Prediction of machine reconfigurability using artificial neural network for a reconfigurable serial product flow line

Faisal Hasan, P.K. Jain and Dinesh Kumar

International Journal of Industrial and Systems Engineering, 2014, vol. 18, issue 3, 283-305

Abstract: Reconfigurable machines (RMs) are considered to be one of the vital elements of modern manufacturing systems like reconfigurable manufacturing systems (RMSs). These machines offered customised flexibility in terms of capacity and functionality. Reconfigurable machines are assembled using some basic/essential modules and auxiliary modules. The RMTs can be reconfigured into several other configurations for variable functionality and capacity by keeping its base modules and just adding/removing or adjusting the auxiliary modules. Measuring machine reconfigurability may be considered as one of the important challenge in assessing the performance of these manufacturing systems. In the present paper, an artificial neural network model has been proposed for quantitative assessment of reconfigurability values of RMs on the product flow line. The data is generated using a developed mathematical model based on multi attribute utility theory. The ANN predictive model could thus provide a flexible and objective framework for manufacturers to evaluate reconfigurability of machines for a given product flow line. The developed approach has been demonstrated using a multi stage serial reconfigurable product flow line.

Keywords: artificial neural networks; ANNs; machine reconfigurability; reconfigurable flow lines; serial product flow lines; reconfigurable machines; reconfigurable manufacturing systems; mathematical modelling; multiattribute utility theory; MAUT. (search for similar items in EconPapers)
Date: 2014
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=65535 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijisen:v:18:y:2014:i:3:p:283-305

Access Statistics for this article

More articles in International Journal of Industrial and Systems Engineering from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().