An attractive but little explored field of application of the shape memory technology is the area of rotary actuators, in particular for generating endless motion. This paper presents a miniature rotary motor based on shape memory alloy (SMA) wires and overrunning clutches which produces high output torque and unlimited rotation. The concept features a SMA wire tightly wound around a low-friction cylindrical drum to convert wire strains into large rotations within a compact package. The seesaw motion of the drum ensuing from repeated contraction-elongation cycles of the wire is converted into unidirectional motion of the output shaft by an overrunning clutch fitted between drum and shaft. Following a design process formerly developed by the authors, a six-stage prototype with size envelope of 48´22´30 mm is built and tested. Diverse supply strategies are implemented to optimize either the output torque or the speed regularity of the motor with the following results: maximum torque = 20 Nmm; specific torque = 6.31´10-4 Nmm/mm3; rotation per module = 15 deg/cycle; free continuous speed = 4.4 rpm.

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Concept selection is a critical stage of the engineering design process because of its potential to influence the direction of the final design. While formalized selection methods have been developed to increase its effectiveness and reduce human decision-making biases, research that understands these biases in more detail can provide a foundation for improving the selection process. One important bias that occurs during this process is ownership bias, or an unintentional preference for an individuals’ own ideas over the ideas of others. However, few studies have explored ownership bias in a design setting and the influence of other factors such as the gender of the designer or the “goodness” of an idea. In order to understand the impact of these factors in engineering design education, a study was conducted with 110 engineering students. The results from this study show that male students tend to show ownership bias during concept selection by selecting more of their own ideas while female students tend to show the opposite bias, the Halo Effect, by selecting more of their team members’ concepts. In addition, participants exhibited ownership bias for ideas that were considered good or high quality, but the opposite bias for ideas that were not considered good or high quality. These results add to our understanding of the factors that impact team concept selection and provide empirical evidence of the occurrence of ownership bias and the effects of gender and idea goodness in engineering design education. 

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Nathan Knerr and Daniel Selva
J. Mech. Des 138(9), 091403; doi: 10.1115/1.4033987​

Cityplot is a new visualization technique for engineering design that uses a dimensionally-reduced representation of the design decisions to represent the mapping from the decisions to the criteria upon which a design is judged. The shown Cityplot depicts possible CubeSat constellations to support the 2007 Earth Science Decadal Survey. Each constellation is comprised of up to 4 CubeSats and each CubeSat can select from a list of 7 instruments. Possible CubeSat constellations are “cities” and are placed in a 2d space to be visualized. An individual constellation can also be seen as a table of instruments (rows) being present (black) on a given CubeSat (columns). The benefits, costs and risks of each possible constellation are represented as color-coded “buildings” in each “city”. The criteria in this example are: a tiered count of satisfied Decadal objectives (blue), the average CubeSat Technology Readiness Level (red), lifecycle cost (green), maximum number of lost instruments upon loss of a single satellite (black). A taller building indicates the possible constellation performs better in that criteria. Dark purple “roads” between two designs indicate that only one instrument is either added to or removed from one CubeSat to make one constellation identical to the other. Cityplot simultaneously shows sensitivity of criteria to decisions, criteria tradeoffs and design families via a quick intuitive view of the design space.

Current design theory lacks a systematic method to identify what designers know that helps them to create innovative products. In the early stages of idea generation, designers may find novel ideas come readily to mind, or may become fixated on their own or existing products. This may limit the ability to consider more, and more varied candidate concepts that may potentially lead to innovation. To aid in idea generation, we sought to identify “design heuristics,” or “rules of thumb,” evident in award-winning designs. In this paper, we demonstrate a content analysis method for discovering heuristics in the designs of innovative products. Our method depends on comparison to a baseline of existing products so that the innovative change can be readily identified. Through an analysis of key features and functional elements in the designs of over 400 award-winning products, forty heuristic principles were extracted. These Design Heuristics are outlined according to their perceived role in changing an existing product concept into a novel design, and examples of other products using the heuristics are provided. To demonstrate the ease of use of these Design Heuristics, we examined outcomes from a classroom study, and found that concepts created using Design Heuristics were rated as more creative and varied. The analysis of changes from existing to innovative products can provide evidence of useful heuristic principles to apply in creating new designs.
 

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The synthesis of functional molecular mechanisms is constrained by the notorious difficulties in fabricating nano-links of prescribed shapes and sizes. Thus, the classical mechanism synthesis methods, which assume the ability to manufacture any designed links, cannot provide a systematic process for designing molecular mechanisms. We propose a new approach to build functional mechanisms with prescribed mobility by only using elements from a predefined "link soup". The resulting synthesis procedure is the first of its kind that is capable of systematically synthesizing functional linkages with prescribed mobility constructed from a soup of primitive entities. Furthermore, the proposed systematic approach outputs the ATLAS of candidate mechanisms, which can be further processed for downstream applications. Although the scope of this technique is rather general, its immediate application is the design of molecular machines assembled from nano-links that either exist in nature or can be fabricated.

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​This paper proposes a framework to analyze the architectural complexity of systems developed with a product family. A product family is a set of products that are derived from common sets of parts, interfaces, and processes, known as the product platform. Through the use of product platforms, several variations of products can be developed in a short period of time with relatively low-engineering costs to capture market share in niche market segments as the demand arises. This work can be used in a variety of ways to guide product platform and variant architecture development during the initial concept generation stage. The effectiveness of the proposed framework is demonstrated through a case study of a train bogie platform.
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The process starts with building the design structure matrix (DSM) model, which captures the structural architecture as well as mass, energy, and information flow, for the product platform and its variants. Using the DSMs created and the selected complexity metric, the architectural complexity, which includes the structural complexity and flow complexity values, is assessed. Based on the quantitative results obtained, the overall complexity for the product platform and the product family could be compared with other competing product platform architecture and product family concepts. Furthermore, this process also allows system architects and decision makers to manage overall complexity of an entire product family, either through complexity minimization or by designing the entire platform and product architecture to be less sensitive to engineering changes in terms of complexity fluctuation.

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​Engineers have taken an interest in origami and developing it further for applications. Characteristics of origami of particular interest to engineers include: (1) stowability, (2) portability, (3) deployability, (4) part number reduction, (5) manufacturability from a flat sheet of material, (6) a single manufacturing technique (folding), (7) reduced assembly, (8) ease of miniaturization, and (9) low material volume and mass. Several of these attributes are of particular value in aerospace applications and it is anticipated that many more aerospace mechanisms could be developed through the use of a design process that adapts origami characteristics for use in devices and products. The research presented in this paper has two main objectives: to demonstrate that a design framework can be created to more reliably use origami patterns and principles as the basis for aerospace mechanisms and provide examples that illustrate an approach to designing origami-adapted products. This paper presents the origami-adapted design process, which is then illustrated and tested using three examples of preliminary design: an origami bellows to protect the drill shafts of a Mars Rover, an expandable habitat for the International Space Station, and a deployable parabolic antenna for space and earth communication systems.

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​This work is the first demonstration of the use of a parameterized approach to design optimization on a complex engineering problem. Parameterized optimization solves a family of optimization problems as a function of exogenous parameters. When applied to a subsystem of interest, it results in general knowledge about the capabilities of the subsystem rather than a restrictive point solution. The motivation is that it often is necessary to advance the development of a subsystem independent of system-level specifics. This is true during initial research and development efforts or in sensitive military and competitive industrial design environments in which compartmentalization of information is common and necessary. It also is important in systems development projects when the need for concurrency often requires subsystem designers to make progress in the absence of full information about other interfacing subsystems. We solve this specialized design problem using the predictive parameterized Pareto genetic algorithm (P3GA). The approach is demonstrated for the multifunctional design of a structurally-integrated liquid metal circuit intended to provide integrated cooling functionality.  A family of optimal design solutions associated with values of external parameters (bounded real numbers) is computed efficiently using P3GA. The demonstration employs both high- and low-fidelity multi-physical engineering models seamlessly and results in general knowledge about the subsystem as a function of parameters associated with other interfacing subsystems.

​Dealing with unforeseeable changing situations, often seen in exploratory and hazardous task domains, requires engineered complex systems that can feature flexibility to fulfill multiple resolutions over long lifespans, robustness to deal with environmental changes and resilience to sustain system damage. The current top-down engineering design approach has its limitations in cases where it is impossible to fully consider and predict the true operational uncertainties because the hidden interdependencies among the system components can lead to unforeseeable interactions at operation time. The challenge for engineering design researchers and practitioners is how to devise new ways to design such adaptive systems. Nature embodies certain qualities such as evolution, cellular organization, and self-organizing behavior, which seem to overcome the deficiencies of the traditional top-down engineering design process. Taking advantage of the flexibility of multi-agent systems, we proposed a self-organizing systems approach, in which mechanical cells or agents organize themselves as the environment and tasks change based on a set of predefined rules. This study is positioned at the interface between the science and engineering of complex systems by taking the “by emergence” approach to achieve desired functions of cellular self-organizing systems.

Through the case studies we investigated the impact of social rule based social structuring, measured by social rules adoption rate and the size of agent population, on the system performance in the face of increasing task complexity. The results have shed interesting insights including: the behavior of self-organizing systems becomes more chaotic when tasks are more complex; stronger social structuring is effective for a smaller number of agents and weaker social structuring is more effective for a larger number of agents; and there can be a “singular” number of agents where social structuring is neither effective nor efficient.  In conclusion, self-organization has profound implications in dealing with task complexity and can be used intentionally as a tool in the design of adaptive complex systems. The balance of task complexity, the number of agents, and social structuring is the key. Understanding self-organization, minimizing harmful effects, and promoting positive effects will become essential in future engineering design of complex systems.

Soheil Arastehfar, Ying Liu and Wen Feng Lu
J. Mech. Des 138(3), 031103 (Feb 01, 2016); doi: 10.1115/1.4032396
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​Digital prototype (DP), as a form of communication media, allows designers to communicate design concepts to users by rendering the physical characteristics, e.g., size, colour, and texture. One important aspect is how well users can estimate the values of the physical characteristics of design concepts through interactions with DPs. Better estimates can lead to better perceptions of the designed attributes closely associated with the physical characteristics, and hence, useful user feedback about design concepts. The correctness of the estimates depends on two crucial factors: the ability of DPs to render physical characteristics and the way DPs are used to communicate physical characteristics in a particular environment and via different input/output devices. To date, little attention has been paid to the latter. Hence, it is important to identify an effective way of using DPs via the effectiveness assessment of various possibilities. This paper introduces a methodology for evaluating the effectiveness of communicating physical characteristics to users using DPs. During user interactions with DPs, the methodology collects user estimates of various physical characteristics and assesses the estimates on three dimensions, i.e., degree of correctness, time to make an estimate and handling of different values. The assessments are then evaluated by statistical analysis to reveal the effectiveness of the way of engaging DPs in helping users correctly and quickly estimate the values. The evaluated effectiveness reflects how successful the way of using a DP is, and also helps to suggest a better approach.