Integral aided method for material selection based on quality function deployment and comprehensive VIKOR algorithm

被引:39
作者
Cavallini, Carlo [1 ]
Giorgetti, Alessandro [1 ]
Citti, Paolo [1 ]
Nicolaie, Francois [2 ]
机构
[1] Univ Guglielmo Marconi, Dept Technol & Innovat Dev, I-00193 Rome, Italy
[2] Ferrari SpA, I-41053 Maranello, MO, Italy
来源
MATERIALS & DESIGN | 2013年 / 47卷
关键词
COLD SPRAY; NORMALIZATION; COATINGS;
D O I
10.1016/j.matdes.2012.12.009
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In engineering design, the selection of material alternatives usually depends of different criteria based on the specific problem. Due to the different units of this criteria, a normalization process is needed in the selection model. A lot of normalization approach can be found in literature and at the same time many algorithms have been developed to ensure the optimal material selection for a certain industrial application. Two elements of reflection can be drawn from the analysis of these. The first is the absence of an aided support to the selection of the correct engineering criteria by whom operate the selection process. The second is the need to define a weighting method that at the same time can be user-friendly to use and representative of the project's needs. A new selection model based on the integration between House of Quality and the Comprehensive Vikor Algorithm is presented in this paper. This approach, called Integral Aided Material Selection (IAMS), can overcome the main lack of traditional material selection model and provide a real support tool to the project team. That way the project team can optimally choose the selection criteria and assign to these the correct priority coherently with the project needs. A case study is presented to illustrate and justify the proposed method. The topic of the case study concerns the identification of the best coating for the protection of an aluminum alloy substrate (Al-7075) from the effects of abrasive wear against an alternating counterpart made by a high-strength cast iron. (C) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码:27 / 34
页数:8
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