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knowledge resources utilized in the process include
differentiating features, customers’ requirements,
assimilability, manufacturability, and heuristic
knowledge (e.g. production rules), etc. In applying
the above knowledge support scheme for modular
product design, the following should be noted: (i)
The first step, system requirement modeling and
analysis, should be considered in development of a
modular product design; (ii) A system and structured
approach is a mandatory for development of a
modular product design; (iii) A new product is
developed through functional analysis, rather than
modifying existing ones; (iv) Complex products or
systems have a considerable amount of constraints
that limit the product design.
3.2.4 Assessment of Product Design
Figure 5 illustrates an overall view of the proposed
fuzzy assessment model with design failure mode and
effective analysis (DFMEA), in which there are three
major stages to implement the assessment:
fuzzification, rule evaluation, and defuzzification
(Diaz-Hermida et al., 2005; Li et al., 2005). The
model firstly uses linguistic variables to depict the
severity, frequency of occurrence, and detectability of
the failure. These inputs are then fuzzified to
determine the degree of membership in each input
group. The resulting ‘fuzzy inputs’ are evaluated
using a linguistic rule base and fuzzy logic operations
to create a classification of the riskiness of the failure
mode and an associated degree of membership in the
risk group. This ‘fuzzy output’ is then defuzzified to
give the prioritization level for the failure mode. The
fuzzification process, using crisp gradings, converts
the severity, occurrence, and detectability inputs into
the fuzzy representations that can then be matched
with the premises of the rules in the rule base (Diaz-
Hermida et al., 2005; Li et al., 2005). The rule base
depicts the riskiness of each combination of input
variables. It is composed of the expert knowledge
about the interactions between various failure modes
and effect that is represented in the form of fuzzy ‘IF-
THEN’ rules. There are two parts included: an
antecedent that is compared to the inputs and a
consequence that is the result. For example, ‘IF t is P
THEN u is Q’ where P and Q are linguistic values
defined by fuzzy sets on the ranges t and u
respectively. The portion IF of the rule ‘t is P’ is
called the antecedent or premise, while the portion
THEN of the rule ‘u is Q’ is called the consequence or
conclusion. The antecedent is an interpretation that
returns a single number between 0 and 1, whereas the
consequence is an assignment that assigns the entire
fuzzy set Q to output variable u.
Figure 5: Diagram of a fuzzy criticality assessment model.
The importance of fuzzy ‘IF-THEN’ rules stems
from the fact that human expertise and knowledge
can often be represented in the form of fuzzy rules.
For the fuzzy criticality analysis, the system
expresses the seriousness of a failure through its
severity, the failure probability through its
occurrence and how easy a failure can be detected
through its detectability. The fuzzy inference process
uses min-max inferencing to estimate the rule
conclusions base on the system input values
(Ladneer et al., 2003). The result of this process is
called the fuzzy conclusion. The applicability of a
rule is determined from the conjunction of the rule
antecedents. The defuzzification process builds a
crisp grading from the fuzzy conclusion set to
express the riskiness of the design so that corrective
actions and design revisions can be prioritized. The
defuzzification process is required to figure out the
meaning of the fuzzy conclusions and their
membership values, and resolve conflicts between
different results, which may have been triggered
during the rule evaluation. Several defuzzification
algorithms have been developed (Li et al., 2005;
Roychowdhury and Pedrycz, 2001). One of the
widely used algorithms, center of gravity, is applied
as it gives the average, weighted by their degree of
truth, of the support values at which all the
membership functions that apply reach their
maximum value.
The design decision support subsystem (DIIS)
consists of several main units: attributes input,
criticality assessment, exploring and grading, and
graphical user interaction. These units are supported
by a knowledge base and a material database. The
operational procedure is described in the following
with reference to Figure 6(a). The initiatory lists
AProductDevelopmentSystemusingKnowledge-intensiveSupportApproach
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