Developing Key Technologies towards an Engineer Centered Quantitative Design Methodology

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Engineering design, as an innovative, complex, and highly constrained process, demands practical methodologies and tools. There have been a few decades of research on design automation, yet human beings are far from being able to “automate” engineering design. This work is based on the belief that human beings, i.e., engineers, should remain at the center of the design activities for creativity and innovation. However, instead of relying on personal experience, engineers should be supported with a quantitative design methodology, which provides them with high-level design information to assist in making design decisions. This research aims at developing key technologies towards such a quantitative design methodology. What are the design activities that an engineer needs quantitative support for? An engineer often explores different design scenarios with interchangeable design objectives and constraints. Also, an engineer needs to answer “what-if” questions, find sensitive design parameters, identify the optimal design for different scenarios, and explain the optimal design to the management. Answers to these questions can only be adequately obtained through quantitative analysis, integration, and optimization. To address engineer’s needs, three key technologies are identified and will be developed in this research towards an engineer-centered quantitative design methodology: 1) intelligent optimization management; 2) engineer-centered design interaction; and 3) distributed engineering computing environment. The intelligent optimization management technology is expected to free-up engineers from being experts in optimization. To achieve this, we propose to use a unified optimization formulation, with which engineers can freely designate design objectives and constraints to explore various design scenarios. Secondly, we are to use data mining technologies to gain knowledge about the design problem, which can be utilized to assist the optimization. Thirdly, we aim to develop an optimization strategy whose search schemes and parameters are automatically tuned through a feedback control loop. All of these methods are to be built on the strength of our lab in design optimization, and will enable an automatic optimization process without asking engineers to manually select optimization algorithms or their parameters. For engineer-centered design interaction, we propose to use surrogates for fast visualization, apply data mining techniques for pre- and post-processing, and develop advanced visual techniques to support various design studies. To develop a distributed engineering computing environment, we will tailor a typical distributed computing system to satisfy unique requirements of engineering design. New configuration and load balancing schemes are to be designed. For three decades, the design automation research community has been focusing on pure mathematics based design automation methodologies. The proposed research revolutionizes the conventional design automation research by bringing engineers to the center of the design. The proposed methodology also differs from qualitative design methodologies that lack solid quantitative foundation. This methodology will enable engineers to systematically and efficiently explore and search for the optimal design and leverage strong capabilities of modern engineering analyses. With the proposed quantitative design methodology, engineers will make informed decisions, and better products and novel processes are expected to be generated more efficiently. The impact of the proposed research to the manufacturing sector of Canada is therefore significant and will be shown through close collaborations with industry partners for practical benefits and timely technology transfers.