Abstract
Designing a complex mechatronic product involves multiple design variables, objectives, constraints, and evaluation criteria as well as their nonlinearly coupled relationships. The design space can be very big consisting of many functional design parameters, structural design parameters, and behavioral design (or running performances) parameters. Given a big design space and inexplicit relations among them, how to design a product optimally in an optimization design process is a challenging research problem. In this paper, we propose a systematic optimization design method based on design space reduction and surrogate modelling techniques. This method firstly identifies key design parameters from a very big design space to reduce the design space, secondly uses the identified key design parameters to establish a system surrogate model based on data-driven modelling principles for optimization design, and thirdly utilizes the multiobjective optimization techniques to achieve an optimal design of a product in the reduced design space. This method has been tested with a high-speed train design. With comparison to others, the research results show that this method is practical and useful for optimally designing complex mechatronic products.
Highlights
It is very difficult to optimally design a complex mechatronic product for many reasons
It can effectively couple design space reduction and the system surrogate modelling techniques into the system optimization modelling. These two techniques are discussed separately; when applying the surrogate modelling technique to develop a surrogate model for describing complex system relationships, there is a general difficulty in determining what parameters in both inputs and outputs should be chosen to establish an effective surrogate model for use in the optimization. It demonstrates that the identified key variables from the space reduction are well qualified as the input variables for establishing the corresponding system surrogate model and the optimization model, providing a general form of the system optimization modelling and solving techniques for complex mechatronic product optimal design
It can be concluded that the system optimization method based on the system surrogate model with only key design parameters is more effective in terms of optimization accuracy and computational cost
Summary
It is very difficult to optimally design a complex mechatronic product for many reasons. The challenges are threefold: (1) the number of design variables or design space is very big; (2) these design parameters have complex system coupling relationships, and it is difficult to take all design parameters in optimization, needing a design space reduction; and (3) a practical model for predicting the coupled vast system performances involves several subsystem performances. In the current design practice, designing a typical complex mechatronic product such as a high-speed train is mainly by the trial and error method. It lacks a systematic design optimization method for its design.
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