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Performance estimation in drilling using artificial neural networks

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Moore, Timothy John 2000 , 'Performance estimation in drilling using artificial neural networks', Research Master thesis, University of Tasmania.

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Abstract

Drilling is often carried out as one of the last steps in the manufacturing production
of a part and demands process reliability. The work piece would have undergone
extensive machining before and thus the final drilling demands considerable
attention. It is apparent that any optimisation made to the drilling process will make
manufacturing more productive and improve quality. The estimation of the process
outcomes is necessary for reliable optimisation techniques.
Traditional methods of performance estimation develop mathematical models and
relationships, for individual performance estimation, for a set of process parameters.
With advances in on-line control of machine tools there is a need to predict more
than one performance feature. Artificial intelligence offers an alternative method for
performance estimation and one that can perform simultaneous estimation of more
then one performance feature.
This project aims to use artificial intelligence, more specifically artificial neural
networks (ANN), for the simultaneous prediction of the performance measures of
thrust and torque in the conventional drilling process. An initial investigation into
the relationship between hole oversize and the vibrations in two planes will also be
undertaken. This work is seen as not only a step towards establishing intelligent
tools for machining performance estimation, while addressing the mathematical and
scientific basis of machining science, but also as a step towards the use of artificial
intelligence for on-line control of the conventional drilling process.

Item Type: Thesis - Research Master
Authors/Creators:Moore, Timothy John
Keywords: Drilling and boring, Neural networks (Computer science)
Copyright Holders: The Author
Copyright Information:

Copyright 2000 the author - The University is continuing to endeavour to trace the copyright
owner(s) and in the meantime this item has been reproduced here in good faith. We
would be pleased to hear from the copyright owner(s).

Additional Information:

Thesis (M.Eng.Sc.)--University of Tasmania, 2001. Includes bibliographical references

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