Designing mechatronic systems is known to be a very complex and tedious process due to the high number of system components, their multi-physical aspects, the couplings between the different domains involved in the product, and the interacting design objectives. This inherent complexity calls for the crucial need of a systematic and multi-objective design thinking methodology to replace the often-used sequential design approach that tends to deal with the different domains and their corresponding design objectives separately leading to functional but not necessarily optimal designs. Thus, a new approach based on a multi-criteria profile for mechatronic systems is presented in this paper for the conceptual design stage. Additionally, to facilitate fitting the intuitive requirements for decision-making in the presence of interacting criteria, three different methods are proposed and compared using a case study of designing a vision-guided quadrotor drone system. These methods benefit from three different aggregation techniques such as Choquet integral, Sugeno integral and fuzzy-based neural network. To validate the decision yielded by the results of global concept score for each aggregation methods, a computer simulation of a visual servoing system on all design alternatives for quadrotor drone has been performed. It is shown that although the Sugeno fuzzy can be a useful aggregation function for decisions under uncertainty, but the approaches using Choquet fuzzy and fuzzy integral-based neural network seem to be more precise and reliable in a multi-criteria design problem where interaction between the objectives cannot be overlooked.
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Multicriteria design of a mechatronic system: a questionnaire Q-ID:MMP016-2014
Consider the case of designing a mechatronic system such as a Quadrotor drone (as shown in Fig. 15).
The designer team has already come up with a set of five criteria as follows:
Increasing machine intelligence:
including two components of control intelligence and interface intelligence.
Increasing system reliability.
Reducing design complexity:
depends on the quantity of components, the degree of architecture complexity, number of feedback loops in design process, the number of distinct knowledge bases, the controller complexity and finally the extent of embedded software in product.
Increasing design flexibility:
depends on the number of alternative component design paths, the number of components customization options and the number of choices for system architecture.
including cost of various parts, components, production, manufacture, etc.
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Mohebbi, A., Achiche, S. & Baron, L. Multi-criteria fuzzy decision support for conceptual evaluation in design of mechatronic systems: a quadrotor design case study. Res Eng Design 29, 329–349 (2018). https://doi.org/10.1007/s00163-018-0287-6
- Decision support
- Mechatronic systems
- Multi-criteria design
- Fuzzy logic
- Quadrotor system