Automatic Enumeration of Feasible Kinematic Diagrams for Split Hybrid Configurations With a Single Planetary Gear
Power-split hybrid electric vehicles embody two electric machines in addition to the internal combustion engine, and it employs one or more planetary gear sets (PG) while disposing of the transmission. Most of the prior studies on the design of power-split hybrids focused on finding optimal powertrain configurations, which are configurations specifying the components connections. However, a selected powertrain configuration cannot be physically realized as it does not specify the components arrangements in three dimensional space. Therefore, a given powertrain configuration should be depicted into feasible kinematic diagrams, which are used to generate the three dimensional drawings used for manufacturing. Multiple kinematic diagrams can be depicted for a given powertrain configuration as each kinematic diagrams specifies the exact components arrangements in addition to their connections. In this work, an automatic approach is developed to generate all the feasible kinematic diagrams for any given power-split powertrain configuration with a single PG. First, all the possible components arrangements, i.e. positioning diagrams, are generated. Then, a set of developed feasibility rules are applied on each positioning diagram in order to filter out infeasible components arrangements. Lastly, feasible kinematic diagrams are depicted for each feasible positioning diagram, and a set of preferred design criteria are used to select arrangements that best suit the vehicle’s manufacturability, packaging, maintenance, and cost. The proposed methodology guarantees automatically finding the components arrangements that best suit the desired vehicle through the search of the entire design space.
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Dipanjan Ghosh, Andrew Olewnik, Kemper Lewis, Junghan Kim and Arun Lakshmanan
J. Mech. Des 139(9), 091401 (Jul 12, 2017); doi: 10.1115/1.4036780
Understanding consumer perceptions of products and the potential impact of those perceptions on purchase decisions is critical information that should influence product development decisions. Though firms often seek consumer feedback on products, such feedback often occurs long after product use and lacks specific details about the interaction, usage context, etc. This work introduces a novel framework – Cyber-Empathic Design – that integrates sensor data and real-time user feedback to develop a more accurate model of user perceptions. The framework is applied to a case study focused on user perceptions of shoes. The results of this work demonstrate the potential for product developers to leverage the IoT (internet-of-things) movement, real-time user feedback, and advances in machine learning to connect user perceptions to specific engineered product features.
Figure: Data collection method (left) and resulting perceptual model (right).
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