The adoption of innovative Near Net Shape (NNS) manufacturing technologies can dramatically reduce costs and lead times in established manufacturing processes. However identifying candidate NNS processes and optimizing their implementation is frequently done in an adhoc manner without the benefits of a structured process of discovery and assessment. Motivated by the need for more robust assessment of potential NNS applications this thesis presents a methodology for selecting NNS manufacturing technologies and optimizing their implementation in established manufacturing processes. The literature review highlights a lack of systematic methodologies that support a holistic approach to assessing the impact of an NNS process (in terms of machining time and raw material consumption) on an established manufacturing chain. The methodology (known as the Near Net Shape Operative(NeNeShO) Protocol) is a three step pipeline that first creates a short-list of candidate processes, before selecting and, lastly, optimising the operational parameters. The first phase (Product Geometry, Manufacturing and Material Matching (ProGeMa3) is a quantitative methodology that selects a set of viable primary shaping process using a unique form of Process Selection Matrix (ProSMa), that associates processes with a range of materials and product geometry they can shape. ProGeMa3 ranks the candidate processes (using fuzzy logic) by their ability to achieve target product requirements (e.g. tolerances, mechanical properties) in relationship to current process capabilities.The second phase (Differential Cost and Feasibility Analysis - DFCA) combines technological feasibility (i.e. analytical, numerical or experimental approaches) and economic feasibility (theoretical, statistical derived, analogous cost models) to establish the ability of an NNS process to deliver the specified product requirements. The process models used in phase two are also applied in the third phase (Conditional Design Optimization- CoDeO) and, depending on the selected route, optimization algorithms (e.g. genetic algorithms) or statistical methods (e.g. Design of Experiment) are used to refine the implementation. Case study applications and existing literature have been used to establish the completeness and effectiveness of the NeNeShO methodology. The resulting NNS selection system is believed to be the only quantitative and systematic procedure that can guide both the selection and optimization of feasible NNS processes in the context of an existing process chain.
|Date of Award||28 May 2019|
- University Of Strathclyde
|Sponsors||EPSRC (Engineering and Physical Sciences Research Council)|
|Supervisor||Jonathan Corney (Supervisor) & Paul Xirouchakis (Supervisor)|