Defining functions when dissecting a product is an important method to learn how it works.  There are three primary methods for identifying functions in a product.  In this study, we tested the hypothesis that one of the methods is superior to the others.  We tested the three methods in a within-subjects design of experiments to see how each method compared to the others, as measured by the size, shape, and content of each function tree.  We found that there was no dominant method for discovering functions in a dissected device.  This implies that any method is appropriate for understanding how a device works. Additionally, we found generalized aspects of creating function diagrams that are difficult for novice designers.

Lead users play a vital role in next generation product development, as they help designers discover relevant product feature preferences months or even years before they are desired by the general customer base. Existing design methodologies proposed to extract lead user preferences are typically constrained by temporal, geographic, size and heterogeneity limitations. To mitigate these challenges, the authors of this work propose a set of mathematical models that mine social media networks for lead users and the product features that they express relating to specific products.  The authors hypothesize that i) lead users are discoverable from large scale social media networks and ii) product feature preferences, mined from lead user social media data, represent product features that do not currently exist in product offerings but will be desired in future product launches. An automated approach to lead user product feature identification is proposed to identify latent features (product features unknown to the public) from social media data. These latent features then serve as the key to discovering innovative users from the ever increasing pool of social media users. The authors collect  ~2.1 billion social media messages in the United States during a period of 31 months (from March 2011 to September 2013) in order to determine whether lead user preferences are discoverable and relevant to next generation smartphone designs.

For the Abstract and Full Paper please see ASME's Digital Collection.

 Sen Lin1, Longyu Zhao, James K. Guest, Timothy P. Weihs and Zhenyu Liu
J. Mech. Des 137(8), 081402 (Aug 01, 2015); doi: 10.1115/1.4030297 

Fixed geometry fluid diodes are devices that allow fluid to flow in one direction but inhibit flow in the reverse direction.  Unlike valves, which have moving parts, fixed geometry fluid diodes achieve this effect by using the inertia of the fluid to guide flow into tortuous paths in the reverse flow case.  Topology optimization is used in this paper to design diodes of various aspect ratios, including an example to reproduce the Tesla valve, a fixed geometry diode originally designed and patented by Nicola Tesla.  The objective function is to maximize diodicity, measured as the ratio of pressure drop in the reverse flow case to the forward flow case, and a gradient-based optimizer is used to solve the topology optimization formulation. An optimized design was 3D printed and experimentally tested to verify diode-like behavior.