Today, manufacturing system engineering companies face three major trends: First, the systems have to implement more functions and, hence, grow in complexity. Then, besides mechanical engineering discipline the electrical and software engineering disciplines contribute more and more to the functionality of the system and, hence, require increasing efforts. And finally, the integration of the components from the individual engineering disciplines has become a costly task taking up to 25% of the project budget.
When talking to management personnel from manufacturing system engineering companies one can observe several practical challenges, which need to be addressed: For one, todays projects are dominated typically by mechanical design decisions and, thus, synergies between the different engineering disciplines cannot be exploited. Then, the design decisions taken in one discipline are not synchronized properly with the other disciplines leading to inconsistent and incompatible designs. Furthermore, the quality of the overall system design is not evaluated sufficiently and, hence, design flaws remain undetected until commissioning. And finally, the different engineering activities are carried out sequentially in a waterfall fashion potentially leading to costly design iterations.
In contrast, when looking at related work on the design of manufacturing systems, one can observe a number of problems that remain unsolved until today: In the first place, the approaches typically cover only a subset of design information and, therefore, do not represent each engineering discipline appropriately and sufficiently. Secondly, even if the approaches cover a wide range of design information an integrated formal foundation is missing, which defines the syntactic and semantic relations between the design elements precisely and unambiguously. As a consequence, an automated evaluation of the design information cannot be performed or can be performed only over a limited subset of design information. And finally, a practical methodology is missing fostering early verification and validation of the design decisions.
To overcome the current situation, this doctoral thesis provides six contributions: Foremost, the principles and ideas of test-driven and top-down, compositional software development methods are adapted to the cyber-physical manufacturing system domain. Then, an existing modeling technique and underlying formalism is adapted and extended to cover design information about customer requirements, manufacturing processes, and test cases in addition to part geometries, motion profiles, and energy as well as signal flow behaviors. Subsequently, a taxonomy and formal definition of quality issues is developed including syntactic completeness and consistency as well as extrinsic and intrinsic semantic execution constraints. In the following, a prototypical tooling is proposed, which demonstrates how the modeling technique and the quality issues can be implemented in practice. Then, the overall approach is applied to an industry-close showcase, the pick and place unit installed at the Institute for Automation and Information Systems, Technical University of Munich. Finally, based on the experiment and data collected during tool usage the feasibility of the test-driven method for the conceptual design of cyber-physical manufacturing systems is discussed, the validity of the system model and the underlying modeling technique is analyzed, and the relevancy of the syntactic and semantic quality issues in practical applications is shown.
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Today, manufacturing system engineering companies face three major trends: First, the systems have to implement more functions and, hence, grow in complexity. Then, besides mechanical engineering discipline the electrical and software engineering disciplines contribute more and more to the functionality of the system and, hence, require increasing efforts. And finally, the integration of the components from the individual engineering disciplines has become a costly task taking up to 25% of the p...
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