AbstractThe increasing demand for energy and natural resources, along with global competitiveness and a shift towards mass customisation, represent important factors that have driven the fourth industrial revolution (Industry 4.0). Computer Numerical Control (CNC) machining processes represent one of the most deployed manufacturing processes for parts production worldwide and are well known for its resources and energy-intensive activities. Recently, companies are urgently seeking intelligent approaches to enhance the efficiency and sustainability of machining, to meet the global needs of more sustainability, enhanced competitiveness and, this way, cope with environmental, economic and political factors.
In response to this scenario, this thesis presents two novel approaches of process planning optimisation and real-time supervisory control towards more intelligent, sustainable and efficient manufacturing. The process planning optimisation approach can achieve efficient and sustainable machining by addressing challenging trade-offs of the impacts of machining process parameters and several operational efficiency performance indicators, i.e., energy efficiency, productivity and cutting tool life. To support the trade-offs, an empirical analysis of the cutting tool wear phenomena and cutting tool life, and the influence of machining process parameters on several tool effectiveness indicators (i.e., total cutting time, total cutting length and total volume of material removed) has been carried out. This analysis further supported the investigation of predicting the cutting tool life using power consumption models. Such investigation supported the optimum selection of machining process parameters for the roughing stage. The real-time supervisory control can tackle quality assurance in CNC machining. This system provides in-process support to manual operations of engineers to ensure that the machined parts will meet the challenging precise requirements of surface quality.
To conclude, this thesis contributes towards the development and implementation of more intelligent approaches focusing on both pre and in-process applications to improve the efficiency and sustainability of manufacturing processes. The results from the validation showed that the proposed optimisation approaches effectively supported improved decision-making on input parameters’ selection to achieve highly-efficient processes and meet the manufacturing requirements.
|Date of Award||Jan 2020|
|Supervisor||Weidong Li (Supervisor) & Michael Fitzpatrick (Supervisor)|