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김성신(Seongsin Kim),강남우(Namwoo Kang) 대한기계학회 2021 대한기계학회 춘추학술대회 Vol.2021 No.8
It is well known that braking performances are target performances which must be considered for vehicle development. Apparent piston travel (APT) which is the distance piston travels when the driver pushing on the pedal. In this work, we propose a deep learning-based inverse design model for APT to reduce time and cost of iterative optimization methods. we can get the optimal design instantly, that satisfies APT performance. In particular, in order to have various optimal design values for a single performance target, an inverse designing technique using unsupervised learning such as Conditional GAN(CGAN) and Conditional VAE(CVAE), is presented. This approach will be extended to various vehicle systems in the future.
Reliability-Based Design for Market Systems (RBDMS) : Case Study on Electric Vehicle Design
Ungki Lee(이웅기),Namwoo Kang(강남우),Ikjin Lee(이익진) 대한기계학회 2017 대한기계학회 춘추학술대회 Vol.2017 No.11
When designing a product, both engineering uncertainty and market heterogeneity should be considered to reduce the risk of failure in the market. Reliability-based design optimization (RBDO) approach allows decision makers to achieve target confidence in product performance under engineering uncertainty. On the other hand, design for market systems (DMS) approach helps decision makers to find profit-maximized product design under market heterogeneity. This paper proposes a reliability-based design for market systems (RBDMS) framework for electric vehicle (EV) design by integrating RBDO and DMS approaches. In RBDO, the product quality is controlled based on arbitrarily defined reliability. However, from the viewpoint of the company, it is necessary to set the appropriate reliability since the increased reliability increases both consumer satisfaction and manufacturing cost. Therefore, by modeling how reliability affects the customer choice, the optimum reliability which maximizes the profit can be derived. The “reliability” in the framework is used as follows: (1) a decision variable; (2) an attribute that directly influences the customer preference; (3) standards to determine advertised performance; and (4) reliability constraints in engineering model. We optimized and compared four scenarios depending on whether engineering systems are deterministic or probabilistic, and whether a market is homogeneous or heterogeneous. The results show that designing an entry EV model with low reliability is recommended under engineering uncertainty and market heterogeneity, while designing a premium EV model with high reliability is recommended to ensure the robustness of the profit.