A Systematic Optimization Design Method for Complex Mechatronic Products Design and Development

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.

A New Reliable Operating Region Design Method

“…It is considered reliable, provided the following conditions hold: y 1 ≥ 1 and y 2 ≥ 1. at is, y l � (1, 1). Moreover, based on the safety margin, engineers set the target values of these two performances as y 0 � (y 01 , y 02 ) � (3,3). e second row of Table 4 shows the design space in terms of the bounds of each design parameter.…”

“…Design, manufacture, use, maintenance, and various other factors determine the reliability of products [2,3]. In the manufacturing industry, however, the focus is upon the design and manufacture of products.…”

Development of the universal model of mechatronic system with a hydraulic drive

“…It is necessary to optimize the mechatronic system in general. Papers [6,7] have considered the methods for optimal design of complex mechatronic systems. However, nothing was said about construction of physical and mathematical models of the processes occurring in the mechatronic systems.…”

Participative Method to Identify Data-Driven Design Use Cases

“…The data-driven design use case given in [18] shows a study that uses parameterized numerical simulation models and surrogate models to enable optimization based on a genetic algorithm. Since this approach not suggest a variable selection approach, the selection use case study of [17] is applicable. Here, all available high speed train design variables are analyzed each with respect to the relative importance based on previous engineering designers knowledge.…”