Retrieval of old designs that meet current requirements on geometrical and/or technical information is a problem that is often encountered in batch manufacturing systems. This paper models the design retrieval system as a human associative memory. A neural network model, viz., the Hopfield net model, is used to develop a Design Retrieval System. The development of the model and its implementation are discussed using illustrative examples. The system has been verified with test cases on rotational as well as non-rotational parts. © 1992.

T. T. Narendran

Components

Design aids

Drawing (graphics)

Information retrieval systems

Manufacture

Manufacturing data processing

Models

Artificial neural networks (ANN)

Associative memory

BATCH MANUFACTURING SYSTEMS

COMPONENT CLASSIFICATION

COMPONENT DRAWINGS

DESIGN RETRIEVAL SYSTEM

GROUP TECHNOLOGY (GT)

HOPFIELD NET MODEL

HUMAN MEMORY FUNCTION

Neural Networks

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Neural network model for design retrieval in manufacturing systems

Computers in Industry

Identification of machine-cells is one of the most important problems in the design of cellular manufacturing systems (CMSs). It involves decomposing a manufacturing system into machine-cells by grouping machines and parts. Several algorithms with varying degrees of success have been proposed and utilised to solve this problem. Among the modern tools, neural network models have the potential to solve the machine-cell formation problem. Choosing the competitive learning model, adaptive resonance theory (ART) model and self-organizing feature map (SOFM) model from neural network theory for this purpose, we demonstrate their suitability for solving the machine-cell formation problem. Applications on trial problems show the viability for solving the machine-cell formations problem and stand testimony to the practical utility of neural network models in designing CMSs. © 1994 Taylor & Francis Group, LLC.

International Journal of Production Research

Machine-cell formation through neural network models

Introduction to the Theory of Neural Computation

Geological Society, London, Engineering Geology Special Publications

About this title ‐ Hot Deserts: Engineering, Geology and Geomorphology Engineering Group Working Party Report

Journal	Computers in Industry
ISSN	01663615
Open Access	No