Framework for incorporating stakeholders’ preferences in lifecycle welding flux design
Department of Industrial and Production Engineering, University of Ibadan, Ibadan, Nigeria.
Research Article
Global Journal of Engineering and Technology Advances, 2021, 07(02), 012-025.
Article DOI: 10.30574/gjeta.2021.7.2.0064
Publication history:
Received on 18 March 2021; revised on 30 April 2021; accepted on 03 May 2021
Abstract:
Welding flux design involves optimising multiple quality attributes which are often conflicting and are of varying degree of concern to the stakeholders. The attributes are often selected as design criteria and are sometimes at different levels in the hierarchy such as primary, secondary and tertiary attributes. The articulation and incorporation of the opinions of stakeholders concerning the intensity of importance attached to each attribute at all the levels in the hierarchy in the optimisation process has remained a challenge. In this study, a framework was proposed for the articulation and incorporation of the preferences of stakeholders concerning all levels of attributes and illustrated with a lifecycle welding flux design case from the literature involving 5, 21 and 25 primary, secondary and tertiary criteria, respectively. Fifteen out of the 21 secondary criteria could not be further unbundled and were added to the tertiary criteria to make 40 lowest level criteria. The subjective judgements of stakeholders were converted to weighting coefficients using the analytical hierarchy process. The coefficients for the lowest level criteria were converted to global weights (WGC)which were the preference indices for use in the optimisation models. Based on the values of WGC, moisture pick-up, extrudability and slag detachability were ranked 1st , 2nd , and 3rd respectively and the respective 38th, 39th and 40th lowest level criteria were fume generation, dilution and charpy impart strength.
Keywords:
Relative importance; Multiple response optimization; Design objectives; Sub-criteria; Welding flux quality optimization; Analytical hierarchy process
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