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The application of neural network and statistical modelling to the scale-up and optimisation of a new adhesive tape manufacturing process. / J. D Grant-Abban

Swansea University Author: J. D Grant-Abban

Abstract

This thesis is concerned with the successful application of statistical and neural modelling techniques to the efficient scale-up and optimisation of a new pressure- sensitive tape manufacturing facility. This thesis describes the generation of modelling data, the use of back propagation neural netw...

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Published: 2002
Institution: Swansea University
Degree level: Master of Philosophy
Degree name: M.Phil
URI: https://cronfa.swan.ac.uk/Record/cronfa42539
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first_indexed 2018-08-02T18:54:57Z
last_indexed 2019-10-21T16:48:00Z
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spelling 2018-08-16T14:39:02.9105634 v2 42539 2018-08-02 The application of neural network and statistical modelling to the scale-up and optimisation of a new adhesive tape manufacturing process. 979d71ec4d46120d80715a49043d5177 NULL J. D Grant-Abban J. D Grant-Abban true true 2018-08-02 This thesis is concerned with the successful application of statistical and neural modelling techniques to the efficient scale-up and optimisation of a new pressure- sensitive tape manufacturing facility. This thesis describes the generation of modelling data, the use of back propagation neural networks to model properties, and the optimisation of the process using the neural models. Modelling data was purposely generated in a series of trials, using the structure of Design of Experiments to cover the process envelope systematically and efficiently. A neural model, using data from pilot-scale process experiments, was used to influence the design of the full-scale processes. Once the full-scale processes were established, a more detailed 34 factor neural model was developed. This second neural model was validated and used to optimise and explore the full-scale process. The validation exercise detected anomalous data using statistical analysis. The anomalous data was subsequently quarantined and prevented from confusing the optimisation effort. The detailed understanding of the process came from running DoE's on the virtual process, represented by the second neural model. These DoE's provided useful contour plots and response surface visualisations of the 'black box' neural network model, giving insights that guided optimisation and indicated the potential for tape with significantly enhanced product properties. E-Thesis Mechanical engineering. 31 12 2002 2002-12-31 COLLEGE NANME Engineering COLLEGE CODE Swansea University Master of Philosophy M.Phil 2018-08-16T14:39:02.9105634 2018-08-02T16:24:29.6029952 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised J. D Grant-Abban NULL 1 0042539-02082018162502.pdf 10805288.pdf 2018-08-02T16:25:02.4730000 Output 13300606 application/pdf E-Thesis true 2018-08-02T16:25:02.4730000 false
title The application of neural network and statistical modelling to the scale-up and optimisation of a new adhesive tape manufacturing process.
spellingShingle The application of neural network and statistical modelling to the scale-up and optimisation of a new adhesive tape manufacturing process.
J. D Grant-Abban
title_short The application of neural network and statistical modelling to the scale-up and optimisation of a new adhesive tape manufacturing process.
title_full The application of neural network and statistical modelling to the scale-up and optimisation of a new adhesive tape manufacturing process.
title_fullStr The application of neural network and statistical modelling to the scale-up and optimisation of a new adhesive tape manufacturing process.
title_full_unstemmed The application of neural network and statistical modelling to the scale-up and optimisation of a new adhesive tape manufacturing process.
title_sort The application of neural network and statistical modelling to the scale-up and optimisation of a new adhesive tape manufacturing process.
author_id_str_mv 979d71ec4d46120d80715a49043d5177
author_id_fullname_str_mv 979d71ec4d46120d80715a49043d5177_***_J. D Grant-Abban
author J. D Grant-Abban
author2 J. D Grant-Abban
format E-Thesis
publishDate 2002
institution Swansea University
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
document_store_str 1
active_str 0
description This thesis is concerned with the successful application of statistical and neural modelling techniques to the efficient scale-up and optimisation of a new pressure- sensitive tape manufacturing facility. This thesis describes the generation of modelling data, the use of back propagation neural networks to model properties, and the optimisation of the process using the neural models. Modelling data was purposely generated in a series of trials, using the structure of Design of Experiments to cover the process envelope systematically and efficiently. A neural model, using data from pilot-scale process experiments, was used to influence the design of the full-scale processes. Once the full-scale processes were established, a more detailed 34 factor neural model was developed. This second neural model was validated and used to optimise and explore the full-scale process. The validation exercise detected anomalous data using statistical analysis. The anomalous data was subsequently quarantined and prevented from confusing the optimisation effort. The detailed understanding of the process came from running DoE's on the virtual process, represented by the second neural model. These DoE's provided useful contour plots and response surface visualisations of the 'black box' neural network model, giving insights that guided optimisation and indicated the potential for tape with significantly enhanced product properties.
published_date 2002-12-31T03:53:10Z
_version_ 1763752636689940480
score 11.013776

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