Manufacturing resources degrade continuously throughout their life. This may be due to external factors such as wear and vibration, or operator induced factors such as the selection of incorrect cutting parameters. Planning anufacturing processes based on nominal machine data, can as a consequence result in the generation of inefficient and infeasible manufacturing instructions and out-of-tolerance parts. A method to accurately represent the actual capability of resources is needed to effectively model the manufacturing resources to enable more accurate process planning. In this research a new framework for macro and micro process planning of CNC machining processes is proposed. The framework is based on a computer aided process planning system based on actual machine capability entitled CAPPable. Machining errors affecting the overall health of machines have been reviewed and identified. STEP-NC has been used to model the machining resources and their associated errors. A manufacturing capability profile has been designed in which it is possible to store the values which reflect the degradation of machining resources. CAPPable has been implemented as a STEP-Compliant prototype CAPP system for machining and validated on micro and macro levels. It has been demonstrated that using this framework, the current capability of resources can be accurately represented and can improve process planning effectiveness compared to using nominal manufacturing resource information. Through implementation of this framework, the capability of manufacturing resources can utilise resources to a far greater extent than currently possible. CAPPable has also been extended for use to generate improved part setup location routines for CNC machining.
|Date of Award||13 Feb 2019|
|Supervisor||Vimal Dhokia (Supervisor) & Stephen Newman (Supervisor)|