Suresh, Nallan C. "Neural Network Applications for Group Technology and Cellular Manufacturing"
Computational Intelligence in Manufacturing Handbook
Edited by Jun Wang et al
Boca Raton: CRC Press LLC,2001

©2001 CRC Press LLC

4

Neural Network
Applications for
Group Technology
and Cellular

Manufacturing

4.1 Introduction

4.2 Artificial Neural Networks

4.3 A Taxonomy of Neural Network Application
for GT/CM

4.4 Conclusions4.1 Introduction

Recognizing the potential of artificial neural networks (ANNs) for pattern recognition, researchers first
began to apply neural networks for group technology (GT) applications in the late 1980s and early 1990s.
After a decade of effort, neural networks have emerged as an important and viable means for pattern

the basis for the creation of manufacturing cells. Each cell is dedicated to manufacturing one or more
part families. The potential benefits from (properly designed) cellular manufacturing systems include:
• Reduced manufacturing lead times and work-in-process inventories
• Reduced material handling
• Simplified production planning and control
• Greater customer orientation
• Reduced setup times due to similarity of tool requirements for parts within each family
• Increased capacity and flexibility due to reduction of setup times, etc.
For implementing GT and designing cells, early approaches relied on

classification and coding systems

,
based on the premise that part families with similar designs will eventually lead to identification of cells.
Classification and coding systems involve introducing codes for various design and/or manufacturing
attributes. A database is created and accessed through these “GT codes.” This offers several advantages,
such as design rationalization and variety reduction and better data management, as mentioned above.
But the codification activity involves an exhaustive scrutiny of design data, possible errors in coding, and
the necessity for frequent recoding. The need for classification and coding systems has also been on the
decline due to advances in database technologies, especially the advent of relational databases.
Therefore, in recent years, cell design methods have bypassed the cumbersome codification exercise.
They have relied more on a direct analysis of part routings, to identify parts with similar routings and
machine requirements. Part families and machine families are identified simultaneously by manipulating
part-machine incidence matrices.

FIGURE 4.1

Elements of GT/CM. (From Suresh, N.C. and Kay, J.M. (Eds.), 1998,

Group Technology and Cellular

performance and application domain for various neural network architectures.

4.2 Artificial Neural Networks

Artificial neural networks have emerged in recent years as a major means for

pattern recognition,

and it
is this particular capability that has made ANNs a useful addition to the tools and techniques applicable
for group technology and design of cellular manufacturing systems.
ANNs are “massively parallel, interconnected networks of simple processing units (neurons), and their
hierarchical organizations and connections which interact with objects in the real world along the lines
of biological nervous systems” [Kohonen, 1984]. The basic elements of a neural network are the processing
units (neurons), which are the nodes in the network, and their connections and connection weights.
The operation of a neural network is specified by such factors as the propagation rule, activation/trans-
fer function, and learning rule. The neurons receive weighted input values, which are combined into a
single value. This weighted input is transformed into an output value through a nonlinear

activation
function

. The activation function could be a hard limiter, sigmoidal nonlinearity or a threshold logic
limit. This neuro-computing process is illustrated in Figure 4.2.

FIGURE 4.2

Neural computation.
x
1

..
w
1j
w
2j
w
3j
w
n-1,j
w
nj
y
j
Input Vector

©2001 CRC Press LLC

In a neural network, the nodes respond to information converging from other layers via the connec-
tions. The connection weights represent almost all the stored information in a network, and these weights
are updated in response to new information entering the system. The learning rule specifies how the
weights are to be updated in response to new information. For further details on basics of neural networks,
readers are referred to works such as Wasserman [1989] and McClelland and Rumelhart [1988]. It must
be stressed that all the above networks, though based on massive parallelism, are all still simulated using
conventional, sequential computing, awaiting the development of neuro-computing hardware in the
future.
Among the many properties of ANNs, their pattern recognition capability is of foremost relevance in
the context of GT/CM. Unlike traditional artificial intelligence (AI) methods, employing logic and rule-
driven procedures for pattern recognition, ANNs are adaptive devices that recognize patterns more
through experience. Neural networks also have the ability to learn complex patterns and to generalize
the learned information faster. They have the ability to work with incomplete information. Compared


the network is trained, so that the inputs, as well as information indicating correct outputs, are presented
to the network. The network is also “programmed” to know the procedure to be applied to adjust the
weights. Thus, the network has the means to determine whether its output was correct and the means
to apply the learning law to adjust its weights in response to the resulting errors. The weights are modified
on the basis of the errors between desired and actual outputs in an iterative fashion.
In

unsupervised learning,

the network has no knowledge of what the correct outputs should be, since
side information is not provided to convey the correct answers. As a series of input vectors are applied,
the network clusters the input vectors into distinct classes depending on the similarities. An

exemplar
vector

(representative vector) is used to represent each class. The exemplar vector, after being created, is
also updated in response to a new input that has been found to be similar to the exemplar. As all inputs
are fed to the network, several exemplars are created, each one representing one cluster of vectors.

Combined unsupervised–supervised learning

first uses unsupervised learning to form clusters. Labels are
then assigned to the clusters identified and a supervised training follows.
Many types of neural network models have been developed over the years. The taxonomy of neural
network models proposed by Lippmann [1987] is widely used in the literature. This classifies ANNs first

©2001 CRC Press LLC


TABLE 4.1

Pattern Classification Based on Design and Manufacturing Features

Supervised Learning

Unsupervised Learning
Application Area
Back-Propagation
Hopfield
Competitive Learning
Interactive Activation
Kohonen’s SOFM
ART1 and Variants
Fuzzy ART

Facilitate Classification and Coding

Kaparthi & Suresh [1991]
•
D

esign Retrieval Systems

Kamarthi et al. [1990]
Venugopal & Narendran [1992]
•
•

Part Family Formation

of the Opitz coding system. The examples demonstrated pertained to rotational parts, but extension to

TABLE 4.2

Pattern Classification Based on Part–Machine/Tool Matrix Elements

Supervised Learning

Unsupervised Learning
Application Area
Back-Propagation
Hopfield
Competitive Learning
Interactive Activation
Kohonen’s SOFM
ART1 and Variants
Fuzzy ART
Other Models

Block Diagonalization

Jamal [1993] •
Malave & Ramachandran [1991]
Venugopal & Narendran [1992a, 1994]
Chu [1993]
Malakooti & Tang [1995]
•
•
•
•

•
Burke & Kamal [1992, 1995]
Suresh & Kaparthi [1994]
Kaparthi & Suresh [1994]
Kamal & Burke [1996]
•
•
•
•

Capacitated Cell Formation

Rao and Gu [1994, 1995]
Suresh, Slomp & Kaparthi [1995]
•
•

Sequence-Dependent Clustering

Suresh, Slomp & Kaparthi [1999]
•

Part-Tool Matrix Elements

Arizono et al. [1995]
•

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prismatic parts was seen to be feasible. The network was viewed as an element of a computer-aided design

Middle (hidden) layer
Family 1
Input Patterns: Feature Vector
Family 3Family 2
Input layer

©2001 CRC Press LLC

layer provides a nonlinear mapping between the features and the part families. The number of neurons
required for the middle layer is normally determined through trial and error. Neural networks are at
times criticized for the arbitrariness, or absence of guidelines for the number of neurons to be used in
middle layers.
The input binary-valued vector is multiplied by the connection weight

w

ij

,

and all the weighted inputs
are summed and processed by an activation function

f

′

ι

(net
w

ij

a

pj

Equation (4.1)

a

pi

=

f

′

ι

(net

pi

) = 1/[1 + exp(-net


ε
δ

pi

a

pj

Equation (4.3)
These activation values are in turn used to calculate the net inputs and activation values of the processing
units in the output layer using Equations 4.1 and 4.2. Next, the activation values of the output units are
compared with desired target values during training. The discrepancy between the two is propagated
backwards using

δ

pi
= (t

pi

– a



(net

pi

)

Σ

j
δ

pk

w

ki

Equation (4.5)
With these discrepancies, the weights are adjusted using Equation 4.3. Based on the above procedure,
Kao and Moon [1991] presented a four-phased approach for forming part families, involving (1) seeding
phase, (2) mapping phase, (3) training phase, and (4) assigning phase. In the seeding phase, a few very
distinct parts are chosen from the part domain to identify basic families. In the mapping phase, these
parts are coded based on their features. The network is trained in the training phase utilizing the back-
propagation rule. In the assigning phase, the network compares a presented part with those with which
it was trained. If the new part does not belong to any assigned family, a new family is identified.
Moon and Roy [1992] developed a feature-based solid modelling scheme for part representations.