Research Papers: Design Theory and Methodology

J. Mech. Des. 2017;139(4):041101-041101-12. doi:10.1115/1.4035793.

The performance of a team with the right characteristics can exceed the mere sum of the constituent members' individual efforts. However, a team having the wrong characteristics may perform more poorly than the sum of its individuals. Therefore, it is vital that teams are assembled and managed properly in order to maximize performance. This work examines how the properties of configuration design problems can be leveraged to select the best values for team characteristics (specifically team size and interaction frequency). A computational model of design teams which has been shown to effectively emulate human team behavior is employed to pinpoint optimized team characteristics for solving a variety of configuration design problems. These configuration design problems are characterized with respect to the local and global structure of the design space, the alignment between objectives, and the resources allotted for solving the problem. Regression analysis is then used to create equations for predicting optimized values for team characteristics based on problem properties. These equations achieve moderate to high accuracy, making it possible to design teams based on those problem properties. Further analysis reveals hypotheses about how the problem properties can influence a team's search for solutions. This work also conducts a cognitive study on a different problem to test the predictive equations. For a configuration problem of moderate size, the model predicts that zero interaction between team members should lead to the best outcome. A cognitive study of human teams verifies this surprising prediction, offering partial validation of the predictive theory.

Topics: Design , Teams
Commentary by Dr. Valentin Fuster
J. Mech. Des. 2017;139(4):041102-041102-12. doi:10.1115/1.4035431.

Over the last two decades, consumers have become increasingly aware and desiring of sustainable products. However, little attention has been paid to developing conceptual design methods that explicitly take into account environmental impact. This paper contributes a method of automated function component generation, and guided down-selection and decision-making based upon environmental impact. The environmental impact of functions has been calculated for 17 of the products found in the Design Repository using ReCiPe scoring in SimaPRO. A hierarchical Bayesian approach is used to estimate the potential environmental impacts of specific functions when realized into components. Previously, product environmental impacts were calculated after a product was developed to the component design stage. The method developed in this paper could be used to provide a criticality ranking based on which functional solutions historically have the greatest risk of causing high environmental impact. The method is demonstrated using a simple clock system as an example. A comparative case study of two phone chargers for use in third-world countries demonstrates the decision-making capabilities of this method, and shows that it is possible to compare the environmental impact of alternative function structures during the conceptual stage of design. With the method presented in this paper, it is now possible to make early functional modeling design decisions specifically taking into account historical environmental impact of functionally similar products.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2017;139(4):041103-041103-11. doi:10.1115/1.4035859.

Trying to decide whether to purchase a sustainable product often puts decision makers in a difficult situation, especially if the more sustainable option provides less desirable features or costs a premium. This paper theorizes that adding sustainability as a variable during product choice evaluations create decisions that are moral choice scenarios, where benefit to society is weighed against personal gain. From an engineering design perspective, modeling user preferences in this context can be extremely difficult. While several methods exist to assist researchers in eliciting consumer preferences, the vast majority relies upon conscious input from the potential consumers themselves. More critically, these methods do not afford researchers the ability to understand the cognitive mechanisms underlying what someone may be feeling or thinking while these preference judgments are being made. In this work, functional magnetic resonance imaging (fMRI) is used to investigate the neural processes behind multi-attribute product preference judgments. In particular, this work centers on uncovering unique features of sustainable preference judgments: preference judgments that involve products for which the environmental impact is a known quantity. This work builds upon earlier work that investigated how preference judgments are altered in the context of sustainability. A deeper look at participant decision making at the time of judgment is examined using neuroimaging with the goal of providing actionable insights for designers and product developers.

Commentary by Dr. Valentin Fuster

Research Papers: Design Automation

J. Mech. Des. 2017;139(4):041401-041401-9. doi:10.1115/1.4035503.

This paper presents a method for design optimization of brass wind instruments. The shape of a trumpet's bore is optimized to improve intonation using a physics-based sound simulation model. This physics-based model consists of an acoustic model of the resonator, a mechanical model of the excitator, and a model of the coupling between the excitator and the resonator. The harmonic balance technique allows the computation of sounds in a permanent regime, representative of the shape of the resonator according to control parameters of the virtual musician. An optimization problem is formulated in which the objective function to be minimized is the overall quality of the intonation of the different notes played by the instrument. The design variables are the physical dimensions of the resonator. Given the computationally expensive function evaluation and the unavailability of gradients, a surrogate-assisted optimization framework is implemented using the mesh adaptive direct search algorithm (MADS). Surrogate models are used both to obtain promising candidates in the search step of MADS and to rank-order additional candidates generated by the poll step of MADS. The physics-based model is then used to determine the next design iterate. Two examples (with two and five design optimization variables) demonstrate the approach. Results show that significant improvement of intonation can be achieved at reasonable computational cost. Finally, the perspectives of this approach for computer-aided instrument design are evoked, considering optimization algorithm improvements and problem formulation modifications using for instance different design variables, multiple objectives and constraints or objective functions based on the instrument's timbre.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2017;139(4):041402-041402-10. doi:10.1115/1.4035792.

Time-dependent reliability problems widely appear in the engineering practice when the material properties of the structure deteriorate in time or random loading modeled as random processes is involved. Among existing methods to the time-dependent reliability problems, the most dominating one is the outcrossing rate method. This paper presents an outcrossing rate model and its efficient calculation approach for system problems, and based on the presented model, a time-dependent system reliability analysis method is proposed. The main idea of the method is to transform the evaluation of the system outcrossing rates into the calculation of a time-invariant system reliability. Three numerical examples are used to demonstrate the effectiveness of the proposed method.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2017;139(4):041403-041403-11. doi:10.1115/1.4035862.

In recent years, evolutionary algorithms based on the concept of “decomposition” have gained significant attention for solving multi-objective optimization problems. They have been particularly instrumental in solving problems with four or more objectives, which are further classified as many-objective optimization problems. In this paper, we first review the cause-effect relationships introduced by commonly adopted schemes in such algorithms. Thereafter, we introduce a decomposition-based evolutionary algorithm with a novel assignment scheme. The scheme eliminates the need for any additional replacement scheme, while ensuring diversity among the population of candidate solutions. Furthermore, to deal with constrained optimization problems efficiently, marginally infeasible solutions are preserved to aid search in promising regions of interest. The performance of the algorithm is objectively evaluated using a number of benchmark and practical problems, and compared with a number of recent algorithms. Finally, we also formulate a practical many-objective problem related to wind-farm layout optimization and illustrate the performance of the proposed approach on it. The numerical experiments clearly highlight the ability of the proposed algorithm to deliver the competitive results across a wide range of multi-/many-objective design optimization problems.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2017;139(4):041404-041404-12. doi:10.1115/1.4035860.

In time-dependent reliability analysis, the first-passage method has been extensively used to evaluate structural reliability under time-variant service circumstances. To avoid computing the outcrossing rate in this method, surrogate modeling may provide an effective alternative for calculating the time-dependent reliability indices in structural analysis. A novel approach, namely time-dependent reliability analysis with response surface (TRARS), is thus introduced in this paper to estimate the time-dependent reliability for nondeterministic structures under stochastic loads. A Gaussian stochastic process is generated by using the expansion optimal linear estimation (EOLE) method which has proven to be more accurate and efficient than some series expansion discretization techniques. The random variables and maximum responses of uncertain structures are treated as the input and output parameters, respectively. Through introducing the response surface (RS) model, a novel iterative procedure is proposed in this study. A Bucher strategy is adopted to generate the initial sample points, and a gradient projection technique is used to generate new sampling points for updating the RS model in each iteration. The time-dependent reliability indices and probabilities of failure are thus obtained efficiently using the first-order reliability method (FORM) over a certain design lifetime. In this study, four demonstrative examples are provided for illustrating the accuracy and efficiency of the proposed method.

Commentary by Dr. Valentin Fuster

Research Papers: Design for Manufacture and the Life Cycle

J. Mech. Des. 2017;139(4):041701-041701-10. doi:10.1115/1.4035826.

In machining process planning, it is critical to ensure that the part created following the manufacturing steps complies with the designated design tolerances. However, the challenge is that manufacturing errors are stochastic in nature and are introduced at almost every step of executing a plan, for example, due to inaccuracy of tooling, misalignment of location, etc. Furthermore, these errors accumulate or “stack up” as the machining process progresses to inevitably produce a part that varies from the original design. The resulting variations should be within prescribed design tolerances for the manufactured part to be acceptable. In this work, we present a novel approach for assessing the manufacturing errors by representing variations of nominal features with transformations that are defined in terms of extents of the features' degrees-of-freedom (DOFs) within their design and manufacturing tolerance zones (MTZs). We show how the manufacturing errors stackup can be effectively represented by the composition and intersection of these transformations. Several examples representing scenarios of different complexities are demonstrated to show the applicability of our approach in assessing the influence of manufacturing errors on the design tolerances following a machining plan. Discussions of our approach are provided to address concerns with the accuracy and efficiency as well as to disclose the potential of our approach to enable a tolerance-aware process planning system.

Commentary by Dr. Valentin Fuster

Research Papers: Design of Mechanisms and Robotic Systems

J. Mech. Des. 2017;139(4):042301-042301-10. doi:10.1115/1.4035587.

Robots designed for space applications, deep sea applications, handling of hazardous material and surgery should ideally be able to handle as many potential faults as possible. This paper provides novel indices for fault tolerance analysis of redundantly actuated parallel robots. Such robots have the potential for higher accuracy, improved stiffness, and higher acceleration compared to similar-sized serial robots. The faults considered are free-swinging joint failures (FSJFs), defined as a software or hardware fault, preventing the administration of actuator torque on a joint. However, for a large range of robots, the proposed indices are applicable also to faults corresponding to the disappearance of a kinematic chain, for example, a breakage. Most existing fault tolerance indices provide a ratio between a robot's performance after the fault and the performance before the fault. In contrast, the indices proposed in this paper provide absolute measures of a robot's performance under the worst-case faults. The proposed indices are based on two recently introduced metrics for motion/force transmission analysis of parallel robots. Their main advantage is their applicability to parallel robots with arbitrary degrees-of–freedom (DOF), along with their intuitive geometric interpretation. The feasibility of the proposed indices is demonstrated through application on a redundantly actuated planar parallel mechanism.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2017;139(4):042302-042302-9. doi:10.1115/1.4035677.

A parallel mechanism possesses several advantages compared to a similar-sized serial mechanism, including the potential for higher accuracy and reduced moving mass, the latter enabling increased load capacity and higher acceleration. One of the most important issues affecting a parallel mechanism is the potential of parallel singularities. Such configurations strongly affect the performance of a parallel mechanism, both in the actual singularity and in its vicinity. For example, both the stiffness of a mechanism and the efficiency of the power transmission to the tool platform are related to the closeness to singular configurations. A mechanism with a mobility larger than the mobility of its tool platform is referred to as a kinematically redundant mechanism. It is well known that introducing kinematic redundancy enables a mechanism to avoid singular configurations. In this paper, three novel kinematically redundant planar parallel mechanisms are proposed. All three mechanisms provide planar translations of the tool platform in two degrees-of-freedom, in addition to infinite rotation of the platform around an axis normal to the plane of the translations. The unique feature of the proposed mechanisms is that, with the appropriate inverse kinematics solutions, all configurations in the entire workspace feature optimal singularity avoidance. It is demonstrated how it is sufficient to employ five actuators to achieve this purpose. In addition, it is shown how including more than five actuators significantly reduces the required actuator motions for identical motions of the tool platform, thereby reducing the cycle times for typical applications.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2017;139(4):042303-042303-9. doi:10.1115/1.4035965.

This paper mainly deals with the determinate design/synthesis of a class of symmetrical and monolithic flexure mechanisms. Each is composed of six identical in-plane wire beams with uniform square cross sections. These flexure stages can provide three out-of-plane tip–tilt–piston motions for applications in high-precision or miniaturization environments. A generic symmetrical structure is proposed at first with a group of defined parameters considering constraint and noninterference conditions. Normalized static analytical compliance entries for the diagonal compliance matrix of a generic structure are derived and symbolically represented by the parameters. Comprehensive compliance analysis is then followed using the analytical results, and quick insights into the effects of parameters on compliances in different directions are gained. Case studies without and with actuation consideration are finally discussed. As a second contribution, a physical prototype with three actuation legs is monolithically fabricated (using computer numerical control milling machining), kinematically modeled, and experimentally tested, which shows that the desired out-of-plane motion can be generated from the in-plane actuation.

Commentary by Dr. Valentin Fuster

Technical Brief

J. Mech. Des. 2017;139(4):044501-044501-4. doi:10.1115/1.4035678.

Stochastic finite-element analysis of composite plates due to low velocity impact (LVI) is studied, considering the material properties (Young's modulii, Poisson's ratio, strengths, and fracture energy) and initial velocity as random parameters. Damage initiation and propagation failure due to matrix cracking are investigated for safety criteria for the LVI. Progressive damage mechanics is employed to predict the stochastic dynamic response of the plates. The Gaussian process response surface method (GPRSM) is presently adopted to determine the probability of failure (Pf). There is a possibility of underestimation of the peak contact force and displacement by 10.7% and 11.03%, respectively, if the scatter in the properties is not considered. The sensitivity-based probabilistic design optimization procedure is investigated to achieve better strength and lighter weight of composite for body armors.

Commentary by Dr. Valentin Fuster

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In