Decision Support System for Designing with Polymer Materials – Current Challenges and Future Expectations - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 502

data that are distributed in a potentially heterogeneous IT infrastructure, personal or
educational knowledge, models and strategies. As long as the system can be designed in
advance and the focus lays on data, in practice the application of decision support systems is
often sufficient. However, according to Yilmaz and Oren (Yilmaz & Oren eds., 2009) the
versions of decision support systems are following:
 agent-directed decision support systems, used for rapidly changing environments
where the system needs to be able to adapt;
 decision support simulation systems are used to obtain, display and evaluate
operationally relevant data in agile contexts by executing models using operational data
exploiting the full potential of modelling and simulation and producing numerical
insight into the behaviour of complex system;
 agent-directed decision support simulation systems are agent-directed simulations that
are applied as a decision support system, whose focus is on processes, so the system can
adapt to new requirements and constraints in the environment.
For the decision support system for plastic material selection presented in Section 7 it is
expected the agent-directed decision support simulation systems to cover all requirements
and wishes the most satisfactory.
6.1 Current decision support systems for material selection
In recent years, many decision support systems where developed and some successfully
launched in real applications (Turban et al., 2004). The significance of material selection
dilemma within the design process is obvious, as several models were developed to support
the engineers at this stage of design. It is essential to study and observe the progress on the
field of material selection intelligent support thus, three examples of novel material selection
models or methods, all for diverse applications, are going to be briefly discussed. It is
significant to get the insight in the background before presentation of the future
expectations in plastic material selection with decision support system.
Material selection query occurs at designing various products. Kumar and Singh
represented intelligent system for selection of materials for progressive die components,
where two knowledge base modules are comprised (Kumar & Singh, 2007). One is
designated as selection of materials for active and inactive components of progressive dies.
The other module was developed for determination of hardness range of materials for active
dies. Acquired knowledge in the knowledge base is analysed and incorporated into a set of
production IT rules.
At the present time, we acknowledge several formalised methods to support selection of
individual materials. Real design problems often involve materials in combination as
sometimes only joint multiple materials can produce the expected performance. Edwards
and Dang address this issue and recommend a multiple-mapping strategy and an inter-level
behavioural modelling strategy. Structural and materials solution in supporting design
decision-making may be considered simultaneously (Edwards & Dang, 2007).
In general, two types of material selection methods were commonly used at past
applications: material index based selection method and knowledge based methods. First
follows some successive steps to identify the optimal material as the material heaving the
largest/smallest material index value. When applying multiple indices the use of
optimization method is mandatory to find the optimal result. Knowledge based methods
use diverse approaches like IT rules approach, decision-making approach and fuzzy multi-