Abstract
The materials selection can affect the design component radically, with effect on the manufacturing systems efficiency, environmental impact issues, and customer satisfaction. There are different methods employed for materials selection; however, two steps are usual for most of these methods: screening and ranking. The ranking step identifies among materials candidates those that can perform the function the best as possible. Multi-criteria methods have been widely employed to materials selection, especially in the ranking step. Most of these methods take advantage of fuzzy numbers and linguistic variables to process qualitative information and information with uncertainties. One of the approaches that have been developed to solve issues related to make decisions in multi-criteria methods using linguistic information is the 2-tuple linguistic computational model. The main advantage of this approach is taking the “loss of information” away, which provides a higher precision on results. This paper aims to present a multi-criteria method for materials selection ranking step based on 2-tuple linguistic variables. The steps and several equations needed to apply the proposed method are described. Two case studies are presented and compare results with other methods to demonstrate the proposed method potential.
Highlights
The materials selection is an essential part of new products development process[1] and shows interdependence either shape design or manufacturing process[2,3]
This paper aims to present a multi-criteria method to materials selection ranking step based on 2-tuple linguistic variables
The linguistic variables set used during the assessment, i.e., material performance was: S = {S0 = Very Low; S1 = Low; S2 = Low Medium; S3 = Medium; S4 = High Medium; S5 = High; S6 = Very High}
Summary
The materials selection is an essential part of new products development process[1] and shows interdependence either shape design or manufacturing process[2,3]. The selection of the material can radically affect the component’s design, affecting the manufacturing systems efficiency, the environmental impacts, and customer satisfaction. Multi-criteria methods have been widely applied to materials selection, especially in the ranking step[5,6,7]. Several of these methods take advantage of quantitative data[8,9,10], in a while, others make use of qualitative data presented as fuzzy numbers[11,12] or linguistic variables[13]. Some methods allow qualitative and quantitative data processing simultaneously[14,15]
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