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Modelling the impact of reduced variability of machine tool downtime

By Nicholas Farquharson Pearce


A model is presented, which allows a machine tool in a service arrangement to be simulated. The impact of machine tool downtime and the sensitivity of machine performance to the services provided may be modelled and optimised. SHOAM, developed by the Boeing Company, is used to make the model and modified to make it applicable for use with a machine tool. SHOAM is a tool which uses discrete event simulation to analyse the performance of an aircraft in a realistic operational setting, allowing the benefits of using health management technologies, which use vehicle condition based information to predict failures and plan the support services, to be assessed. Machine tool industry members are interviewed to gather information on typical operational scenarios involving advanced machine tools and the approaches to the services provided to maintain them. These reveal the highly user-driven nature of the machine tool service industry, with users specifying the services they desire, the costs of which are then added to machine sales for a premium. Machine maintenance is often neglected by users who value short-term productivity gains over longer-term performance and manufacturers are therefore unable to guarantee performance based contracts. This neglect and the variability in machine tool environments are found to impede the development of machine reliability. The information from the interviews, in concert with a literature review of equipment service management is used to develop the behavioural requirements. A new model is then created using elements from SHOAM. Fundamental changes are made to the way SHOAM models operational scenarios and the response to condition-based information to represent the current behaviour in the machine tool industry. Typical machine tool services are included, which allows the costs and benefits of using health management technologies to be compareto existing machine services. These technologies are found to increase machine tool operational reliability and make a significant contribution to reducing lifecycle costs

Publisher: Cranfield University
Year: 2008
OAI identifier:
Provided by: Cranfield CERES

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