Research Papers: Design Theory and Methodology

J. Mech. Des. 2016;138(4):041101-041101-11. doi:10.1115/1.4032265.

Dealing with unforeseeable changing situations, often seen in exploratory and hazardous task domains, requires systems that can adapt to changing tasks and varying environments. The challenge for engineering design researchers and practitioners is how to design such adaptive systems. Taking advantage of the flexibility of multi-agent systems, a self-organizing systems approach has been proposed, in which mechanical cells or agents organize themselves as the environment and tasks change based on a set of predefined rules. To enable self-organizing systems to perform more realistic tasks, a two-field framework is introduced to capture task complexity and agent behaviors, and a rule-based social structuring mechanism is proposed to facilitate self-organizing for better performance. Computer simulation-based case studies were carried out to investigate how social structuring among agents, together with the size of agent population, can influence self-organizing system performance in the face of increasing task complexity. The simulation results provide design insights into task-driven social structures and their effect on the behavior and performance of self-organizing systems.

Commentary by Dr. Valentin Fuster

Research Papers: Design Automation

J. Mech. Des. 2016;138(4):041401-041401-7. doi:10.1115/1.4032629.

Over the past few decades, folding paper has extended beyond the origami deployable applications to reach the engineering field. Nevertheless, mechanical information about paper behavior is still lacking, especially during folding/unfolding. This article proposes an approach to characterize the paper fold behavior in order to extract the material data that will be needed for the simulation of folding and to go a step further the single kinematics of origami mechanisms. The model developed herein from simple experiments for the fold behavior relies on a macroscopic local hinge with a nonlinear torsional spring. Though validated with only straight folds, the model is still applicable in the case of curved folds thanks to the locality principle of the mechanical behavior. The influence of both the folding angle and the fold length is extracted automatically from a set of experimental values exhibiting a deterministic behavior and a variability due to the folding process. The goal is also to propose a methodology that may extend the simple case of the paper crease, or even the case of thin material sheets, and may be adapted to other identification problems.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2016;138(4):041402-041402-11. doi:10.1115/1.4032215.

In this effort, we present novel nonlinear modeling of two solenoid actuated butterfly valves subject to a sudden contraction and then develop an optimal configuration in the presence of highly coupled nonlinear dynamics. The valves are used in the so-called smart systems employed in a wide range of applications including bioengineering, medicine, and engineering fields. Typically, thousands of the actuated valves operate together to regulate the amount of flow and also to avoid probable catastrophic disasters which have been observed in practice. We focus on minimizing the amount of energy used in the system as one of the most critical design criteria to yield an efficient operation. We optimize the actuation subsystems interacting with the highly nonlinear flow loads in order to minimize the amount of energy consumed. The contribution of this work is the inclusion of coupled nonlinearities of electromechanical valve systems to optimize the actuation units. Stochastic, heuristic, and gradient based algorithms are utilized in seeking the optimal design of two sets. The results indicate that substantial amount of energy can be saved by an intelligent design that helps select parameters carefully and also uses flow torques to augment the closing efforts.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2016;138(4):041403-041403-10. doi:10.1115/1.4032630.

Uncertainty is ubiquitous throughout engineering design processes. Robust optimization (RO) aims to find optimal solutions that are relatively insensitive to input uncertainty. In this paper, a new approach is presented for single-objective RO problems with an objective function and constraints that are continuous and differentiable. Both the design variables and parameters with interval uncertainties are represented as affine forms. A mixed interval arithmetic (IA)/affine arithmetic (AA) model is subsequently utilized in order to obtain affine approximations for the objective and feasibility robustness constraint functions. Consequently, the RO problem is converted to a deterministic problem, by bounding all constraints. Finally, nonlinear optimization solvers are applied to obtain a robust optimal solution for the deterministic optimization problem. Some numerical and engineering examples are presented in order to demonstrate the advantages and disadvantages of the proposed approach. The main advantage of the proposed approach lies in the simplicity of the conversion from a nonlinear RO problem with interval uncertainty to a deterministic single-looped optimization problem. Although this approach cannot be applied to problems with black-box models, it requires a minimal use of IA/AA computation and applies some widely used advanced solvers to single-looped optimization problems, making it more suitable for applications in engineering fields.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2016;138(4):041404-041404-12. doi:10.1115/1.4032774.

A set-based approach is presented for exploring multilevel design problems. The approach is applied to design negative stiffness metamaterials with mechanical stiffness and loss properties that surpass those of conventional composites. Negative stiffness metamaterials derive their properties from their internal structure, specifically by embedding small volume fractions of negative stiffness inclusions in a continuous host material. Achieving high stiffness and loss from these materials by design involves managing complex interdependencies among design variables across a range of length scales. Hierarchical material models are created for length scales ranging from the structure of the microscale negative stiffness inclusions to the effective properties of mesoscale metamaterials to the performance of an illustrative macroscale component. Bayesian network classifiers (BNCs) are used to map promising regions of the design space at each hierarchical modeling level, and the maps are intersected to identify sets of multilevel solutions that are likely to provide desirable system performance. The approach is particularly appropriate for highly efficient, top-down, performance-driven, multilevel design, as opposed to bottom-up, trial-and-error multilevel modeling.

Commentary by Dr. Valentin Fuster

Research Papers: Design for Manufacture and the Life Cycle

J. Mech. Des. 2016;138(4):041701-041701-12. doi:10.1115/1.4032504.

Additive manufacturing (AM) has evolved from prototyping to functional part fabrication for a wide range of applications. Challenges exist in developing new product design methodologies to utilize AM-enabled design freedoms while limiting costs at the same time. When major design changes are made to a part, undesired high cost increments may be incurred due to significant adjustments of AM process settings. In this research, we introduce the concept of an additive manufactured variable product platform and its associated process setting platform. Design and process setting adjustments based on a reference part are constrained within a bounded feasible space (FS) in order to limit cost increments. In this paper, we develop a cost-driven design methodology for product families implemented with additive manufactured variable platforms. A fuzzy time-driven activity-based costing (FTDABC) approach is introduced to estimate AM production costs based on process settings. Time equations in the FTDABC are computed in a trained adaptive neuro-fuzzy inference system (ANFIS). The process setting adjustment's FS boundary is identified by solving a multi-objective optimization problem. Variable platform 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.

Commentary by Dr. Valentin Fuster

Research Papers: Design Education

J. Mech. Des. 2016;138(4):042001-042001-12. doi:10.1115/1.4032398.

In the United States, the greatest decline in the number of students in the STEM education pipeline occurs at the university level, where students, who were initially interested in STEM fields, drop-out or move on to other interests. It has been reported that “of the 23 most commonly cited reasons for switching out of STEM, all but 7 had something to do with the pedagogical experience.” Thus, understanding the characteristics of the pedagogical experience that impact students' interest in STEM is of great importance to the academic community. This work tests the hypothesis that there exists a correlation between the semantic structure of lecture content and students' affective states. Knowledge gained from testing this hypothesis will inform educators of the specific semantic structure of lecture content that enhance students' affective states and interest in course content, toward the goal of increasing STEM retention rates and overall positive experiences in STEM majors. A case study involving a series of science and engineering based digital content is used to create a semantic network and demonstrate the implications of the methodology. The results reveal that affective states such as engagement and boredom are consistently strongly correlated to the semantic network metrics outlined in the paper, while the affective state of confusion is weakly correlated with the same semantic network metrics. The results reveal semantic network relationships that are generalizable across the different textually derived information sources explored. These semantic network relationships can be explored by researchers trying to optimize their message structure in order to have its intended effect.

Commentary by Dr. Valentin Fuster

Research Papers: Design of Mechanisms and Robotic Systems

J. Mech. Des. 2016;138(4):042301-042301-9. doi:10.1115/1.4032580.

This paper for the first time investigates the six-dimensional compliance characteristics of orthoplanar springs using a compliance-matrix based approach, and validates them with both finite element (FEM) simulation and experiments. The compliance matrix is developed by treating an orthoplanar spring as a parallel mechanism and is revealed to be diagonal. As a consequence, corresponding diagonal compliance elements are evaluated and analyzed in forms of their ratios, revealing that an orthoplanar spring not only has a large linear out-of-plane compliance but also has a large rotational bending compliance. Both FEM simulation and experiments were then conducted to validate the developed compliance matrix. In the FEM simulation, a total number of 30 types of planar-spring models were examined, followed by experiments that examined the typical side-type and radial-type planar springs, presenting a good agreement between the experiment results and analytical models. Further a planar-spring based continuum manipulator was developed to demonstrate the large-bending capability of its planar-spring modules.

Topics: Springs
Commentary by Dr. Valentin Fuster

Research Papers: Design of Direct Contact Systems

J. Mech. Des. 2016;138(4):043301-043301-8. doi:10.1115/1.4032631.

Due to the lack of knowledge in terms of their flexibility and deformation, spline joints are typically assumed to be rigid in dynamic models of gearboxes, transmissions, and drivetrains. As various dynamic phenomena are associated with the stiffness of a spline joint, any high-fidelity dynamic model of drivetrains must properly capture the stiffness of spline joints. In this study, a general analytical stiffness formulation for spline joints is proposed based on a semi-analytical spline load distribution model. This formulation defines a fully populated stiffness matrix of a spline joint including radial, tilting, and torsional stiffness values as well as off-diagonal coupling terms. A blockwise inversion method is proposed and implemented with this analytical formulation to reduce computational time required. At the end, a detailed parametric study is presented to demonstrate the sensitivity of the spline stiffness matrix to torque level, tooth modifications, misalignments, and tooth indexing errors.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2016;138(4):043302-043302-13. doi:10.1115/1.4032579.

A manufacturing process for fabricating ease-off surfaces of a face gear drive that is provided with controllable unloaded meshing performance and local bearing contact is proposed. In order to control the unloaded meshing performance, a predesigned transmission error, a predesigned contact path, and the length of contact ellipse are applied in the redesign of the ease-off surfaces of the pinion and face gear. A method of point contact between the grinding disk and the manufactured pinion is proposed to generate the pinion's ease-off surface, the grinding disk is driven by a series of parabolic motions. Numerical examples are used to illustrate the application of the proposed method, the proposed method is proven to be feasible, and the redesigned face gear is proven to be able reproduce the predesigned unloaded meshing performance simulated by tooth contact analysis (TCA). The influence of misalignment on unloaded meshing performance is also analyzed.

Commentary by Dr. Valentin Fuster

Technical Brief

J. Mech. Des. 2016;138(4):044501-044501-6. doi:10.1115/1.4032810.

Insights uncovered by research in design cognition are often utilized to develop methods used by human designers; in this work, such insights are used to inform and improve computational methodologies. This paper introduces the heterogeneous simulated annealing team (HSAT) algorithm, a multiagent simulated annealing (MSA) algorithm. HSAT is based on a validated computational model of human-based engineering design and retains characteristics of the model that structure interaction between team members and allow for heterogeneous search strategies to be employed within a team. The performance of this new algorithm is compared to several other simulated annealing (SA) based algorithms on three carefully selected benchmarking functions. The HSAT algorithm provides terminal solutions that are better on average than other algorithms explored in this work.

Commentary by Dr. Valentin Fuster

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