1. INTRODUCTION
Global industrial competition requires enterprises to use
innovative technologies for manufacturing and provision of goods
and services in the shortest possible time frame with minimum cost. To
achieve the goal of assuring short development cycles for new products,
they are developing a process-based knowledge-driven product
development environment by employing information technology. One major
emphasis is knowledge-based engineering (KBE), which focuses on
acquiring, storing, and utilizing knowledge for design and
manufacturing. One of the applications of KBE is the knowledge-based
system (KBS), which represents an interactive computer-based
decision-making tool that uses both factual and heuristic data acquired
from domain experts for problem solving.
The success of a KBS critically depends on the amount of knowledge
embedded in the system. Expert knowledge, which results from an
individual's extensive problem-solving experience, has been
described as unconscious knowledge (Mitta,
1989). As experts achieve greater competency, their ability to
explain the fine details associated with problem solving strategies
degrades. Intermediate solution steps are unconsciously performed as a
matter of routine as strategies are compressed into a few major steps
(Gaines, 1987). Thus, not all of the
knowledge involved can be decoded from schema to a semantic
representation that is available in human working memory and hence is
not accessible for the experts to report. This is especially true in
the field of manufacturing, where the experts are those who actually
work with the machines on the shop floor. They usually do not receive
formal scientific training, and most of their knowledge is biased
toward their own heuristic. Although they can easily solve a complex
problem, they usually have great difficulty explaining why and how they
make a particular decision. This is the hurdle in smooth knowledge
acquisition (Ham & Lu, 1988). In
addition, the iterations involved in knowledge acquisition and
incremental development for building up a complete KBS render the
manual approach highly time consuming. Because knowledge acquisition is
a lengthy and painful process, it is often referred to as the
bottleneck in KBS development (Gonzalez &
Dankel, 1993).
To overcome the knowledge acquisition bottleneck, we developed an
integrated knowledge acquisition methodology, which targets
manufacturing application, as shown in Figure
1. The key features and advantages of the methodology are that
it can do the following:
- maintain a database of problem solving cases, which allows domain
experts to verify their knowledge and discover data discrepancies and
knowledge engineers to gain better understanding of the problem
domain;
- automatically extract high-level rules from the database, which
allow knowledge engineers to independently acquire rule-type knowledge
and become “pseudo-experts” (as a result, they can interact
more effectively with domain experts);
- integrate domain experts' subjective knowledge with general
knowledge embedded in examples, which results in a system that covers a
much wider problem domain than otherwise possible; and
- provide closed-loop knowledge acquisition and utilization, which
results in optimal knowledge management to achieve true manufacturing
intelligence.
The integrated knowledge acquisition methodology.
2. BACKGROUND
In the 1970s, KBSs emerged as software systems that behave like human
experts when solving application problems in certain domains. The major
reason that prohibited both building and maintaining such systems was
the lack of robust and highly efficient computational technologies.
Therefore, developing new technologies received a great deal of
attention. Facilities to develop large KBSs exist, for example,
development methodologies such as KADS (Wielinga et
al., 1992), environments such as KEATS (Motta
et al., 1988), and shells such as DEX (Bohanec & Rajkovič, 1990). Constructing
KBSs to test the feasibility of new technologies and approaches brought
forth rather promising results. The field of KBSs has expanded
enormously. Applications of KBS range from medical diagnosis, chemical
analysis, geological exploration, computer configuration, and recently
to real-time process control, nuclear power plant operation, and marine
navigation (Coenen & Bench-Capon, 1993). In
the manufacturing and production environment, expert systems have been used
for tasks such as scheduling, engineering design, plant layout, manufacturing
system modeling, product quality control, process planning, and production
management (Mital & Anand, 1994).
However, the maintenance difficulty of KBSs hindered their general
acceptance. In addition, the building of large-scale commercial KBSs is
always associated with long development time, high costs, and high
risk. Just as the software crisis resulted in the establishment of the
discipline of software engineering, the unsatisfactory situation in the
construction and maintenance of KBSs made clear the need for
engineering the development procedure for KBSs. In other words, there
is an urgent need to construct and maintain KBSs in a systematic and
controllable manner (Studer et al., 1998).
A typical KBS consists of three basic components: the knowledge base
(KB) indicating the domain application area, the system context and
working memory, and the inference engine controlling the
problem-solving strategy. The KB comprises the knowledge that is
specific to the application domain, including such things as simple
facts about the domain, rules or frames that describe relations or
phenomena in the domain, and possibly also methods, heuristics, and
ideas for solving problems in this domain. The inference engine
contains methods of manipulating the knowledge in the KB.
The most popular KBS approach adopted by the industry, the symbolic
artificial intelligence (AI) approach, is usually applied for
constructing the KB of a KBS. In this approach, the knowledge is
represented using IF–THEN production rules; in this way, the KB
can be called the rule base (RB). Such a knowledge-based system is also
called an expert system or production system. It symbolically
manipulates IF–THEN rules to solve various problems.
Although this approach is successful, it suffers from the so-called
knowledge acquisition bottleneck because it is challenging and time
consuming to acquire and represent knowledge in terms of rules. In
other words, this bottleneck obstructs the efficiency and effectiveness
of building the RB of a KBS. In addition, the performance of the
resultant KBS will be impaired, when the amount of extracted rules is
very large and certain flaws such as redundancy, conflict, subsumption,
and circularity exist (Higa, 1996).
Recent advance in information technology has resulted in several
learning techniques that can potentially be used for automatic
knowledge acquisition, including artificial neural networks and
decision tree learning (Mitchell, 1997).
Neural networks belong to a family of models that are based on a
learning by example paradigm in which problem-solving knowledge is
automatically generated according to actual examples presented to them.
The knowledge, however, is represented at a subsymbolic level in terms
of connections and weights. Neural networks act like black boxes in
providing little insight into how decisions are made, which limits
their usefulness in many decision support applications. On the other
hand, decision trees can be easily translated into highly intelligible
IF–THEN rules. However, current decision tree learning algorithms
are only suited for problems that are described by a fixed set of
attributes (adding new attributes is impractical or inconvenient);
parameters should have discrete values (if not, discretization should
be carried out); and the target function to be learned should have
discrete output values. Some other shortcomings of decision tree
learning are discussed in Quinlan (1993).
In order to overcome these problems, techniques such as data
discretization, automatic rule extraction, and rule simplification and
pruning are needed. These techniques, described in Section 3, are the
major components of a systematic approach for rapid KBS development.
3. INTEGRATED KNOWLEDGE ACQUISITION
The system architecture for integrated knowledge acquisition is
illustrated in Figure 2. Engineering raw data
containing the cases is accumulated by observing and recording the
operations from experienced operators. Raw data are preprocessed using
methods such as discretization, normalization, and cleansing.
Apparently, wrong cases are removed from raw data after consulting with
domain experts. For those cases with similar inputs and outputs, some
of them can be removed as redundant cases after data discretization.
After data preprocessing, each of the input attributes can be described
by a fuzzy linguistic term (such as small, medium, and large). Because
each linguistic term can be represented by a set, a continuous-valued
input can thus be classified into a specific set and represented in a
binary scheme. After all the cases have been converted using linguistic
terms, some inconsistent cases can be removed. This procedure can
effectively scale down the cases in engineering raw data and makes the
subsequent rule extraction easier. Finally, rules are extracted through
neural network learning or using decision tree learning. These rules
are described using humanly intelligible linguistic terms, which can be
easily verified, modified, or supplemented by domain experts. Rule
pruning and simplification will make the RB more concise and
consistent. Because linguistic terms (which are imprecise) are
involved, a Mamdani-type fuzzy inference mechanism is used as the
inference engine.
It should be noted that, during the process of eliciting knowledge
from industry personnel, some knowledge is deductive (from general to
specific, from premise to conclusion), some is inductive (from
individual cases to general conclusion), and some is abductive
(unbounded inference). The KBSs discussed in this paper handle
deductive knowledge. However, this paper points out that some
quantitative method can be introduced to transfer the other two types
of knowledge (inductive and abductive) into deductive knowledge. For
example, probability can be used to measure the confidence of the
conclusion of an abductive rule. Such quantitative information is
represented by the weight of a rule, which will be discussed.
3.1. Discretization
Discretizing continuous parameters is inspired in part by the fact
that experts usually describe parameters using linguistic terms instead
of an exact value. There are several advantages of data discretization:
it provides regularization because it is less prone to variance in
estimation from small fragmented data, the amount of data can be
greatly reduced because some redundant data can be identified and
removed, and some rule extraction method such as decision trees can
perform better if all of the continuous-valued attributes are
discretized before rule induction.
Equal width is one of the most frequently used unsupervised data
discretization methods. It is also the simplest one, which divides the
range of observed values for a variable into N intervals of
equal sized intervals. The range of each interval is calculated by
where the number of intervals should be determined by users.
This method applies to the situation where the distribution of sample
data is quite uniform. However, it is vulnerable to outliers that may
drastically skew the range (Catlett, 1991). A
statistically justified heuristic method for supervised discretization
is the chi-merge algorithm (Kerber,
1992). This algorithm begins by placing each observed real value
into its own interval and proceeds by using the χ2 test
to determine when adjacent intervals should be merged. The extent of
the merging process is controlled by the use of a χ2
threshold α, indicating the maximum χ2 value that
warrants merging two intervals. The author reports that on random data,
a very high threshold must be set to avoid creating too many
intervals.
Chi2 (Liu & Setiono, 1997) is
a variant of the chi-merge algorithm. It consists of two phases. In
phase 1, a rough common α (significance level) for all variables is
used. In phase 2, the intervals are further merged using a separate
α for each variable. In general, Chi2 can form simpler terms, which
leads to improved predictive accuracy. However, it is computationally
complicated because it takes each different data point as an initial
interval. In addition, it merges variables sequentially rather than
trying to minimize the inconsistency resulting from merging. To
overcome these drawbacks, we developed a greedy chi-merge
algorithm for data discretization.
Greedy chi-merge also contains two phases: crude discretization and
fine-tuning. Compared to the Chi2 algorithm, which takes the number of
distinct values of a variable as the initial number of intervals,
greedy chi-merge performs an initial partitioning based on the change
in the output value, as illustrated in Figure
3. In this way, the number of intervals for each variable can be
greatly reduced.
The initial partitioning of greedy chi-merge.
The next phase, fine tuning, is based on greedy search, which always
chooses to merge the intervals of a variable that causes the least
inconsistency rate among all mergeable variables. Although all the
attributes are considered simultaneously, only the merging that causes
the lowest inconsistency rate takes place. The pseudocodes of the
greedy chi-merge are shown in Figure 4.
The pseudocodes for greedy chi-merge.
The problem of data inconsistency results when, after discretization,
data samples exist in which the inputs are the same but the outputs are
different. The inconsistency rate is calculated using the following
expression, based upon the method proposed by Liu and Setiono (1997):
where N is the total number of cases,
ni is the number of cases with the same
input (i = 1, 2,…, I),
[sum ]i
ni = N;
ci, j is the count of class
j in ni (j = 1,
2,…, J), [sum ]i
ci, j =
ni; and
3.2. Rule extraction
Once all continuous variables are discretized, linguistic terms can
be used to describe these variables. The original data samples are
converted accordingly. As a result, all the inputs and outputs become
discrete. A procedure, which utilizes the learning ability of a neural
network, has been developed to extract rules for these converted data
samples. The details of the procedure have been described in a previous
publication (Huang et al., 2001). An outline
of the five-step approach is provided in Figure
5. In summary, the converted data is represented using a binary
scheme and used to train a neural network, which is specially
structured according to the linguistic terms used. Linguistic
IF–THEN rules are then extracted by analyzing the connections and
weights of the trained neural network, using a dynamic depth-first
search algorithm to reduce computation complexity.
Rule extraction using a neural network.
An alternative rule extraction approach is through the use of
decision tree learning. Decision trees are currently a highly developed
technique for partitioning data samples into a set of covering decision
rules (Weiss & Kulikowski, 1991).
Decision tree learning algorithms such as ID3 and C4.5 have been widely
publicized by Quinlan. Readers interested in the details should refer to
Quinlan (1986, 1993).
Once the decision tree is constructed for a specific problem, rules can be
extracted directly from left to right and from top to bottom of the decision
tree. Usually the number of rules extracted equals the number of leaves
existing in the decision tree. Decision tree induction algorithms scale up
very well for large data sets.
The extracted rules, represented using a linguistic IF–THEN
format, are easily understood. They can be used to enrich the knowledge
engineer's understanding of the underlying domain. With this
knowledge, the knowledge engineer can have a more intelligent interview
with domain experts. Asking domain experts more detailed questions can
result in more refined and comprehensive knowledge in that domain. The
knowledge engineer can then transfer the unstructured knowledge
acquired from domain experts into structured knowledge in IF–THEN
format and combine these rules with rules automatically extracted from
data samples.
3.3. Rule simplification and pruning
The performance of a KBS will be adversely affected when the KB
increases in size and contains conflicting rules. In this sense, a
compact KB is desirable. However, a trade-off always exists between
completeness (covering the entire knowledge domain) and compactness
(using as few rules as possible). In the manufacturing domain, shop
floor operators prefer to have a small amount of highly intuitive rules
by sacrificing a small loss in predictive accuracy. Therefore, we
believe rule simplification and pruning are justified in manufacturing
application. Two concepts, namely, PofM (Possibility of Merging) and
AofP (Accuracy of Prediction), are introduced to reduce the size of a
KB while maintaining an acceptable level of performance. Details of
rule simplification and pruning are discussed as follows.
When rules are provided by domain experts, they may be in a compound
format. A rule that contains OR and/or NOT is a compound rule,
which brings potential and indetectable inconsistency and thus hinders
the maintainability of rules (Higa, 1996).
Automatically extracted rules are never in a compound format. Compound
rules acquired manually should be transformed in order to maintenance
consistency. For example, we have the following rule:
- IF (a1 is LARGE OR a2 is MEDIUM) AND
(a3 is NOT SMALL) THEN o1 is MEDIUM
Assume that a3 is described using three linguistic terms,
SMALL, MEDIUM, and LARGE. The rule can be transformed into four
noncompound rules:
- IF a1 is LARGE AND a3 is MEDIUM THEN
o1 is MEDIUM
- IF a1 is LARGE AND a3 is LARGE THEN
o1 is MEDIUM
- IF a2 is MEDIUM AND a3 is MEDIUM THEN
o1 is MEDIUM
- IF a2 is MEDIUM AND a3 is LARGE THEN
o1 is MEDIUM
A typical problem with automatically extracted rules is that the
rules could overlap, which means these rules can be combined to form a
simpler rule. For example, we have the following three rules:
- IF a1 is SMALL AND a2 is LARGE THEN
o1 is LARGE
- IF a1 is MEDIUM AND a2 is LARGE THEN
o1 is LARGE
- IF a1 is LARGE AND a2 is LARGE THEN
o1 is LARGE
Assume that a1 is described using three linguistic terms,
SMALL, MEDIUM, and LARGE. These three rules can be combined into one
simpler rule:
- IF a2 is LARGE THEN o1 is LARGE
The Quine–McCluskey algorithm is known as an approach of
Boolean functions simplification (McCluskey,
1956). In this paper, it is used to automatically combine
overlapping rules that may exist in the automatically extracted rule
set. When combining manually acquired rules with automatically
extracted rules, especially when compounded rules are transformed,
redundant rules may appear. Redundant rules means that rules having
identical premises also have identical consequences. Removal of
redundant rules is trivial because it can be realized through a simple
search.
Rules are simplified once redundant rules are removed and overlapping
rules are combined. A further step is to prune these rules to make the
rule base more compact. The criterion used for pruning is the
generalization ability (predictive accuracy). The user may provide a
testing data set and define an acceptable level of predictive accuracy.
Rules are pruned by merging similar rules and removal of insignificant
rules, as long as these actions do not result in a predictive accuracy
with an unacceptable low level. This is accomplished by using the
concepts of PofM and AofP.
When a weight is associated with a rule, as in the case of rule
extraction using dynamic depth-first search (the neural network
approach), an insignificant rule means a rule that has a small weight.
If a rule does not have a weight originally, as in the case of rule
induction using ID3 or rule provided by a domain expert, an
insignificant rule is one that applies to few data samples. In this
case, we can assign a weight to each rule based on how many data
samples they apply. Thus, weight is used as a unified measure for rule
significance.
The rule simplification and pruning algorithm is shown in Figure 6 using the following definitions:
[bull ] SR (Similar Rules): rules that have identical output values and
only one different input value.
[bull ] KI (Key Input): the input that distinguishes SR.
[bull ] SRG (Similar Rule Group): a rule group that holds all SR. Note that
a rule can belong to different SRG.
[bull ] MSRG (Mergeable Similar Rule Group): a SRG where the KI values of
all the rules have adjacent values and the number of the rules equals
to the number of available values for the KI. For example, the
following rules can be put into a MSRG:
- IF a1 is LARGE AND a2 is MEDIUM THEN o1
is MEDIUM
- IF a1 is SMALL AND a2 is MEDIUM THEN o1
is MEDIUM
- IF a1 is MEDIUM AND a2 is MEDIUM THEN o1
is MEDIUM
[bull ] PofM: each MSRG has a PofM value
[bull ] DC (Don't Care): An input does not appear in a rule; its value
can be ignored by the rule.
[bull ] AofP: In general, previous unseen cases are used to test the
predictive accuracy of a rule base. AofP is defined as
Rule simplification and pruning and an algorithm.
Note that all rules are in digital format, for example, 2 0 4
−1 5. The first 3 digits stand for the input value, 5 is the
output value, and −1 is for wherever the DC attribute exists.
Each attribute is represented by a number, ranging from 0 to the number
of intervals −1. Take the three rules listed above for MSRG as
example. Two attributes are concerned, a1 and a2.
Both have three intervals; so does the output. The three rules are
represented as 2 1 1, 0 1 1, and 1 1 1.
4. CASE STUDIES
4.1. Benchmark case
To illustrate how to use automatic knowledge acquisition to rapidly
develop a KBS and to demonstrate its effectiveness, the popular iris
flower classification problem is used as a benchmark case study. The
problem is based on a data set first used by Fisher (1936) for illustrating discriminant analysis
techniques. The classification problem then became a standard benchmark
problem for testing different classification methods. The data set can
be obtained from the University of California at Irvine ftp server
(ftp://128.195.1.46/pub/machine-learning-databases/iris/).
It contains 150 patterns: 50 of them belong to the class of Iris
setosa, 50 belong to the class of Iris versicolor, and 50
belong to the class of Iris virginica. There are four input
parameters to describe the three classes of flowers, namely, sepal
length, sepal width, petal length, and petal width. They are all
continuous-valued attributes.
A knowledge-based system is developed with the following steps.
First, after discretization with greedy chi-merge, two parameters of
sepal length and sepal width are removed because they have only one
interval. Both petal length and petal width have three intervals after
merging. The discretization result is shown in Table
1.
Data discretization for the Iris classification problem
Second, we used 75 data samples with odd index for training and the
rest for testing. Using the dynamic depth-first search algorithm, four
rules are extracted as follows. The weight attached to a rule shows its
significance degree, as described in Section 4. The weights of rules
have been normalized for easy handling, so weight 1.0 stands for the
most significant and weight 0.0 for insignificant.
- IF petal length is SMALL AND petal width is SMALL, THEN iris class
is Iris Setosa (weight 1.0)
- IF petal length is SMALL AND petal width is LARGE, THEN iris class
is Iris Setosa (weight 0.035636)
- IF petal length is MEDIUM, THEN iris class is Iris Versicolour
(weight 1.0)
- IF petal length is LARGE, THEN iris class is Iris Virginica (weight
1.0)
Third, after rule simplification and pruning, the following rules are
obtained:
- IF petal length is SMALL AND petal width is SMALL, THEN iris class
is Iris Setosa (weight 1.0)
- IF petal length is MEDIUM, THEN iris class is Iris Versicolour
(weight 1.0)
- IF petal length is LARGE, THEN iris class is Iris Virginica (weight
1.0)
Fourth, these rules are used to develop a fuzzy inference system.
Triangular membership functions are used for the input parameters and
singleton spikes are used for output. Among the 75 testing data
samples, 70 are correctly classified.
A comparison with prior results published in the literature is shown
in Table 2. Although our approach did not
achieve 100% accuracy, it yielded a more concise rule set (three rules)
while still maintaining high accuracy (93.3%). Compared with the
approach we proposed, REFuNN and RULES-3 Plus need many more rules to
attain higher accuracy; REFuNN and Premises Learning use much larger
training sets, which means higher computing cost. A trade-off exists
here between conciseness and complexity. In a manufacturing
environment, easy to understand and concise rules are preferred by
operators to guide their daily operations. Therefore, a smaller rule
set that has acceptable performance is desirable.
Comparison of the Iris classification problem
4.2. Industrial application
The integrated knowledge acquisition approach is also successfully
applied to a real-world manufacturing process, drop hammer forming.
Drop hammer forming is one of the most versatile processes for forming
sheet metal parts, which is used extensively by B.F. Goodrich
Aerospace/Aerostructures group in the production of aircraft engine
nacelles. Basically, three process methods are used for drop hammer
forming: bare punch, blow down, and blow up. Bare punch is the simplest
procedure. Parts are produced using a single hammer strike and no
forming aids are needed. Blow down and blow up procedures require the
use of forming aids and multiple hammer strikes. The forming aids,
usually rubbers, are placed on (blow down) or under (blow up) the part
to form a pyramid. During the forming process, the pyramid causes the
impact force to flow in the direction of the least rubber build up,
thus preventing wrinkles and folding.
Operator skills play a critical role in assuring product quality in
drop hammer forming. Experienced operators can produce high quality
products by adjusting die configuration, using appropriate forming
aids, and controlling punch blow. Their skills are accumulated through
years of hands-on experience, and they have great difficulty explaining
how and why they make a certain decision. Therefore, new operators have
to learn through observation. This is very inefficient and it usually
takes at least 6 months for a new operator to gain enough knowledge to
be able to work independently. B.F. Goodrich engineers attempted to use
traditional interview-based techniques to develop a KBS. They were not
successful, mainly because the knowledge engineers have very little
understanding of the drop hammer forming process, while experienced
operators typically have no formal engineering education and were
unable to explain the decisions they made.
Armed with the integrated knowledge acquisition method, a student was
sent to B.F. Goodrich's drop hammer forming facility at Chula
Vista, California. An initial interview with domain experts identified
the following inputs: material type, maximum draw depth, presence of
transitions and corners, and die type; while the outputs are identified
as the forming process method, number and type of forming aids, and
number of punch blows. For new operators, to decide which forming
process method is the most challenging task. Therefore, we worked on
rule extraction for process method determination first. We collected 73
historical cases, 35 of them are used for training and the remaining
used for testing. Among all of the inputs, only maximum draw depth has
continuous values. We used equal width as the discretization method and
four intervals were formed. The following rules are obtained:
- IF maximum draw depth is LARGE AND material type is SOFT AND
transitions and corners is YES, THEN forming process is BLOW UP (weight
1.0)
- IF maximum draw depth is LARGE AND material type is SOFT AND die
type is MALE, THEN forming process is BLOW UP (weight 0.297073)
- IF maximum draw depth is LARGE AND material type is HARD AND
transitions and corners is YES AND die type is MALE, THEN forming
process is BLOW UP (weight 0.267993)
- IF transitions and corners is YES, THEN forming process is BLOW
DOWN (weight 1.0)
- IF transitions and corners is NO, THEN forming process is BARE
PUNCH (weight 1.0)
These rules achieved a 95% predictive accuracy when applied to the
testing cases. With these rules, the student gained a good
understanding of the drop hammer forming process. A final interview
with domain experts was then conducted. The experts concluded that
these rules are valid and were able to provide additional details.
Specifically, part material can determine the selection of different
types of rubber as forming aids. If part material is hard and heat
treatment is required before the forming process is applied, then
harder and more heat resistant rubbers are needed as forming aids. In
addition, thinner rubber pads are used for harder parts. The material
of a part, combined with its maximum draw depth, can be used to decide
the number of punch blows needed to produce the part. Softer materials,
such as Aluminum, can be drawn in one stage to around 1 in. However,
hard materials, such as titanium, can only be drawn in one stage to a
maximum of 0.5 in. When the thickness of forming aids is determined,
denoted t, then the number of punch blows (denoted n)
can be easily calculated as n = m/t + 1.
One sheet of forming aids is removed after each punch blow. The last
blow is applied when all the forming aids are removed. Combining the
knowledge acquired, a software tool is then developed to assist
operator decision making in drop hammer forming. Figure 7 shows a snapshot of the software tool.
The drop hammer forming digital assistant.
Some examples are used to illustrate how the acquired process
knowledge is applied for drop hammer forming process planning. The tool
shown in Figure 8a is used to produce an
aluminum part. Its maximum draw depth is 3 in. Since it is a male tool
and there are four corners, forming aids need to be placed under the
part (see Fig. 8b). In other words, the blow
up process method is used. Since aluminum is soft, rubber pads are used
as the forming aid. Three 1-in. rubber pads are used since the maximum
draw depth is 3 in. One sheet of rubber is removed after each punch
blow. The last punch blow is applied when all three sheets of rubber
are removed. Therefore, a total of four punch blows are needed.
A part that requires blow up: (a) tool and (b) configuration.
The tool shown in Figure 9a is also used to
produce an aluminum part. Its maximum draw depth is 0.5 in. Since it is
a female tool and there are transitions, the blow down process method
is used. One sheet of 0.5-in. rubber pad approximately the size of the
part is placed on top of the part (Fig. 9b).
After one punch blow is applied, the rubber pad is removed. Another
punch blow is then applied to obtain the final part.
A part that requires blow down: (a) part and tool and (b) forming
aids placement.
Figure 10 shows a tool that is used to
produce an aluminum part. The tool is essentially flat, with a maximum
draw depth (the difference between the highest and the lowest point of
the area enclosed by the bead) of 0.05 in. It has no transitions. Note
that the bead is used to lock the part. Excess material will then be
trimmed away to obtain the final part. Therefore, there are no corners
either. In this care, bare punch is sufficient to produce the part.
Care should be exercised when determining the presence of
transitions.
A nearly flat part with no corners and transitions.
Figure 11 shows a not-so-flat part that
clearly shows a transition, which needs to be produced using blow down
rather than bare punch.
A not-so-flat part with a transition.
5. CONCLUSION
This paper presents an integrated automatic/interactive knowledge
acquisition approach to rapidly develop knowledge-based systems. This
approach relies on several key algorithms, including data
discretization, rule extraction, and rule simplification and pruning.
After data discretization, linguistic terms can be used to describe the
input and output parameters of the underlying problem. This allows the
extraction of IF–THEN rules that are compatible with domain
experts' heuristic reasoning. As a result, domain experts can
easily verify these rules and provide additional rules in the same
format. These rules are then combined and processed using rule
simplification and pruning, which results in a more compact rule base.
This paper recognizes the trade-off between completeness and
compactness of rules. The goal is to obtain concise and easy to
understand knowledge while maintaining an acceptable level of system
performance.
A benchmark case study showed that the proposed approach has
satisfactory performance. An industrial case study further showed its
practicality and effectiveness. The research work related to this paper
is useful to build up knowledge based systems with small rule sets.
This is somehow a neglected aspect of AI in engineering, especially in
manufacturing. The concise rules can form the foundation of training
modules that can jump-start workers' skills to new and higher
levels of competence.
The effectiveness of the algorithms also depends on the quality of
the training and testing data. This paper assumes the data used is
representative of the problem domain. If not, some preprocessing should
be carried out, which forms another direction of research work. Major
tasks in this area include missing data treatment, data cleansing,
dimensionality reduction, and so forth, which are beyond the scope of
this paper. Further research will focus on automatic tuning of the
developed KBS. Note that fuzzy inference is used to operate on the RB;
thus, the membership functions used will influence the predictive
accuracy. We are currently developing automatic tuning algorithms based
on neural network learning that can adjust parameters of membership
functions for improved performance.
