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.  

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Additive manufacturing (AM) techniques provide designers with greater freedom in creating customized products with complex shapes. When major design changes are made to a part, undesirable high cost increments may be incurred due to AM process setting adjustments, challenging designers to explore AM-enabled design freedom while controlling costs at the same time. In this research, we introduce the concept of a variable product platform and its associated AM process setting platform, based on which the design and process setting adjustments can be restricted within a bounded feasible space in order to limit cost increments. Fuzzy Time-Driven Activity-Based Costing (FTDABC) approach is introduced to predict AM production costs based on process settings. The process setting adjustment’s feasible space boundary is identified by solving a multiobjective optimization problem. Design parameter limitations are computed in a Mamdani-type expert system and then used as constraints in the design optimization to maximize customer perceived utility. Case studies on designing an R/C racing car family illustrate the proposed methodology and demonstrate that the optimized additive manufactured variable platforms can improve product performances at lower costs than conventional consistent platform based design.

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​Computational Design Synthesis (CDS) methods can be used to enable the computer to generate valid and even creative solutions for engineering tasks. In grammatical approaches to CDS, formal grammars are used to represent a desired design language. This language consists of vocabulary that usually describes components and subsystems of a design and a set of grammar rules that describe possible design transformations. The formalized engineering knowledge can then be used by the computer to synthesize designs. For most engineering tasks, two different kinds of rules are required: rules that change the topology of a design, i.e. how the components are connected, and rules that change parameters of a design. One of the main challenges in CDS using topologic and parametric grammar rules is to decide a priori which type of rule to apply in which stage of the synthesis process as well as whether to start from a valid design and perturb it or to start from a void design. The research presented in this paper compares different strategies for topologic and parametric rule applications during automated design synthesis driven by a search algorithm. The presented strategies are compared considering quantity and quality of the generated designs. The effect of the strategies, the selected search algorithm, and the initial design, from which the synthesis is started, are analyzed for two case studies: the synthesis of gearboxes and of bicycle frames. Results show that the effect of the strategy is dependent on the design task and recommendations are given on which strategies to use for which design task.

Multi-material polymer printers allow the placement of different materials within a composite. The individual material phases can be spatially arranged and shaped in an almost arbitrary fashion. Utilizing the shape memory behavior of at least one of the material phases, active composites can be 3D printed such that they deform from an initially flat plate into a curved structure. To navigate this vast design space, systematically and efficiently explorer design options, and find an optimum layout of the composite this paper presents a novel design optimization approach. The optimization approach combines a level set method for describing the material layout and a generalized formulation of the extended finite element method (XFEM) for predicting the response of the printed active composite (PAC). This combination of methods yields optimization results that can be directly printed without the need for additional post-processing steps. The proposed optimization method is studied with examples where the target shapes correspond to a plate-bending type deformation and to a localized deformation. The optimized designs are 3D printed and the XFEM predictions are compared against the experimental measurements. The design studies demonstrate the ability of the proposed optimization method to yield a crisp and highly resolved description of the optimized material layout that can be realized by 3D printing.

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Rigid-body mechanisms (RBMs) and compliant mechanisms (CMs) are traditionally treated in significantly different ways. In this paper, we present an approach to the synthesis of both RBMs and CMs. In this approach, RBMs and CMs are generalized into mechanisms that consist of five basic modules, including Compliant Link (CL), Rigid Link (RL), Pin Joint (PJ), Compliant Joint (CJ), and Rigid Joint (RJ). The link modules and joint modules are modeled with beam and hinge elements, respectively, in a geometrically nonlinear finite element solver, and subsequently a discrete beam-hinge ground structure model is established. Based on this discrete beam-hinge model, a procedure that follows topology optimization is developed, called module optimization. Particularly, in the module optimization approach, the states (both presence or absence and sizes) of joints and links are all design variables, and one may obtain a RBM, a partially CM, or a fully CM for a given mechanical task. The proposed approach has thus successfully addressed the challenge in the type and dimensional synthesis of RBMs and CMs. Three design examples of the path generator are discussed to demonstrate the effectiveness of the proposed approach.