Wang, Jun et al "Applications in Intelligent Manufacturing: An Updated Survey"
Computational Intelligence in Manufacturing Handbook
Edited by Jun Wang et al
Boca Raton: CRC Press LLC,2001

©2001 CRC Press LLC

2

Neural Network
Applications in
Intelligent
Manufacturing:

An Updated Survey

2.1 Introduction

2.2 Modeling and Design of Manufacturing Systems

2.3 Modeling, Planning, and Scheduling of Manufacturing
Processes

2.4 Monitoring and Control of Manufacturing
Processes

2.5 Quality Control, Quality Assurance, and
Fault Diagnosis

2.6 Concluding Remarks



IBM Global Services

©2001 CRC Press LLC

such as the popular multilayer perceptron, are usually used as representational models trained using a
learning rule based on a set of input–output sample data. A popular learning rule is the widely used
backpropagation (BP) algorithm (also known as the generalized delta rule). It has been proved that the
multilayer feedforward neural networks are universal approximators. It has also been demonstrated that
neural networks trained with a limited number of training samples possess a good generalization capa-
bility. Large-scale systems that contain a large number of variables and complex systems where little
analytical knowledge is available are good candidates for the applications of feedforward neural networks.
Recurrent neural networks, such as the Hopfield networks, are usually used as computational models for
solving computationally intensive problems. Typical examples of recurrent neural network applications
include NP-complete combinatorial optimization problems and large-scale or real-time computation
tasks. Neural networks are advantageous over traditional approaches for solving such problems because
neural information processing is inherently concurrent.
In the past two decades, neural network research has expanded rapidly. On one hand, advances in
theory and methodology have overcome many obstacles that hindered the neural network research a few
decades ago. On the other hand, artificial neural networks have been applied to numerous areas. Neural
networks offer advantages over conventional techniques for problem-solving in terms of robustness, fault
tolerance, processing speed, self-learning, and self-organization. These desirable features of neural com-
putation make neural networks attractive for solving complex problems. Neural networks can find
applications for new solutions or as alternatives of existing methods in manufacturing. Application areas
of neural networks include, but are not limited to, associative memory, system modeling, mathematical
programming, combinatorial optimization, process and robotic control, pattern classification and rec-
ognition, and design and planning.
In recent years, the applications of artificial neural networks to intelligent manufacturing have attracted
ever-increasing interest from the industrial sector as well as the research community. The success in utilizing
artificial neural networks for solving various computationally difficult problems has inspired renewed

of the art of the research and highlight the recent advances in research and applications of neural networks
in manufacturing. Because of the vast volume of publications, this chapter considers only the works
published in major archival journals and selected edited books.

2.2 Modeling and Design of Manufacturing Systems

As representational models, artificial neural networks are particularly useful for modeling systems whose
underlying properties are too complex, too obscure, too costly, or too time-consuming to be modeled
analytically using traditional methods. The use of neural networks for modeling and design of manu-
facturing systems includes manufacturing decision making, product design storage and retrieval in group
technology, and formation of part families and machine cells for the design of cellular manufacturing
systems.
Chryssolouris et al. [3] applied neural networks, in conjunction with simulation models, for resource
allocation in job-shop manufacturing systems. Feedforward neural networks called multilayer perceptrons
trained using the popular backpropagation (BP) algorithm were used to learn the inverse mapping of the
simulation task: given desired performance measure levels, the neural networks output suitable values for
the parameters of resources. Based on results generated by a simulator, the neural networks were demon-
strated to be able to find a suitable allocation for the resources to achieve given performance levels. In a
related work, Chryssolouris et al. [4] applied neural networks, also in conjunction with simulation models,
to determine operational policies for hierarchical manufacturing systems under a multiple criteria decision
making framework called MAnufacturing DEcision MAking (MADEMA). Multilayer perceptrons were
used to generate appropriate criterion weights for an entire sequence of multiple criteria decisions on
manufacturing policies. This neural network approach is more appropriate for complex applications entail-
ing chains of decisions, such as job-shop scheduling, whereas conventional methods are preferable for single
or isolated decisions. Madey et al. [5] used a neural network embeded in a general-purpose simulation
system for modeling Continuous Improvement Systems (CIS) policies in manufacturing systems. A mul-
tilayer feedforward neural network trained using the BP algorithm was used to facilitate the identification
of an effective CIS policy and to provide a realistic simulation framework to enhance the capabilities of
simulations. The trained neural network was embedded in the simulation model code, so that the model
had intrinsic advisory capability to reduce time or complexity for linking with external software. The results

conjunction with a feature-based solid modeling system. The part features extracted from a model or
object database were used to train and test a multilayer feedforward neural network. Trained using the
BP algorithm, the neural network neurons signify an appropriate part family for each part. Besides
overcoming some limitations of traditional coding and classification methods, this approach offers more
flexibility and faster response.
Venugopal and Narendran [9] applied the Hopfield network to design storage and retrieval for batch
manufacturing systems. Binary matrix representations of parts based on geometric shapes were stored
in the Hopfield network. Test cases carried out on rotational and nonrotational parts showed the high
percentage of correct retrieval of stored part information using the neural network. The retrieval rapidity
is another major advantage of the neural network model. Such a storage/retrieval system could benefit
the design process by minimizing duplications and variety, thus increasing productivity of both designer
and planner, aiding standardization, and indirectly facilitating quotations. Furthermore, this approach
offers flexibility and could adjust to changes in products. Unfortunately, the limited capacity of the
Hopfield network constrained the possible number of stored designs.
Chakraborty and Roy [10] applied neural networks to part-family classification based on part geo-
metric information. The neural system consisted of two neural networks: a Kohonen’s SOM network
and a multilayer feedforward network trained using the BP algorithm. The former was used to cluster
parts into families and provide data to train the latter to learn part-family relationships. Given data not
contained in the training set, the feedforward neural network performed well with an accuracy of 100%
in most of test cases.
Kiang et al. [11] used the self-organizing map (SOM) network for part-family grouping according to
the operation sequence. An operation sequence based similarity coefficient matrix developed by the
authors was constructed and used as the input to the SOM network, which clustered the parts into
different families subsequently. The performance of the SOM network approach was compared with two
other clustering techniques, the k-th nearest neighbor (KNN) and the single linkage (SLINK) clustering
methods for problems varying from 19 to 200 parts. The SOM-network-based method was shown to
cluster the parts more uniformly in terms of number of parts in each family, especially for large data set.
The training time for the SOM network was very time-consuming, though the trained network can
perform clustering in very short time.
Wu and Jen [12] presented a neural-network-based part classification system to facilitate the retrieving

characteristics. Part families were created using a bond energy algorithm to partition the matrix of part
similarities. Machine cells were simply inferred from part families. The neural network simulated using
a spreadsheet macro showed to be capable of forming part families.
Based on the ART-1 neural network, Kusiak and Chung [17] developed a neural network model called
GT/ART for solving GT problems by block diagonalizing machine-part incidence matrices. This work
showed that the GT/ART neural network is more suitable for grouping machine cells and part families
than other nonlearning algorithms and other neural networks such as multilayer neural networks with
the BP learning algorithm. The GT/ART model allows learning new patterns and keeping existing weights
stable (plasticity vs. stability) at the same time. Kaparthi and Suresh [18] applied the ART-1 neural
network for clustering part families and machine cells. A salient feature of this approach is that the entire
part-machine incidence matrix is not stored in memory, since only one row is processed at a time. The
speed of computation and simplicity of the model offered a reduction in computational complexity
together with the ability to handle large industrial size problems. The neural network was tested using
two sets of data, one set from the literature and the other artificially generated to simulate industrial size
data. Further research is required to investigate and enhance the performance of this neural network in
the case of imperfect data (in the presence of exceptional elements).
Liao and Chen [19] evaluated the ART-1 network for part-family and machine-cell formation. The
ART-1 network was integrated with a feature-based CAD system to automate GT coding and part-family
formation. The process involves a three-stage procedure, with the objective of minimizing operating
and material handling costs. The first stage involved an integer programming model to determine the
best part routing in order to minimize operating costs. The first stage results in a binary machine-part
incidence matrix. In the second stage, the resulting incidence matrix is then input to an ART-1 network
that generates machine cells. In the last stage, the STORM plant layout model, an implementation of a
modified steepest descent pairwise interchange method is used to determine the optimal layout. The
limitation of the approach was that the ART-1 network needs an evaluation module to determine the
number of part families and machine cells.
Extending their work in [18], Kaparthi et al. [20] developed a robust clustering algorithm based on a
modified ART-1 neural network. They showed that modifying the ART-1 neural network can improve
the clustering performance significantly, by reversing zeros and ones in incidence matrices. Three perfectly
block diagonalizable incidence matrices were used to test the modified neural network. Further research

The first purpose is to group the machines into cells given as input the desired number of cells and
process plans. The second purpose is to calculate the loading on each machine given the processing
time of each part. The last purpose of the neural network is to propose alternative groups considering
duplicate machines. The expert system was used to reassign the exceptional elements using alternate
process plans generated by the neural network based on processing time and machine utilization. The
evaluation of process plans considered the cost factors of material handling, processing, and setup.
Finally, the neural network was updated for future use with any changes in machine utilization or cell
configuration.
Rao and Gu [25] proposed a modified version of the ART-1 algorithm to machine-cell and part-family
formation. This modified algorithm ameliorates the ART-1 procedure so that the order of presentation
of the input pattern no longer affects the final clustering. The strategy consists of arranging the input
pattern in a decreasing order of the number of 1’s, and replacing the logic AND operation used in the
ART-1 algorithm, with an operation from the intersection theory. These modifications significantly
improved the neural network performance: the modified ART-1 network recognizes more parts with
similar processing requirements than the original ART-1 network with the same vigilance thresholds.
Chen and Cheng [26] added two algorithms in the ART-1 neural network to alleviate the bottleneck
machines and parts problem in machine-part cell formation. The first one was a rearrangement algorithm,
which rearranged the machine groups in descending order according to the number of 1’s and their
relative position in the machine-part incidence matrix. The second one was a reassignment algorithm,
which reexamined the bottleneck machines and reassigned them to proper cells in order to reduce the
number of exceptional elements. The extended ART-1 neural network was used to solve 40 machine-
part formation problems in the literature. The results suggested that the modified ART-1 neural network
could consistently produce a good quality result.
Since both original ART-1 and ART-2 neural networks have the shortcoming of proliferating categories
with a very few patterns due to the monotonic nonincreasing nature of weights, Burke and Kamal [27]
applied the fuzzy ART neural network to machine-part cell formation. They found that the fuzzy ART
performed comparably to a number of other serial algorithms and neural network based approaches for
part family and machine cell formation in the literature. In particular, for large size problem, the resulting
solution of fuzzy ART approach was superior than that of ART-1 and ART-2 approaches. In an extended



3

parts) were used to evaluate
the performance of this approach. Compared with the serial implementation of the ART-1 neural network
in one process, the distributed processor based implementation could reduce the processing time from
84.1 to 95.1%. Suresh et al. [31] applied the fuzzy ART neural network for machines and parts clustering
with the consideration of operation sequences. A sequence-based incidence matrix was introduced, which
included the routing sequence of each part. This incidence matrix was fed into the fuzzy ART neural
network to generate the sequence-based machine-part clustering solution. The proposed approach was
used to solve 20 problems with size ranging from 50

3

250 to 70

3

1400 (machines

3

parts) and evaluated
by the measure clustering effectiveness defined by the authors. The results showed that the approach had
a better performance for smaller size problems.
Lee and Fisher [32] took both design and manufacturing similarities of parts into account to part-
family grouping using the fuzzy ART neural network. The design attributes, i.e., the geometrical features
of the part were captured and digitalized into an array of pixels, which was then normalized to ensure
scale, translation, and rotation invariant recognition of the image. The normalized pixel vectors were
transformed into a five-digit characteristics vector representing the geometrical features of the part by

automatically, which can be used to retrieve part drawing from the database or process plan from a variant
process planning system. The feasibility of the proposed system has been demonstrated by a case study.
However, the system was limited to those users who knew the machining operations, since machining
features of parts were required when using the feature-based CAD system.
Malakooti and Yang [36] developed a modified self-organizing neural network based on an improved
competitive learning algorithm for machine-part cell formation. A momentum term was added to the
weight updating equation for keeping the learning algorithm from oscillation, and a generalized Euclidean
distance with adjustable coefficients were used in the learning rule. By changing the coefficients, the
cluster structure can be adjusted to adopt the importance preference of machines and parts. The proposed
neural network was independent of the input pattern, and hence was independent of the initial incidence
matrix. On average, the neural network approach gave very good final grouping results in terms of
percentage of exceptional elements, machine utilization, and grouping efficiency compared with two
popular array-based clustering methods, the rank order clustering and the direct clustering analysis, to
ten problems sizing from 5

3

7 to 16

3

43 (machines

3

parts) in the literature.
Arizono et al. [37] applied a modified stochastic neural network for machine-part grouping problem.
A simplified probability function was used in the proposed neural network, which reduced the compu-
tation time compared with other stochastic neural networks. The presented neural network overcame
the local minimum problem existing in deterministic neural networks. The proposed neural network

manufacturing systems, modeling of continuous improvement systems, part classification and coding,
part-family and machine-cell formation, as shown in Figure 2.2. In system-level decision making appli-
cations, simulation was used in combination with neural networks to generate data used by the neural
network to implicitly model the system. In cellular manufacturing applications, neural networks used
to classify parts and machines permit easy identification of part families, machine cells, and exceptional
elements. Neural networks could also be used to assign new parts to an existing classification. Feedfor-
ward neural networks trained using the BP algorithm were popular for this application. Other types of
neural networks included ART networks, Hopfield networks, and SOM neural networks. Weaknesses of
neural networks for modeling and design of manufacturing systems result from neural networks them-
selves. Some parameters or constants must be determined on a trial-and-error basis. Also, neural network
methods cannot always guarantee an optimal solution, and several searches must often be taken to
improve the quality of the solution. Nevertheless, neural networks offer a promising alternative design
method with highly computational efficiency and are able to address some of the limitations of traditional
methods.
Given the ability to learn from experience and inherent parallel processing of neural networks, a neural
network approach allows the implicit modeling of systems using representative data, thus eliminating
the need for explicit mathematical analysis and modeling. Neural networks also have the unique ability
to solve problems with incomplete or noisy data. Furthermore, neural networks are not significantly
influenced by the size of the problem, because global computing is done in parallel and the local computat
ion in each neuron is very simple. Neural networks are therefore appropriate for solving large industrial
problems. As dedicated neurocomputing hardware emerges and improves, neural networks will become
more beneficial for solving large-scale manufacturing modeling and design applications.

FIGURE 2.2

Hierarchy of neural network applications for manufacturing system

modeling and design.
Legends
ART: Adaptive Resonance Theory

Liao and Lee /ART (1994)
Chen and Cheng /ART (1995)
Burke and Kamal /ART (1995)
Chang and Tsai /ART (1997)
Euke et al. /ART (1998)
Suresh et al. /ART (1999)
Lee and Fischer /ART (1999)

©2001 CRC Press LLC

2.3 Modeling, Planning, and Scheduling

of Manufacturing Processes

Typical tasks in process planning include material selection, process selection, process sequencing, and
machining parameter selection. Planning and scheduling generally require two steps: the input–output
process modeling and the selection of parameters to optimize the process with given constraints. Flexible
on-demand scheduling and planning can provide a vital competitive advantage by reducing waste,
improving efficiency and productivity, meeting customer due date, and reflecting the dynamic nature of
increasingly competitive markets. Most planning and scheduling problems in manufacturing are NP-
complete, with precedence constraints among tasks, setup costs, timing requirements, and completion
deadlines. The scheduling and shop management are even more complex in flexible manufacturing
systems (FMS) with on-demand production. Classical heuristic methods approach the problem by
applying some priority rules based upon some easily calculated job parameters, such as due date, setup
times, arrival times. Classical methods obviously cannot take into account all the variables interacting
in manufacturing systems, and lack the time-dependent decision capability needed in production plan-
ning and scheduling, especially in FMS and computer-integrated manufacturing (CIM) environments,
which both require an ability to deal with uncertainty and dynamic behavior. The ability of neural
networks to understand temporal patterns is essential for efficient modeling, planning, and scheduling
of manufacturing processes.

planning and scheduling tasks.