میانگین وزنی فازی برای شناسایی مفهوم طراحی بهینه
Abstract
1- Introduction
2- Methodology
3- Application of the model to design of RAF
4- Results and discussion
5- Conclusion
References
Abstract
In this article, a novel hybridized Multi-Attribute Decision Model (MADM) is developed to identify an optimal design of a Reconfigurable Assembly Fixture (RAF) from a set of alternative design concepts. The model combines the comparative advantage of Fuzzy Analytic Hierarchy Process (FAHP) and the computational strength of the Fuzzy Weighted Average (FWA) based on left and right scores in order to obtain aggregates for the design alternatives considering the relative importance of the design criteria as needed in the optimal design. The model was applied to evaluate four design concepts of a RAF with six design features having numerous sub-features. Results obtained from the evaluation process shows that there are differences in final values of the design alternatives. However, a close variation exists between these values. These differences can be accrued to the interrelationships between the design features and sub-features obtained from the Fuzzy Synthetic Extent (FSE) of the FAHP and an unambiguity judgment of the FWA when aggregating availability of the design features and sub-features in the design alternatives.
Introduction
Robust design of Products and industrial machineries is important from conceptualization to manufacturing and up till usage in order for manufacturers to obtain a share of the competitive market flooded with changeable designs (Olabanji, 2018). The need for these robust designs calls for development of different design concepts of a particular product or machine before a detail design analysis can be done (Song et al., 2013). The availability of alternative design concepts necessitates the need for selection of optimal design concept (Wei and Chang, 2008). Concept selection in engineering design has attracted importance in recent times because it has a direct implication on the quality of the final design. Problems that arises when it is not properly done includes; repetitive alterations and modifications of drafted designs, prolonged developmental time and amplified cost of actualization (Tiwari et al., 2017). In order to arrive at robust design of a new product or engineering component, identification of design attributes and sub features viz a viz the numerous functional requirements from the customers or intended end users becomes the first task (Ayag and Ozdem, 2007; Brackea et al., 2017). The engineering design process attempts to give a holistic approach to identification of the design attributes, sub features and functional requirements. It follows an established design standard by proposing four phases (product planning and clarification of task, conceptual design, embodiment design and detail design). These phases are usually applied to arrive at a detail design of the new product (Yeo et al., 2004; Olabanji and Mpofu, 2014). Also, the engineering design process can be imagined to have a set of eleven steps as described by (Ayag and Ozdem, 2007). The relationship between these two analogies is described in Figure 1 where these four phases are disintegrated into the eleven steps. It may be assumed that the information needed in each step and phase will also follow the same manner. However, since the concept selection step is a decision-making process, adequate information is needed for successful selection process. Concept selection in engineering design can be modelled as a multicriteria decision-making (MCDM) problem since it involves multiple design attributes that are having different sub features. Considering the steps in the engineering design process as an all-inclusive approach, it is possible to develop a relationship between the constraints, design attributes and sub features for determining optimal design concept using the multi-criteria analysis as presented in Figure 2.