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

Abstract

In recent years, artificial neural networks have been applied to solve a variety of problems in numerous areas of manufacturing at both system and process levels. The manufacturing applications of neural networks comprise the design of manufacturing systems (including part-family and machine-cell for- mation for cellular manufacturing systems); modeling, planning, and scheduling of manufacturing processes; monitoring and control of manufacturing processes; quality control, quality assurance, and fault diagnosis. This paper presents a survey of existing neural network applications to intelligent man- ufacturing. Covering the whole spectrum of neural network applications to manufacturing, this chapter provides a comprehensive review of the state of the art in recent literature.

2.1 Introduction

Neural networks are composed of many massively connected simple neurons. Resembling more or less their biological counterparts in structure, artificial neural networks are representational and computational models processing information in a parallel distributed fashion. Feedforward neural networks and recur- rent neural networks are two major classes of artificial neural networks. Feedforward neural networks,

Jun Wang

The Chinese University of Hong Kong

Wai Sum Tang

The Chinese University of Hong Kong

Catherine Roze

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 research in this direction. Neural networks have been applied to a variety of areas of manufacturing from the design of manufacturing systems to the control of manufacturing processes. One top-down classification of neural network applications to intelligent manufacturing, as shown in Figure 2.1, results in four main categories without clearly cut boundaries: (1) modeling and design of manufacturing systems, including machine-cell and part-family formation for cellular manufacturing systems; (2) modeling, planning, and scheduling of manufacturing processes; (3) monitoring and control of manufacturing processes; (4) quality control, quality assurance, and fault diagnosis. The applications of neural networks to manufacturing have shown promising results and will possibly have a major impact on manufacturing in the future [1, 2].

FIGURE 2.1 Hierarchy of neural network applications in intelligent manufacturing.

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This chapter provides a comprehensive survey of recent neural network applications in intelligent manufacturing based on the aforementioned categorization. The aim of the chapter is to review the state 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.