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attribute decision-making approach. All demand a bunch of human cognition and
intelligence just the last one capable of dealing with imprecise data significant for material
selection. Ullah and Harib introduce novel material selection method, which does not
require derivation of material indices or unpleasant inference calculations. Resembling the
material index based selection method it uses always available material property charts as
material relevant information in order to become realistic and user-friendly method. The
application of presented method is selection of optimal materials for robotic components in
early stage of design (Ullah & Harib, 2008).
6.1.1 Material selection results provided by decision support systems
As presented in Subsection 6.1, the decision support system for selection of material for
progressive die components was developed alternatively to manual material selection with
material and die handbooks, heuristics and designer's knowledge and experiences (Kumar
& Singh, 2007). The adequate material choice is one of major activities in die components
design leading to increased die life and consequential costs reduction of sheet material and
costs of production. The proposed system differentiates from existing CAD systems for
progressive dies at providing not only the list of materials but also an option to select easily
available materials from the advice received from the system. After that the list of materials
can be prepared appropriately. According to authors, the discussed system has been
designated as powerful and easy to handle due to interactive mode of system-designer
communication and extensive knowledge base containing knowledge and experiences of
progressive die design. The system was developed to advice the designers at material
selection for progressive die components, thus it is oriented mainly in just tool steels, which
enables the flexibility of the system.
Edwards and Dang proposed a multiple-mapping strategy and an inter-level behavioural
modelling strategy to support design decision-making when applying materials in
combination and when they are combined with structural determination or design
components (Edwards & Dang, 2007). According to the authors, the objective is to facilitate
simultaneous consideration of components and materials at early stage of design and to
provide the platform for the designer to work out the couplings among the material
properties and corresponding components, in order to determine the respective material
properties. Due to the complexity of research area discussed, proposed strategies cannot
assure adequate support to the engineering design problems despite of their appropriate
implementation and satisfactory validated case studies.
Intelligent materials selection method introduced by Ullah and Harib uses a linguistic
description of material selection problems and material property charts relevant to the
linguistic description of the problem (Ullah & Harib, 2008). The presented method supports
the optimal material selection at early stage of design process and is suitable even for
complex machineries, where design requirements and design relevant information are not
precisely known. Discussed method could be applied for all groups of materials and is not
limited to one.
It is obvious that material selection is interesting engineering domain and advanced
decision making process. In recent years, numerous approaches and various methods were
developed to create the decision support system for this particular field. Most material
selection methods are specific at some points and offer a vast amount of opportunities to
build diverse decision support systems. Some are limited to only one group of materials
(Edwards & Dang, 2007; Ullah & Harib, 2008) and some are focused on special group of
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