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Downstream performance prediction for a manufacturing system using neural networks and six-sigma improvement techniques

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Johnston, AB, Maguire, LP and McGinnity, TM (2009) Downstream performance prediction for a manufacturing system using neural networks and six-sigma improvement techniques. Robotics and Computer-Integrated Manufacturing, 25 (3). pp. 513-521. [Journal article]

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DOI: doi:10.1016/j.rcim.2008.02.010

Abstract

Intelligent techniques have been applied in a range of industrial environments although their implementation is not the first choice of many process engineers. In contrast process engineers in a diverse range of manufacturing environments regularly deploy business improvement techniques, such as the six-sigma methodology. Such techniques aim to control and subsequently identify the relationship between the process inputs and outputs so that a process engineer can more accurately predict how the process output shall perform based on the system inputs. Factors such as cost reduction, automatic process control or simply process prediction may be the defining factors in establishing prediction models.In this paper the authors use as a case study the manufacture of hard disc drives, from the developing of the read–write head to the recording media and the overall construction of the hard disc drive with its controlling mechanisms. Each of these stages are separate and complex-processing elements and integral to the functionality of the end product. In addition each of these stages of manufacturing may take days or weeks to complete and be processed in separate facilities and/or countries.This paper reports on the application of intelligent system techniques to improve the downstream performance prediction within this manufacturing environment. The application is guided by a six-sigma methodology to obtain improved performance. The results highlight that significant downstream prediction accuracy can be obtained using this hybrid approach.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:10320
Deposited By:Professor Liam Maguire
Deposited On:01 Feb 2010 12:06
Last Modified:15 Jun 2011 11:09

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