Parameter analysis and optimization of paper feeding devices to minimize jamming and simultaneous feeding of multiple pages

H. Kim, Jongsoo Lee

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

This article investigates an optimal design for a paper-feeding system that minimizes both jamming and simultaneous feeding of multiple papers, hereafter referred to as the multifeeding rate. A total of 11 design parameters for the paper transfer device, the paper separation device, and the paper guide path are selected and analysed in this study. A test jig for feeding and transferring papers is manufactured to obtain experimental data for use in parameter analysis and design optimization. Back-propagation neural network-based causality analysis is employed to extract five dominant variables among 11 design parameters, and the results of causality analysis are compared with sensitivity results obtained from the analysis of means in the context of experimental design. Five-variable, second-order polynomial based approximate metamodels for jam rate and multi-feeding rate are then constructed, and numerical optimization is performed using NSGA-II, a non-dominated sorting genetic algorithm. Finally, two numerical Pareto optimal solutions are verified via experimental testing.

Original languageEnglish
Pages (from-to)2673-2684
Number of pages12
JournalProceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science
Volume225
Issue number11
DOIs
Publication statusPublished - 2011 Nov 1

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Jamming
Jigs
Backpropagation
Sorting
Design of experiments
Genetic algorithms
Polynomials
Neural networks
Testing

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering

Cite this

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abstract = "This article investigates an optimal design for a paper-feeding system that minimizes both jamming and simultaneous feeding of multiple papers, hereafter referred to as the multifeeding rate. A total of 11 design parameters for the paper transfer device, the paper separation device, and the paper guide path are selected and analysed in this study. A test jig for feeding and transferring papers is manufactured to obtain experimental data for use in parameter analysis and design optimization. Back-propagation neural network-based causality analysis is employed to extract five dominant variables among 11 design parameters, and the results of causality analysis are compared with sensitivity results obtained from the analysis of means in the context of experimental design. Five-variable, second-order polynomial based approximate metamodels for jam rate and multi-feeding rate are then constructed, and numerical optimization is performed using NSGA-II, a non-dominated sorting genetic algorithm. Finally, two numerical Pareto optimal solutions are verified via experimental testing.",
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