Michael Friedl, Lukas Weingartner, Peter Hehenberger, Rudolf Scheidl,
"Model Dependency Maps for transparent concurrent engineering processes"
: Proceedings of the 14th Mechatronics Forum International Conference, Mechatronics 2014, Seite(n) 614-621, 2014
Original Titel:
Model Dependency Maps for transparent concurrent engineering processes
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the 14th Mechatronics Forum International Conference, Mechatronics 2014
Original Kurzfassung:
Mechatronic product development involves engineers from different disciplines and from various workgroups, who concurrently work on diverse aspects or sub-systems of that product. They use several models which require input and generate output information. Some of these outputs are inputs for other models, in particular also for other workgroups, or are essential inputs for decision making. For large mechatronic systems these different models, decision making steps and the in- and output data establish a complex interdependency network. Quite often this network is not explicitly known, which leads to inefficient and error prone design processes. In fact, no established methods and tools exist, to investigate and describe existing model networks. In this paper the problem of investigating existing model-, data- and decision-making-networks, their representation and visualization are addressed. The proposed representation of the network is called "Model Dependency Map" (MDM). It helps to increase the mutual understanding of discipline-specific engineering processes and their underlying interdependent models. Also a short introduction to the information-acquisition process is given, followed by a method how information can be effectively visualised using various granularities of the MDM. A small set of specified symbols is used to describe all the accumulated information and knowledge to build up the MDM. Basically, there are two essential types of symbols: nodes and edges. Each node represents a model, which contains a multiplicity of different parameters, but also information about the model?s purpose, involved stakeholders, used tools, knowledge and additional information. The dependencies between the models are represented by a set of defined edges, which, for instance, describe how information (e.g. work-results) is transferred between different models.