Development of Prediction Model for Conductive Pattern Lines Generated Through Positive Displacement Microdispensing System Using Artificial Neural Network

In the fabrication of electronic devices, uniform and good quality conductive printed lines are highly desirable. The goal of the present study is to develop a predictive model for conductive pattern lines produced by the microdispensing system. For this purpose, an artificial neural network (ANN) b...

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Vydané v:Arabian journal for science and engineering (2011) Ročník 46; číslo 3; s. 2429 - 2442
Hlavní autori: Abas, Muhammad, Naeem, Khawar, Habib, Tufail, Khan, Imran, Farooq, Umer, Khalid, Qazi Salman, Rahman, Khalid
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2021
Springer Nature B.V
Predmet:
Algorithms
Artificial neural networks
Back propagation networks
Conductivity
Electronic devices
Engineering
Feed rate
Humanities and Social Sciences
Mathematical models
multidisciplinary
Neural networks
Nozzles
Parameter identification
Prediction models
Process parameters
Research Article-Mechanical Engineering
Science
Microdispensing system
Signal-to-noise ratios
Conductive pattern lines
Prediction model
Artificial neural networks
ISSN:2193-567X, 1319-8025, 2191-4281
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Shrnutí:In the fabrication of electronic devices, uniform and good quality conductive printed lines are highly desirable. The goal of the present study is to develop a predictive model for conductive pattern lines produced by the microdispensing system. For this purpose, an artificial neural network (ANN) based on a feed-forward backpropagation algorithm is adopted. Input process parameters are pressure, feed rate, and standoff distance, while the output performance parameter (response) is the width of pattern lines generated through 200 µm and 500 µm nozzles diameter. The dispensing material is carbon paste having a viscosity of 30 Pa s. Best levels of process parameters are identified to achieve lower width of pattern lines based on the Taguchi signal-to-noise ratios. The identified best levels are found valid in the ranges of printing process parameters after training the neural networks. The prediction ability of ANN models is evaluated based on the leave-one-out cross-validation technique. The results showed that the proposed ANN model accomplished better results in predicting the width of pattern lines. In addition, the proposed approach is extendable to different materials with a variety of viscosities as well as to other similar printing techniques.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2193-567X
1319-8025
2191-4281
DOI:10.1007/s13369-020-05103-3