Topology optimization for mechanism synthesis has been developed for the simultaneous determination of the number and dimension of mechanisms. However, these methods can be used to synthesize linkage mechanisms that consist only of links and joints because other types of mechanical elements such as gears cannot be simultaneously synthesized. In this study, we aim to develop a gradient-based topology optimization method which can be used to synthesize mechanisms consisting of both linkages and gears. For the synthesis, we propose a new ground model defined by two superposed design spaces: the linkage and gear design spaces. The gear design space is discretized by newly proposed gear blocks, each of which is assumed to rotate as an output gear, while the linkage design space is discretized by zero-length-spring-connected rigid blocks. Another set of zero-length springs is introduced to connect gear blocks to rigid blocks, and their stiffness values are varied to determine the existence of gears when they are necessary to produce the desired path. After the proposed topology-optimization-based synthesis formulation and its numerical implementation are presented, its effectiveness and validity are checked with various synthesis examples involving gear-linkage and linkage-only mechanisms.

A bracing device for stabilizing cardiac catheters inside the heart was developed to provide surgical-level dexterity to minimally invasive catheter-based procedures for cardiac valve disease. The brace was designed to have a folding structure, which lies flat along a catheter during navigation through vasculature and then unfolds into a rigid bracing configuration after deployment across the interatrial septum. The brace was designed to be easily deployable, provide bracing support for a transseptal catheter, and also be compliant enough to be delivered to the heart via tortuous vasculature. This aims to improve dexterity in catheter-based mitral valve repair and enable other complex surgical procedures to be done with minimally invasive instruments.

As additive manufacturing (AM) matures, models are beginning to take a more prominent stage in design and process planning for AM. A limitation frequently encountered in AM models is a lack of indication about their precision and accuracy. Often overlooked, information on model uncertainty is required for validation of AM models, qualification of AM-produced parts, and uncertainty management. This paper presents a discussion on the origin and propagation of uncertainty in laser powder bed fusion (L-PBF) models. Four sources of uncertainty are identified: modeling assumptions, unknown simulation parameters, numerical approximations, and measurement error in calibration data. Techniques to quantify uncertainty in each source are presented briefly, along with estimation algorithms to diminish prediction uncertainty with the incorporation of online measurements. The methods are illustrated with a case study based on a transient, stochastic thermal model designed for melt pool width predictions. Model uncertainty is quantified for single track experiments and the effect of online estimation in overhanging structures is studied via simulation. The application of these concepts to estimation and control of the L-PBF process is suggested.

A new concept for a mechanical antilock braking system (ABS) with a centrifugal braking device (CBD), termed a centrifugal ABS (C-ABS), is presented and developed in this paper. This new CBD functions as a brake in which the output braking torque adjusts itself depending on the speed of the output rotation. First, the structure and mechanical models of the entire braking system are introduced and established. Second, a numerical computer program for simulating the operation of the system is developed. The characteristics of the system can be easily identified and can be designed with better performance by using this program to studying the effects of different design parameters. Finally, the difference in the braking performance between the C-ABS and the braking system with or without a traditional ABS is discussed. The simulation results indicate that the C-ABS can prevent the wheel from locking even if excessive operating force is provided while still maintaining acceptable braking performance.

A circular shape placed on an incline will roll; similarly, an irregularly shaped object, such as the Archimedean spiral, will roll on a flat surface when a force is applied to its axle. This rolling is dependent on the specific shape and the applied force (magnitude and location). In this paper, we derive formulas that define the behavior of irregular 2D and 3D shapes on a flat plane when a weight is applied to the shape's axle. These kinetic shape (KS) formulas also define and predict shapes that exert given ground reaction forces when a known weight is applied at the axle rotation point. Three 2D KS design examples are physically verified statically with good correlation to predicted values. Motion simulations of unrestrained 2D KS yielded expected results in shape dynamics and self-stabilization. We also put forth practical application ideas and research for 2D and 3D KS such as in robotics and gait rehabilitation.

Analytical load-displacement relations for flexure mechanisms, formulated by integrating the individual analytical models of their building-blocks (i.e., flexure elements), help in understanding the constraint characteristics of the whole mechanism. In deriving such analytical relations for flexure mechanisms, energy based approaches generally offer lower mathematical complexity, compared to Newtonian methods, by reducing the number of unknowns-specifically, the internal loads. To facilitate such energy based approaches, a closed-form nonlinear strain energy expression for a generalized bisymmetric spatial beam flexure is presented in this paper. The strain energy, expressed in terms of the end-displacement of the beam, considers geometric nonlinearities for intermediate deformations, enabling the analysis of flexure mechanisms over a finite range of motion. The generalizations include changes in the initial orientation and shape of the beam flexure due to potential misalignment or manufacturing. The effectiveness of this approach is illustrated via the analysis of a multilegged table flexure mechanism. The resulting analytical model is shown to be accurate using nonlinear finite elements analysis, within a load and displacement range of interest.

Despite the importance of the architectural modularity of products and systems, existing modularity metrics or algorithms do not account for overlapping and hierarchically embedded modules. This paper presents a graph theoretic spectral approach to characterize the degree of modular hierarchical-overlapping organization in the architecture of products and complex engineered systems. It is shown that the eigenvalues of the adjacency matrix of a product architecture graph can reveal layers of hidden modular or hierarchical modular organization that are not immediately visible in the predefined architectural description. We use the approach to analyze and discuss several design, management, and system resilience implications for complex engineered systems.

In this study, we present an analytical approach for synthesizing line actuation spaces of a parallel flexure mechanism (PFM) that can help designers to arrange linear actuators within the PFM in a correct and optimal way. On the basis of screw theory and upon an assumption of small deformations, an important synthesis criterion stated as "any actuation space of a flexure mechanism is always linearly independent of its constraint space" has been derived and disclosed for the first time. Guided by this criterion, a general synthesis process for the line actuation spaces of PFMs is introduced and demonstrated with several selective examples. The proposed synthesis criterion and process will enable designers to (i) systematically formulate line actuation spaces in the format of screw systems; (ii) likely yield a multiple solution to actuation spaces; and (iii) potentially determine an optimal result from those alternatives for actuator placement.

This paper presents a symbolic formulation for analytical compliance analysis and synthesis of flexure mechanisms with serial, parallel, or hybrid topologies. Our approach is based on the screw theory that characterizes flexure deformations with motion twists and loadings with force wrenches. In this work, we first derive a symbolic formulation of the compliance and stiffness matrices for commonly used flexure elements, flexure joints, and simple chains. Elements of these matrices are all explicit functions of flexure parameters. To analyze a general flexure mechanism, we subdivide it into multiple structural modules, which we identify as serial, parallel, or hybrid chains. We then analyze each module with the known flexure structures in the library. At last, we use a bottom-up approach to obtain the compliance/stiffness matrix for the overall mechanism. This is done by taking appropriate coordinate transformation of twists and wrenches in space. Four practical examples are provided to demonstrate the approach. A numerical example is employed to compare analytical compliance models against a finite element model. The results show that the errors are sufficiently small (2%, compared with finite element (FE) model), if the range of motion is limited to linear deformations. This work provides a systematical approach for compliance analysis and synthesis of general flexure mechanisms. The symbolic formulation enables subsequent design tasks, such as compliance synthesis or sensitivity analysis.

Creating highly articulated miniature structures requires assembling a large number of small parts. This is a very challenging task and increases cost of mechanical assemblies. Insert molding presents the possibility of creating a highly articulated structure in a single molding step. This can be accomplished by placing multiple metallic bearings in the mold and injecting plastic on top of them. In theory, this idea can generate a multi degree of freedom structures in just one processing step without requiring any post molding assembly operations. However, the polymer material has a tendency to shrink on top of the metal bearings and hence jam the joints. Hence, until now insert molding has not been used to create articulated structures. This paper presents a theoretical model for estimating the extent of joint jamming that occurs due to the shrinkage of the polymer on top of the metal bearings. The level of joint jamming is seen as the effective torque needed to overcome the friction in the revolute joints formed by insert molding. We then use this model to select the optimum design parameters which can be used to fabricate functional, highly articulating assemblies while meeting manufacturing constraints. Our analysis shows that the strength of weld-lines formed during the in-mold assembly process play a significant role in determining the minimum joint dimensions necessary for fabricating functional revolute joints. We have used the models and methods described in this paper to successfully fabricate the structure for a minimally invasive medical robot prototype with potential applications in neurosurgery. To the best of our knowledge, this is the first demonstration of building an articulated structure with multiple degrees of freedom using insert molding.

The rapid rise in technologies for data collection has created an unmatched opportunity to advance the use of data-rich tools for lifecycle decision-making. However, the usefulness of these technologies is limited by the ability to translate lifecycle data into actionable insights for human decision-makers. This is especially true in the case of sustainable lifecycle design (SLD), as the assessment of environmental impacts, and the feasibility of making corresponding design changes, often relies on human expertise and intuition. Supporting human sense-making in SLD requires the use of both data-driven and user-driven methods while exploring lifecycle data. A promising approach for combining the two is through the use of visual analytics (VA) tools. Such tools can leverage the ability of computer-based tools to gather, process, and summarize data along with the ability of human-experts to guide analyses through domain knowledge or data-driven insight. In this paper, we review previous research that has created VA tools in SLD. We also highlight existing challenges and future opportunities for such tools in different lifecycle stages-design, manufacturing, distribution & supply chain, use-phase, end-of-life, as well as life cycle assessment. Our review shows that while the number of VA tools in SLD is relatively small, researchers are increasingly focusing on the subject matter. Our review also suggests that VA tools can address existing challenges in SLD and that significant future opportunities exist.

Recovering a system's underlying structure from its historical records (also called structure mining) is essential to making valid inferences about that system's behavior. For example, making reliable predictions about system failures based on maintenance work-order data requires determining how concepts described within the work order are related. Obtaining such structural information is challenging, requiring system understanding, synthesis, and representation design. This is often either too difficult or too time-consuming to produce. Consequently, a common approach to quickly eliciting tacit structural knowledge from experts is to gather uncontrolled keywords as record labels-i.e., "tags." One can then map those tags to concepts within the structure and quantitatively infer relationships between them. Existing models of tag similarity tend to either depend on correlation strength (e.g. overall co-occurrence frequencies), or on conditional strength (e.g. tag sequence probabilities). A key difficulty in applying either model is understanding under what conditions one is better than the other for overall structure recovery. In this paper, we investigate the core assumptions and implications of these two classes of similarity measures on structure recovery tasks. Then, using lessons from this characterization, we borrow from recent psychology literature on semantic fluency tasks to construct a tag similarity measure that emulates how humans recall tags from memory. We show through empirical testing that this method combines strengths of both common modeling paradigms. We also demonstrate its potential as a pre-processor for structure mining tasks via a case study in semi-supervised learning on real excavator maintenance work-orders.