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Title: Optimal use of computing equipment in an automated industrial inspection context
Author: Jubb, Matthew James
ISNI:       0000 0001 3593 4127
Awarding Body: Durham University
Current Institution: Durham University
Date of Award: 1995
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This thesis deals with automatic defect detection. The objective was to develop the techniques required by a small manufacturing business to make cost-efficient use of inspection technology. In our work on inspection techniques we discuss image acquisition and the choice between custom and general-purpose processing hardware. We examine the classes of general-purpose computer available and study popular operating systems in detail. We highlight the advantages of a hybrid system interconnected via a local area network and develop a sophisticated suite of image-processing software based on it. We quantitatively study the performance of elements of the TCP/IP networking protocol suite and comment on appropriate protocol selection for parallel distributed applications. We implement our own distributed application based on these findings. In our work on inspection algorithms we investigate the potential uses of iterated function series and Fourier transform operators when preprocessing images of defects in aluminium plate acquired using a linescan camera. We employ a multi-layer perceptron neural network trained by backpropagation as a classifier. We examine the effect on the training process of the number of nodes in the hidden layer and the ability of the network to identify faults in images of aluminium plate. We investigate techniques for introducing positional independence into the network's behaviour. We analyse the pattern of weights induced in the network after training in order to gain insight into the logic of its internal representation. We conclude that the backpropagation training process is sufficiently computationally intensive so as to present a real barrier to further development in practical neural network techniques and seek ways to achieve a speed-up. Weconsider the training process as a search problem and arrive at a process involving multiple, parallel search "vectors" and aspects of genetic algorithms. We implement the system as the mentioned distributed application and comment on its performance.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Defect detection; Neural networks