McLeay, T E (2016) Unsupervised Monitoring of Machining Processes. PhD thesis, University of Sheffield.
Abstract
Machining processes, such as milling, drilling, turning and grinding, concern the removal of material from a workpiece using a cutting tool. These processes are sensitive to parameters such as cutting tool properties, workpiece materials, coolant application, machine selection, fixturing and cutting parameters. The focus of the work in this thesis is to devise a method to monitor the changing conditions of a machining process over time in order to detect faulty machining conditions and diagnose fault types and causes. A key aim of this thesis is to develop a monitoring regime that has minimal cost of implementation and upkeep in a production environment, therefore an unsupervised monitoring system which applies non-intrusive sensing hardware is proposed.
Metadata
Supervisors: | Sharman, A and Turner, M S |
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Keywords: | machining, process monitoring, machine learning, sensing, milling, advanced manufacturing, fault detection |
Awarding institution: | University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Advanced Manufacuring Research Centre (Sheffield) |
Identification Number/EthosID: | uk.bl.ethos.706041 |
Depositing User: | Dr T E McLeay |
Date Deposited: | 20 Mar 2017 09:11 |
Last Modified: | 12 Oct 2018 09:35 |
Open Archives Initiative ID (OAI ID): | oai:etheses.whiterose.ac.uk:16556 |
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