Jiachuan Wang

Knowledge interaction with genetic programming in mechatronic systems design using bond graphs

This paper describes a unified network synthesis approach for the conceptual stage of mechatronic systems design using bond graphs. It facilitates knowledge interaction with evolutionary computation significantly by encoding the structure of a bond graph in a genetic programming tree representation. On the one hand, since bond graphs provide a succinct set of basic design primitives for mechatronic systems modeling, it is possible to extract useful modular design knowledge discovered during the evolutionary process for design creativity and reusability. On the other hand, design knowledge gained from experience can be incorporated into the evolutionary process to improve the topologically open-ended search capability of genetic programming for enhanced search efficiency and design feasibility. This integrated knowledge-based design approach is demonstrated in a quarter-car suspension control system synthesis and a MEMS bandpass filter design application.

Interactive evolutionary solution synthesis in fuzzy set-based preliminary engineering design

This paper describes an interactive evolutionary approach to synthesize component-based preliminary engineering design problems. This approach is intended to address preliminary engineering design as an evolutionary synthesis process, with the needs for human-computer interaction in a changing environment caused by uncertainty and imprecision inherent in the early design stages. It combines an agent-based hierarchical design representation, set-based design generation, fuzzy design trade-off strategy and interactive design adaptation into evolutionary synthesis to gradually refine and reduce the search space while maintaining solution diversity to accommodate future changes. The fitness function of solutions employed is not fixed but adapted according to elicited human value judgment and constraint change. It incorporates multi-criteria evaluation as well as constraint satisfaction. This new approach takes advantage of the different roles of computers and humans play in design and optimization. The methodology will be applicable to general multi-domain applications, with emphasis on physical modeling of dynamic systems. An automotive speedometer design case study is included to demonstrate the methodology.

An evolutionary approach for robust layout synthesis of MEMS

The paper introduces a robust design method for layout synthesis of MEM resonators subject to inherent geometric uncertainties such as the fabrication error on the sidewall of the structure. The robust design problem is formulated as a multi-objective constrained optimisation problem after certain assumptions and treated with multiobjective genetic algorithm (MOGA), a special type of evolutionary computing approaches. Case study based on layout synthesis of a comb-driven MEM resonator shows that the approach proposed in this paper can lead to design results that meet the target performance and are less sensitive to geometric uncertainties than typical designs

Exploring Open-Ended Design Space of Mechatronic Systems

To realize design automation of mechatronic systems, there are two major issues to be dealt with: open-topology generation of mechatronic systems and simulation or analysis of those models. For the first issue, we exploit the strong topology exploration capability of genetic programming to create and evolve structures representing mechatronic systems. With the use of ERCs (ephemeral random constants) in genetic programming, we can evolve the sizing of mechatronic system components together with the system structures simultaneously. The second issue, simulation and analysis of those system models, is made more complex when the systems are mixed-energy-domain systems. We take advantage of bond graphs as a tool for multi- or mixed-domain modeling and simulation of mechatronic systems. Because there are many considerations in mechatronic system design that are not completely captured by a bond graph, it is beneficial to generate multiple solutions, allowing the designer more latitude in choosing a model to implement. The approach in this paper is capable of providing a variety of design choices to the designer for further analysis, comparison and trade-off study. The approach is shown to be efficient and effective and is demonstrated in an example of open-ended real-world mechatronic system design application, a typewriter re-design problem.

Cooperative body–brain coevolutionary synthesis of mechatronic systems

Abstract To support the concurrent design processes of mechatronic subsystems, unified mechatronics modeling and cooperative body–brain coevolutionary synthesis are developed. In this paper, both body-passive physical systems and brain-active control systems can be represented using the bond graph paradigm. Bond graphs are combined with genetic programming to evolve low-level building blocks into systems with high-level functionalities including both topological configurations and parameter settings. Design spaces of coadapted mechatronic subsystems are automatically explored in parallel for overall design optimality. A quarter-car suspension system case study is provided. Compared with conventional design methods, semiactive suspension designs with more creativity and flexibility are achieved through this approach.

Interactive Preference Incorporation in Evolutionary Engineering Design

This chapter presents an approach that takes advantage of the different roles that computers and humans play in an interactive engineering design environment. It draws on the positive features of learning-oriented methods and searching-oriented methods, thus adapting design trade-off strategy when more precise preference information is learned during the evolutionary search process. The rationale and advantages of evaluating design fitness based on a fuzzy-set based preference aggregation are provided, which not only relies on specifying parameters about importance weights of different design attributes, but also the degree of compensation among them. The designers’ preferences are elicited, and the parameter learning of the preference aggregation function is implemented in an artificial neural network. Guided by online adaptive fitness evaluation, the current favorable solution set is generated by means of evolutionary computation through a component-based design synthesis approach. An example problem of panel meter design configuration is provided to demonstrate the approach.

Hierarchical evolutionary synthesis of MEMS

We discuss the hierarchy that is involved in a typical MEMS design and how evolutionary approaches can be used to automate the hierarchical design and synthesis process for MEMS. At the system level, the approach combining bond graphs and genetic programming can lead to satisfactory design candidates of system level models that meet the predefined behavioral specifications for designers to tradeoff. At the physical layout synthesis level, the selection of geometric parameters for component devices is formulated as a constrained optimization problem and addressed using a constrained GA approach. A multiple-resonator microsystem design is used to illustrate the integrated design automation idea using evolutionary approaches.

A Group Technology Based Representation for Product Portfolios

Repository based applications for portfolio design offer the potential for leveraging archived design data with computational searches. Toward the development of such search tools, we present a representation for product portfolios that is an extension of an existing Group Technology (GT) coding scheme. Relevance to portfolio design is treated with a case study example of a hand held grinder design. Results of this work provide a numerical coding representation that captures function, form, material and manufacturing data for systems. This extends the current GT line work by combining these four types of design data and clarifying the use of the functional basis in a GT code. The results serve as a useful starting point for the development of portfolio design algorithms, such as genetic algorithms, that account for this combination of design information.Copyright

Learning and Adapting Fuzzy Set-Based Trade-off Strategy in Engineering Design Synthesis

Multi-criteria decision methods are common in engineering design solution synthesis to accomplish trade-offs among competing objectives. Since design is an evolving interactive process with less precise information available in earlier stages than in later stages, the trade-off strategy could also change as design stages progress and more information is added. This paper provides the rationale and advantages of choosing design trade-off strategies based on fuzzy set-based preference aggregation, which not only relies on specifying parameters about importance weights of design attributes, but also the degree of compensation among them. A neural network function approximation method and procedure, devised to learn and adapt the trade-off strategies according to the current preference information available from the environmental evaluation feedback, is then provided. As the design process evolves, this adaptation should lead to more suitable and stabilized trade-off strategies. A numerical example of experimentation is included to demonstrate the approach.

GPBG: A Framework for Evolutionary Design of Multi-domain Engineering Systems Using Genetic Programming and Bond Graphs

Current engineering design is a multi-step process proceeding from conceptual design to detailed design and to evaluation and testing. It is estimated that 60–70% of design decisions and most innovation occur in the conceptual design stage, which may include conceptual design of function, operating principles, lavout, shape, and structure. However, few computational tools are available to help designers to explore the design space and stimulate the product innovation process. As a result, product innovation is strongly constrained by the designer’s ingenuity and experience, and a systmatic approach to product innovation is strongly needed.