| Summary: | Manufacturing variation and its propagation is an ever-present challenge for assembly. Nowhere is this a more difficult problem than in the aerospace industry. An industry where components are large and complex and tolerances are tight. This difficulty has halted the aerospace industry's progress towards greater automation, cheaper production and faster production rates. There is a lack of research considering automatic manufacturing variation control methods that are active during the production process: both for part-to-part assemblies and fixtured assemblies.
This research addresses this gap by developing a novel and generalised data model, complete with two adaptive methodologies; one for each of the two types of assembly. The data model, drawing upon object-oriented programming techniques, improves upon the traditional static design approach to manufacturing and replaces it with a dynamic, adaptive and machine-readable model-driven approach. When supplied with metrology data, the data model monitors and automatically corrects the state of the key characteristics of assemblies effected by manufacturing variation. The first methodology leverages the data model to enable adaptive part-to-part assembly in low-tolerance applications. Applications such as those found in the aerospace industry, where part-to-part assembly can vastly reduce tooling and gauge costs and decrease assembly lead times. The second methodology enables the automatic control of multiple key characteristics for metrology-guided robotic assembly in low-tolerance applications. In addition to this, the methodology does not rely on any prior known mathematical or mechanical relationships. This ensures the methodology is robust to: variation in the components, location errors of the fixture and assembly robot, and errors in the pickup location between the robot and the component being assembled. This robustness to location errors is used to enable high-precision mobile assembly systems and reconfigurable/flexible factories. Ultimately, this work is a paradigm shift for the assembly of aerospace components. It provides generalised approaches to either flexibly and robustly automate the existing techniques of aerospace assembly using only off-the-shelf automation, or it circumvents the traditional challenges by enabling adaptive part-to-part assembly.
The thesis achieves this by following a structured research methodology. The research methodology focuses on three research objectives which have been developed from knowledge gaps identified from a review of the current literature. The thesis details each of the contributions in their respective chapters and each objective has been verified and evaluated against aerospace requirements by implementing technical demonstrators. An example of adaptive part-to-part assembly is presented for aerospace applications. It also presents two instances of metrology-guided robotic assembly systems; also for aerospace application. The first is a static robot cell where the fixture is mobile and the second is a mobile assembly robot with a static fixture. All demonstrators focus on rib-to-wing box assembly, since it is the most prevalent of primary structure assembly operations on each aircraft.
This thesis provides an automated and flexible approach to controlling manufacturing variation and its propagation during production. This is shown via capability studies applied to each of the technical demonstrators presented in this thesis. These studies validate that both of the methodologies presented are repeatable and capable of aerospace requirements. They also validate the practicality of the overall adaptive approach enabled by the data model developed in this thesis.
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