Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/27849
Title: Handling uncertainties in modelling manufacturing processes with hybrid swarm intelligence
Authors: Chan, KY
Dillon, TS
Kwong, CK 
Keywords: fuzzy least square regression
manufacturing process modelling
nonlinearities
particle swarm optimisation
uncertainties
Issue Date: 2012
Publisher: Taylor & Francis
Source: International journal of production research, 2012, v. 50, no. 6, p. 1714-1725 How to cite?
Journal: International journal of production research 
Abstract: Seldom has research regarding manufacturing process modelling considered the two common types of uncertainties which are caused by randomness as in material properties and by fuzziness as in the inexact knowledge in manufacturing processes. Accuracies of process models can be downgraded if these uncertainties are ignored in the development of process models. In this paper, a hybrid swarm intelligence algorithm for developing process models which intends to achieve significant accuracies for manufacturing process modelling by addressing these two uncertainties is proposed. The hybrid swarm intelligence algorithm first applies the mechanism of particle swarm optimisation to generate structures of process models in polynomial forms, and then it applies the mechanism of fuzzy least square regression algorithm to determine fuzzy coefficients on polynomials so as to address the two uncertainties, fuzziness and randomness. Apart from addressing the two uncertainties, the common feature in manufacturing processes, nonlinearities between process parameters, which are not inevitable in manufacturing processes, can also be addressed. The effectiveness of the hybrid swarm algorithm is demonstrated by modelling of the solder paste dispensing process.
URI: http://hdl.handle.net/10397/27849
ISSN: 0020-7543
EISSN: 1366-588X
DOI: 10.1080/00207543.2011.560206
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