics
manufacturing and material science. It enables the fabrication of electrical and
photonic devices using printing techniques such as inkjet, screen printing with
conductive inks. PE facilitates the printing of a wide array of electronic
components on various substrates, thereby enabling the construction of
conventional circuits. The rapid expansion of PE across industrial sectors have
sparked significant interest due to its capacity to produce intricate components. A
fundamental aspect of PE lies in the application of conductive ink during printing
process, which is pivotal in developing flexible electronic circuits and enhancing
the communicative capabilities of objects. The selection of appropriate ink is
paramount in meeting consumer requirements and ensuring product functionality.
Traditionally, ink selection has been a manual task, heavily reliant on the expertise
of designers. This conventional approach is time-consuming and may not always
yield optimal results. Hence, there is a growing need to design an automated system
for ink selection in printing applications.
The fundamental focus of the research work is to build automated systems
for choosing conductive ink for PE applications using neural network,
metaheuristic algorithm, and deep learning model. The introduced models for
conductive ink selection in PE are as follows:
An automated system using Multilayer Perceptron Neural Network
(MLPNN) and Support Vector Machine (SVM) for conductive ink
selection in PE. A conductive ink selection system using Particle Swarm Optimization-
MLPNN (PSO-MLPNN) A system to pick a suitable conductive ink for PE applications with the
help of Convolutional Neural Network (CNN)
The first phase of this research work deals with the development of an
automated system for conductive ink selection using MLPNN. Input data is
normalized into a common range between 0 and 1 using min-max technique. Two
models namely MLPNN and SVM are separately designed and trained to capture
the intricate relationship between input and output variables. These trained models
are used to select the conductive ink based on the input data. Performance of the
presented system is analysed by varying number of hidden layers, number of
hidden neurons, and number of training and testing samples. Efficacy of the models
are evaluated by computing accuracy, recall, precision, F1 score, balance
classification rate, and miss classification rate.
The second phase of this research work introduces a novel method for
choosing conductive ink for PE employing PSO and MLPNN. In this method, input
data is preprocessed using min-max method. A MLPNN is designed and trained
using PSO algorithm to learn the relationships between input and output variables.
Finally, trained PSO-MLPNN is used to select ink based on input features. Similar
to first phase, performance of the presented system is analysed by varying
parameters and evaluated using various metrics, and compared with standard
MLPNN. Third phase of this research work builds an automated system to improve
accuracy using 1D CNN. Input data is preprocessed with min-max method. A 1D
CNN is designed and trained to choose conductive ink for PE applications. Efficacy
of the model is evaluated by computing accuracy, recall, precision, F1 score,
balance classification rate, and miss classification rate and compared with SVM,
MLPNN and PSO-MLPNN.
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Computer Science and Engineering