Creating flexible and automated production facilities is a complex process that requires high levels of cooperation involving all mechatronics disciplines, where software tools being utilised have to work as closely as their users. Some of these tools are well-integrated but others can hardly exchange any data. This research aims to integrate the software systems applied by the mechatronic engineering disciplines to enable an enhanced design process characterised by a more parallel and iterative work flow. This thesis approaches systems integration from a data modelling point of view because it sees information transfer between heterogeneous data models as a key element of systems integration. A new approach has been developed which is called middle-in data modelling strategy since it is a combination of currently applied top-down and bottom-up approaches. It includes the separation of data into core design data which is modelled top-down and detailed design data modules which are modelled bottom-up. The effectiveness of the integration approach has been demonstrated in a case study undertaken for the mechatronic engineering design process of body shop production lines in the automotive industry. However, the application of the middle-in data modelling strategy is not limited to this use case: it can be used to enhance a variety of system integration tasks. The middle-in data modelling strategy is tested and evaluated in comparison with present top-down and bottom-up data modelling strategies on the basis of three test cases. These test cases simulated how the systems integration solutions based on the different data modelling strategies react to certain disturbances in the data exchange process as they would likely occur during industrial engineering design work. The result is that the top-down data modelling strategy is best in maintaining data integrity and consistency while the bottom-up strategy is most flexibly adaptable to further developments of systems integration solutions. The middle-in strategy combines the advantages of top-down and bottom-up approaches while their weaknesses and disadvantages are kept at a minimum. Hence, it enables the maintenance of data modelling consistency while being responsive to multidisciplinary requirements and adaptive during its step-by-step introduction into an industrial engineering process.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:630020 |
| Date | January 2014 |
| Creators | Proesser, Malte |
| Publisher | De Montfort University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/2086/10492 |
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