Neural Networks in Manufacturing Operations

Neural Networks in Manufacturing Operations

Eldon Gunn (Dalhousie University, Canada) and Corinne MacDonald (Dalhousie University, Canada)
Copyright: © 2006 |Pages: 17
DOI: 10.4018/978-1-59140-670-9.ch010
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Abstract

This chapter provides some examples from the literature of how feed-forward neural networks are used in three different contexts in manufacturing operations. Operational design problems involve the determination of design parameters, such as number of kanbans, in order to optimize the performance of the system. Operational-system decision support refers to the use of neural networks as decision-support mechanisms in predicting system performance in response to certain settings of system parameters and current environmental factors. Operational-system-control problems are distinguished from decision support in that the consequences of a control decision are both an immediate return and putting the system in a new state from which another control decision needs to be taken. In operational control, new ideas are emerging using neural networks in approximate dynamic programming. Manufacturing systems can be very complex. There are many factors that may influence the performance of these systems; yet in many cases, the true relationship between these factors and the system outcomes is not fully understood. Neural networks have been given a great deal of attention in recent years with their ability to learn complex mappings even when presented with a partial, and even noisy, set of data. This has resulted in their being considered as a means to study and perhaps even optimize the performance of manufacturing operations. This chapter provides some examples from the literature of how neural networks are used in three different contexts in manufacturing systems. The categories (1) operational design, (2) operational decision-support systems, and (3) operational control are distinguished by the time context within which the models are used. Some examples make use of simulation models to produce training data, while some use actual production data. In some applications, the network is used to simply predict performance or outcomes, while in others the neural network is used in the determination of optimal parameters or to recommend good settings. Readers who wish to explore further examples of neural networks in manufacturing can examine Udo (1992), Zhang and Huang (1995), and Wang, Tang, and Roze (2001). We begin with two areas in which neural networks have found extensive use in manufacturing. Operational-system design has seen considerable use of neural networks as metamodels that can stand in place of the system, as we attempt to understand its behavior and optimize design parameters. Operational-system decision support refers to the use of neural networks as decision-support mechanisms in predicting system performance in response to certain settings of system parameters. We close with a short introduction to an area where we anticipate seeing growing numbers of applications, namely the use of approximate dynamic programming methods to develop real-time controllers for manufacturing systems.

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Table of Contents
Preface
Joarder Kamruzzaman, Rezaul Begg, Ruhul Sarker
Acknowledgments
Joarder Kamruzzaman, Rezaul Begg, Ruhul Sarker
Chapter 1
Joarder Kamruzzaman, Ruhul A. Sarker
The primary aim of this chapter is to present an overview of the artificial neural network basics and operation, architectures, and the major... Sample PDF
Artificial Neural Networks: Applications in Finance and Manufacturing
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Chapter 2
Ruhul A. Sarker, Hussein A. Abbass
Artificial Neural Networks (ANNs) have become popular among researchers and practitioners for modeling complex real-world problems. One of the... Sample PDF
Simultaneous Evolution of Network Architectures and Connection Weights in Artificial Neural Networks
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Chapter 3
David Encke
Researchers have known for some time that nonlinearity exists in the financial markets and that neural networks can be used to forecast market... Sample PDF
Neural Network-Based Stock Market Return Forecasting Using Data Mining for Variable Reduction
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Chapter 4
Yuehui Chen, Ajith Abraham
The use of intelligent systems for stock market prediction has been widely established. In this paper, we investigate how the seemingly chaotic... Sample PDF
Hybrid-Learning Methods for Stock Index Modeling
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Chapter 5
John Fulcher, Ming Zhang, Shuxiang Xu
Financial time-series data is characterized by nonlinearities, discontinuities, and high-frequency multipolynomial components. Not surprisingly... Sample PDF
Application of Higher-Order Neural Networks to Financial Time-Series Prediction
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Chapter 6
Masoud Mohammadian, Mark Kingham
In this chapter, an intelligent hierarchical neural network system for prediction and modelling of interest rates in Australia is developed. A... Sample PDF
Hierarchical Neural Networks for Modelling Adaptive Financial Systems
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Chapter 7
Sumit Kumar Bose, Janardhanan Sethuraman, Sadhalaxmi Raipet
The term structure of interest rates holds a place of prominence in the financial and economic world. Though there is a vast array of literature on... Sample PDF
Forecasting the Term Structure of Interest Rates Using Neural Networks
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Chapter 8
Joarder Kamruzzaman, Ruhul A. Sarker, Rezaul K. Begg
In today’s global market economy, currency exchange rates play a vital role in national economy of the trading nations. In this chapter, we present... Sample PDF
Modeling and Prediction of Foreign Currency Exchange Markets
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Chapter 9
Tong-Seng Quah
Artificial neural networks’ (ANNs’) generalization powers have in recent years received admiration of finance researchers and practitioners. Their... Sample PDF
Improving Returns on Stock Investment through Neural Network Selection
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Chapter 10
Eldon Gunn, Corinne MacDonald
This chapter provides some examples from the literature of how feed-forward neural networks are used in three different contexts in manufacturing... Sample PDF
Neural Networks in Manufacturing Operations
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Chapter 11
M. Imad Khan, Saeid Nahavandi, Yakov Frayman
This chapter presents the application of a neural network to the industrial process modeling of high-pressure die casting (HPDC). The large number... Sample PDF
High-Pressure Die-Casting Process Modelling Using Neural Networks
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Chapter 12
Sergio Cavalieri, Paolo Maccarrone, Roberto Pinto
The estimation of the production cost per unit of a product during its design phase can be extremely difficult, especially if information about... Sample PDF
Neural Network Models for the Estimation of Product Costs: An Application in the Automotive Industry
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Chapter 13
Tapabrata Ray
Surrogate-assisted optimization frameworks are of great use in solving practical computationally expensive process-design-optimization problems. In... Sample PDF
A Neural-Network-Assisted Optimization Framework and Its Use for Optimum-Parameter Identification
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Chapter 14
George A. Rovithakis, Stelios E. Perrakis, Manolis A. Christodoulou
In this chapter, a neuroadaptive scheduling methodology, approaching machine scheduling as a control-regulation problem, is presented and evaluated... Sample PDF
Artificial Neural Networks in Manufacturing: Scheduling
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Chapter 15
Bernard F. Rolfe, Yakov Frayman, Georgina L. Kelly, Saeid Nahavandi
This chapter describes the application of neural networks to recognition of lubrication defects typical to industrial cold forging process. The... Sample PDF
Recognition of Lubrication Defects in Cold Forging Process with a Neural Network
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