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On-line surface roughness estimation in cylindrical turning using neural networks


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Backhouse, BA 2000 , 'On-line surface roughness estimation in cylindrical turning using neural networks', Research Master thesis, University of Tasmania.

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In recent years a direct method of surface finish quality detection by electrical
resistance, optical, image processing and dial indicator methods have been proved to
be quantitatively unreliable. The cutting tool wear undergoes a gradual increase and
the failure at the end of useful life is decided on the extent of the wear growth. This
irregular tool wear trend proportionally causes the quality of the surface finish to be
unpredictable. Therefore there is a great need for a reliable quantitative method to
establish the quality of a work materials surface finish, online. From an industry point of view, it is often necessary to identify the production
quality of work materials to avoid expensive losses on machines. A worn or wearing
cutting tool will cause a gradual decline in the surface quality of a component,
causing unfavorable circumstances. For example, machining of alloy wheels in
Ford, Chrysler and GMH have a specific surface roughness to achieve for effective
coating of the wheels. The quality of the wheel coatings and their 'scratch resistance'
depend on the surface profile produced during the machining operation. While many
industries adopt a 'direct modeling' approach, where the machining process variables
are given as inputs to the process to estimate the surface roughness, there is no
evidence available of the 'inverse modeling' where the operating conditions are
estimated based on the target values of the surface roughness. Nevertheless, using the
direct modeling the surface roughness can only be measured 'off line' as a quality
control exercise to meet the specifications of the wheel. There is a general
disagreement with the manufacturer specifications of the surface roughness, on line,
with the constant changes in the cutting tool conditions and wear. Hence there is a
need for 'on line' determination of the surface roughness while carrying out the
machining operation. This will give an indication on the extent of tool wear growth.
Further, this study will also give quantitative values of surface roughness 'on line'
and its application to the manufacturing process.
In this study a neural network model is proposed for surface roughness detection 'online'
in cylindrical turning operations. Neural network architectures will be used as
'direct' modeling techniques to estimate the surface roughness. As 'inverse' modeling tools the operating conditions will also be predicted using the surf ace
roughness and cutting tool wear as inputs.
Extensive turning experiments on a lathe will be carried out covering a
comprehensive range of cutting conditions to generate the knowledge base for the
training stage of the neural network algorithms. The process parameters measured
during the experimentation for identification of surface finish quality includes the
forces, cutting tool vibrations and surface finish during cylindrical turning operation.
These problem addresses one of the most pressing needs of modem automobile
manufacturing industry where an 'on line' estimation of surface roughness is seen as
an important parameter.
This project while improving the understanding of the machining parameters and
their influence on surface finish will also identify necessary neural network tools for
application. This work is seen as a step towards establishing intelligent tools for
machining performance estimation, while addressing the mathematical and scientific
basis of machining science.

Item Type: Thesis - Research Master
Authors/Creators:Backhouse, BA
Keywords: Surfaces (Technology), Neural networks (Computer science), Machining
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:

Includes CD-ROM in back pocket. Thesis (M.Eng.Sc.)--University of Tasmania, 2001. Includes bibliographical references

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