Tool Condition Performance Monitoring, Evaluation and Analysis in a Modern Industrial Precision Manufacturing Environment

Jonathan Downey

    Research output: Types of ThesisDoctoral Thesis


    This thesis presents research and evaluation of the various phenomena that potentially contain worthwhile information in the performance of machining operations. In particular what these phenomena can tell a computer-controlled machine, or the machine operator, about the degree of tool wear being experienced during the operation. Tool wear is an unavoidable element of machining operations and a variety of approaches have been investigated and implemented to delay the onset of wear, e.g. cutting fluid, tool coatings. However, there is currently no reliable system whereby tool wear itself can be monitored during the cutting operation; this information is only available at the end of the operation, through tool or work piece inspection and interrogation. It was the intention of this research to determine which of the machining phenomena, or fusions of these phenomena, is likely to provide the most worthwhile information for such monitoring. All the data gathered during this research and presented in this thesis was from machines working in a commercial environment, carrying out typical live production cutting operations, thus providing data that could be worthwhile for modern industry. While this work started as the author’s own research into process monitoring of the machining process, it expanded into an FP7 funded project with the acronym “REALISM” Realism project (2015), with the author’s organisation Schivo and WIT as the Lead Partners. This pan-European project formally commenced in Jan 2014 and had a 2-year duration, concluding at the end of 2015. REALISM’s aim was to build and test a tool condition monitoring system and, based on that, to lay the groundwork for the future goal of optimizing tool usage and the tool change decision.
    Original languageEnglish
    Awarding Institution
    • O'Leary, Paul, Supervisor
    • Raghavendra, Ramesh, Supervisor
    Publication statusUnpublished - 2018


    • Tool condition performance monitoring


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