ABSTRACT
AI is a filed of study that encompasses computational
techniques for performing tasks that apparently require intelligence when
performed by humans. Such problems
include diagnosing problems in automobiles, computers and people, designing new
computers writing stores and symphonies, finding mathematical theorems,
assembling and inspecting products in factories. It is a technology of information processing
concerned with process of reasoning, learning and perception.
AI is both an art and a
science. The activity of developing
intelligent computer systems employs both proved mathematical principles,
empirical results of studying previous systems.
The ability of systems to sense its
environment, make decisions and control actions is known as intelligence. Intelligent action when carried out on a
machine are known as artificial intelligence which can be formally defined as-
“:It is a branch of computer science, which deals with a growing set of
computer problem solving techniques being developed to imitated the human
thought or decision making process on to produce the same results as those
processes.”
1.
INTRODUCTION:
AI in Design research is concerned with the
use of AI techniques to study design.
This is done by making hypotheses about designing and producing modes of
design, knowledge and activity. Using
these hypotheses and modes, computer programs (i.e., design systems) are built
that can aid designers and carry out some portion of the design process. Thus hypotheses about design are tested and
refined using design systems.
Design, as a complex, goal-directed,
problem-solving activity, has long been studied as a topic in AI. Apart from the intellectual challenge, it is
also an important contributor to economic success, and is a fundamental
precursor to manufacturing.
AI techniques and components of an
intelligent manufacturing system. Since
its emergence in the 1950s AI has provided several techniques with applications
in manufacturing. In the early years,
knowledge-based systems, (KBS) attracted great attention. Recently neural networks (NN), case-based
reasoning (CBR), genetic algorithms (GAs) and fuzzy logic have attracted more
attention and have been successfully employed in manufacturing.
This section contains a very brief
and much simplified outline of the main.
AI techniques and the components of simplified model of an intelligent.
Manufacturing system.
2. AI TECHNIQUES:
2.1 KBs: The first attempt to be widely used to equip
manufacturing systems with some degree
of intelligence was the use of KBS.
They seek to incorporate human knowledge about an application area,
usually elicited from experts in the particular domain, so that the system can
automatically replicate aspects of best practice. The human knowledge is represented using the
IF- THEN production systems or more structured formats such as frames and
semantic nets.
2.2 NNs: NNs are
based on ideas about how the brain may work.
Input stimuli (e.g. the parameter values have been widely used in many
classification and optimization situations.
History data are used to “train” the network, automatically determining
the most appropriate configuration of the hidden network.
2.3 Fuzzy logic: Fuzzy logic allows the representation and
processing of uncertain information such as linguistic statements, for example
the customer desires a car, which is “fairly luxurious”. Judging the degree not is used in
mathematical modeling or target setting.
2.4 GAs: GA use
ideas from population genetics for solving complex global optimization
problems. A pool of potential candidate
solutions evolves through reproduction and mutation of the fittest and
elimination of the least promising solutions of each generation are made “extinct”.
2.5 CBR: In CBR, the intelligent component of the
system contains a history of past problems and the (successful) solutions,
applied. Future problems can then be
considered through analogy with these past cases to home in rapidly on the most
promising type of solutions. A further
step is incorporating machine learning though the updating of the ‘case-base”
with those for which the solutions suggested proved successful.
3. COMPONENTS OF AN INTELLIGENT
MANUFACTURING SYSTEMS:
3.1 INTELLIGENT DESIGN: The importance of product design is
undeniable. A firm’s products or
services are typically the primary source and focus of contact with its
customers, and the development of new designs plays a key role in establishing
and maintaining a competitive position for most firms. There are many problems in design
manufacturing systems.
3.2 FUZZY LOGIC: Optimizing the efficiency in cutting a sculptured
surface using numerically controlled machining techniques needs to carefully
consider the relationship between cutting edges and surfaces geometry. A fuzzy basis material removal optimization
approach is suggested by Ip (1998(to compensate the variation of cutting speed
due to the change of gradient on the sculptured surface in machining
process. Fuzzy modeling is used for the
penicillin-G conversion processs. A
linguistic fuzzy model, which represents, the kinetic term of the conversion,
is developed from experimental data by means of fuzzy clustering.
3.3 CBR: The frequent used of past experience by human
engineers when solving new problems has led to an interest in the use of CBR to
help automate engineering design. Purvis
and Pu (1998) developed a constraint based methodology for case
combination. The methodology is
implemented in a CBR system called COMPOSER and has been tested in two
design domains: assembly sequence design and configuration design. Gao et al (1998) used BR for mechanical plan
systems design. Design plans are stored
as the actual cases in the CBR system.
Cbr is used for the design of bar linkages and fixtures design.
A product may have different ways of
disassembly an experence is important during disassembly if e want to satisfy
goals such as part reuse, recycle or discard.
Zeid et al (1997) have proposed a CBR approach to solve design
for disassembly (DFD) problem.
3.4 HYBRID SYSTEMS: Chen et al (1998) have developed an
integrated expert system that consist of a knowledge base, a database,
pattern-recognition, artificial NN and GA modules for complicated chemical
reaction systems used t prepare industrial materials. The system has been used in many applications
including the production of alloy steel, synthetic rubber ceramic materials
production and materials design of composite materials, high temperature
superconductors, and ceramic, semiconductors.
Lee et al (1999) have developed
fuzzy non- linear programming modes to optimize machining operations. It uses fuzzy login together with traditional
mathematical programming to make a more flexible maker. The model also uses an NN model, which can be
used to assess the machinability of the machining operations. The output from the fuzzy non-linear
programming model provides the input for the NN model. A material design system has been developed,
utilizing mathematical modeling and knowledge-based approaches by Shivathaya
and Fang(1999). The KBS generates about
15-30 different target compositions for steelmaking for each customer
order. Fuzzy logic is applied in the system for the design of steel plates, to
rank the alternative target compositions for steelmaking according to the
degree to which they will satisfy customer’s requirements.
4. FUZZY MODELLING FOR AIR FUEL
RATIO CONTROL:
This
section outlines the development and use of a fuzzy model applied to a
simulated engine air fuel ratio (AFR) control system. Accurate control of AFR relies on knowledge
of airflow into the engine and the dynamics, which affect injected fuel within
the inlet manifold. The system is
non-linear with operating point dependent dynamics and possesses a time delay,
which varies, as a function of engine speed and inlet manifold air mass flow
rate. Whilst satisfactory control of the
system is achieved in steady-state using conventional PID control, there exist
difficulties when encountering transient conditions, such as sudden
acceleration/ deceleration and loaded/unloaded conditions. Essentially, a feed forward element is
introduced as a base controller which makes used of a fuzzy model; this adjusts
the fuel injection rate according to the current operating conditions of engine
crankshaft speed and inlet manifold pressure, with the air flow into the engine
being estimated using a fuzzy model. The
fuzzy model is identified using a clustering technique applied to
input-output data and the identified
model is used to set the feedforward fuel injection pulse width signal to
obtain the desired AFR. An advantage of
the approach is that measurements of inlet manifold pressure and manifold
airflow rate are not required. Rather,
use is made of a fuzzy model-based scheme which requires measurements of the
throttle plate angle, denoted >>
& cir; and the factor >> only, where >> = actual AFR/desired
AFR; with the control objective being to maintain at unity, thereby achieving
the desired stoichiometric ratio. The
quantity >> is measured using a linear exhaust gas oxygen (EGO) sensor
mounted in the exhaust system. Figure 2
shows the general control scheme where AB V, tim and tic denote the air by-ass
valve control signal and the contributing to the fuel injection signal it form
the feedforward and feedback controllers respectively. It should be noted that the feedback
controller, giving due consideration to the system time delay, is based on the
Smith predictor method. The set-pint of
unity and fig 2 Control scheme using PID controller and feedforward fuzzy model
for base level control in the measured value of >> are denoted >>
SP and >> e respectively.
One
of the major limitations of employing feedback control regulating >> is
that the closed loop includes a transport delay. It is further compounded by the fact that
this delay varies as a function of both engine speed and mass air flow rate. The results achieved using the fuzzy model
based feedforward element, shown in fig 2.
are encouraging and indicate that such a fuzzy model-based approach can
be effectively applied to transient control of >>. Transient conditions are simulated when the
engine crankshaft speed is fixed at 2,500 rpm and the position of the throttle
plate angle is varied.
5.
CONCLUSION:
This paper has
highlighted following areas:
- Introduction to AI techniques in design and components of intelligent manufacturing systems
- A case study of Fuzzy modeling for air fuel ratio control where fuzzy methodologies have shown distinct advantages over conventional mode based strategies, for control and decision making in two different applications. The results to date are encouraging, indicating potential for implementation of fuzzy logic methodologies in practice.
6.
REFERENCES:
·
Intelligent systems I manufacturing: current
developments and future prospects by-:
Farid Meziane, Sunil Vadera,
Khairy Kobbacy, Naqthan Proudlove
·
Applications of AI in design and manufacturing
systems by-:
Dr. Ken Brown and Mrs. Pat
Fothergill
·
Intelligent systems for optimization and control
by-:
K.J.Burnham, O.C.L. Haas and D.
J. G. JamesW
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