Guest Editorial

Commentary by Dr. Valentin Fuster

Research Papers

J. Mech. Des. 2015;137(5):051001-051001-12. doi:10.1115/1.4028530.

Innovation, including engineering innovation, is essential for economic growth. Currently, while most design practices in engineering education focus on aspects of “good” technical design, elements of innovation may be neglected. This research investigates design process activities that yield innovative artifacts. Specifically, we examine the types of design activities, their timing, and the associations among each other. Specifically, two research questions are explored. First, what design activities do teams engage in that relate to the innovativeness of the resultant design artifact? Second, how do these design activities impact the succeeding activities across the design process (from problem definition to working prototype (WP))? To explore these questions, 16 senior capstone bioengineering design teams are followed as they advance from initial conceptualization to WP over an average 23 week period. Several significant measures suggest that innovative teams differ from their noninnovative counterparts in terms of what activities they engage in, how much they engage in the particular activities, and in what phase they conduct the activities. Specifically, certain activities utilized in the early phase (e.g., marketing) are essential for innovation. Moreover, in terms of iterations through activities, spending significant time and effort while developing a design, as well as having smooth, rich iterations throughout the process contribute to the innovativeness of the artifact.

Topics: Design , Teams , Innovation
Commentary by Dr. Valentin Fuster

Research Papers: Design Theory and Methodology

J. Mech. Des. 2015;137(5):051101-051101-10. doi:10.1115/1.4029519.

In this paper, we explore the possibility of reconciling and integrating practical affordance- and function-based design representations. We present a classic function-based design method and representation and argue for the benefits of augmenting it with affordance-based approaches. Building on existing function concept ontologies, we present an integrated approach to developing early-stage design representations. This approach combines the use of affordance and function representations to capture user needs across a device's life cycle. We demonstrate how affordances add rigor and expressiveness to the early stages of traditional design processes, and how traditional function-based tools provide affordance-based design (ABD) with structured methods for concept generation. The integrated approach is illustrated with an example, in which a use case is explicitly decomposed to demonstrate the structure of relationships between users, goals, actions, artifacts, functions, and affordances.

Topics: Design
Commentary by Dr. Valentin Fuster
J. Mech. Des. 2015;137(5):051102-051102-13. doi:10.1115/1.4029585.

An engineering design curriculum that introduces functional modeling methods is believed to enhance the ability to abstract complex systems, assist during the concept generation phase of design, and reduce design fixation. To that end, a variety of techniques for considering function during design have been proposed in the literature, yet there are a lack of validated approaches for teaching students to generate functional models and no reliable method for the assessment of functional models. This paper presents a study investigating students' ability to generate functional models during a homework assignment; the study includes three different treatment conditions: (1) students who receive only a lecture on functional modeling, (2) students who receive a lecture on functional modeling as well as a step-by-step example, and (3) students who receive a lecture, a step-by-step example, and an algorithmic approach with grammar rules. The experiment was conducted in a cornerstone, undergraduate engineering design course, and consequently, was the students' first exposure to functional modeling. To assess student generated functional models across all three conditions, an 18 question functional model scoring rubric was developed based on flow-based functional modeling standards. Use of the rubric to assess the student generated functional models resulted in high inter-rater agreement for total score. Results show that students receiving the step-by-step example perform as well as students receiving the step-by-step example and an algorithmic approach with grammar rules; both groups perform better than the lecture-only group.

Commentary by Dr. Valentin Fuster

Research Papers: Design Automation

J. Mech. Des. 2015;137(5):051401-051401-9. doi:10.1115/1.4029520.

Time-dependent reliability analysis requires the use of the extreme value of a response. The extreme value function is usually highly nonlinear, and traditional reliability methods, such as the first order reliability method (FORM), may produce large errors. The solution to this problem is using a surrogate model of the extreme response. The objective of this work is to improve the efficiency of building such a surrogate model. A mixed efficient global optimization (m-EGO) method is proposed. Different from the current EGO method, which draws samples of random variables and time independently, the m-EGO method draws samples for the two types of samples simultaneously. The m-EGO method employs the adaptive Kriging–Monte Carlo simulation (AK–MCS) so that high accuracy is also achieved. Then, Monte Carlo simulation (MCS) is applied to calculate the time-dependent reliability based on the surrogate model. Good accuracy and efficiency of the m-EGO method are demonstrated by three examples.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2015;137(5):051402-051402-9. doi:10.1115/1.4029756.

The Monte Carlo simulation (MCS) can provide high reliability evaluation accuracy. However, the efficiency of the crude MCS is quite low, in large part because it is computationally expensive to evaluate a very small failure probability. In this paper, a subset simulation-based reliability analysis (SSRA) approach is combined with multidisciplinary design optimization (MDO) to improve the computational efficiency in reliability-based MDO (RBMDO) problems. Furthermore, the sequential optimization and reliability assessment (SORA) approach is utilized to decouple an RBMDO problem into a sequential of deterministic MDO and reliability evaluation problems. The formula of MDO with SSRA within the framework of SORA is proposed to solve a design optimization problem of a hydraulic transmission mechanism.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2015;137(5):051403-051403-10. doi:10.1115/1.4029768.

In designing microstructural materials systems, one of the key research questions is how to represent the microstructural design space quantitatively using a descriptor set that is sufficient yet small enough to be tractable. Existing approaches describe complex microstructures either using a small set of descriptors that lack sufficient level of details, or using generic high order microstructure functions of infinite dimensionality without explicit physical meanings. We propose a new machine learning-based method for identifying the key microstructure descriptors from vast candidates as potential microstructural design variables. With a large number of candidate microstructure descriptors collected from literature covering a wide range of microstructural material systems, a four-step machine learning-based method is developed to eliminate redundant microstructure descriptors via image analyses, to identify key microstructure descriptors based on structure–property data, and to determine the microstructure design variables. The training criteria of the supervised learning process include both microstructure correlation functions and material properties. The proposed methodology effectively reduces the infinite dimension of the microstructure design space to a small set of descriptors without a significant information loss. The benefits are demonstrated by an example of polymer nanocomposites optimization. We compare designs using key microstructure descriptors versus using empirically chosen microstructure descriptors as a demonstration of the proposed method.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2015;137(5):051404-051404-14. doi:10.1115/1.4029704.

In order to be practical, solutions of engineering design optimization problems must be robust, i.e., competent and reliable in the face of uncertainties. While such uncertainties can emerge from a number of sources (imprecise variable values, errors in performance estimates, varying environmental conditions, etc.), this study focuses on problems where uncertainties emanate from the design variables. While approaches to identify robust optimal solutions of single and multi-objective optimization problems have been proposed in the past, we introduce a practical approach that is capable of solving robust optimization problems involving many objectives building on authors’ previous work. Two formulations of robustness have been considered in this paper, (a) feasibility robustness (FR), i.e., robustness against design failure and (b) feasibility and performance robustness (FPR), i.e., robustness against design failure and variation in performance. In order to solve such formulations, a decomposition based evolutionary algorithm (DBEA) relying on a generational model is proposed in this study. The algorithm is capable of identifying a set of uniformly distributed nondominated solutions with different sigma levels (feasibility and performance) simultaneously in a single run. Computational benefits offered by using polynomial chaos (PC) in conjunction with Latin hypercube sampling (LHS) for estimating expected mean and variance of the objective/constraint functions has also been studied in this paper. Last, the idea of redesign for robustness has been explored, wherein selective component(s) of an existing design are altered to improve its robustness. The performance of the strategies have been illustrated using two practical design optimization problems, namely, vehicle crash-worthiness optimization problem (VCOP) and a general aviation aircraft (GAA) product family design problem.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2015;137(5):051405-051405-10. doi:10.1115/1.4029767.

Game-theoretic models have been used to analyze design problems ranging from multi-objective design optimization to decentralized design and from design for market systems (DFMS) to policy design. However, existing studies are primarily analytical in nature, which start with a number of assumptions about the individual decisions, the information available to the players, and the solution concept (generally, the Nash equilibrium). There is a lack of studies related to engineering design, which rigorously evaluate the validity of these assumptions or that of the predictions from the models. Hence, the usefulness of these models to realistic engineering systems design has been severely limited. In this paper, we take a step toward addressing this gap. Using an example of crowdsourcing for engineering design, we illustrate how the analytical game-theoretic models and behavioral experimentation can be synergistically used to gain a better understanding of design situations. Analytical models describe what players with assumed behaviors and cognitive capabilities would do under specified conditions, and the behavioral experiments shed light on how individuals actually behave. The paper contributes to the design literature in multiple ways. First, to the best of our knowledge, it is a first attempt at integrated theoretical and experimental game-theoretic analysis in design. We illustrate how the analytical models can be used to design behavioral experiments, which, in turn, can be used to estimate parameters, refine models, and inform further development of the theory. Second, we present a simple experiment to understand behaviors of individuals in a design crowdsourcing problem. The results of the experiment show new insights on using crowdsourcing contests for design.

Commentary by Dr. Valentin Fuster

Research Papers: Power Transmissions and Gearing

J. Mech. Des. 2015;137(5):052601-052601-11. doi:10.1115/1.4029586.

To double-crown an involute helical gear, a hobbing method is proposed by setting the hob's diagonal feed motion as a second-order function of hob's traverse movement and modifying the tooth profile of hob cutter into a dual-lead form with pressure angle changed in its longitudinal direction. Merits of the proposed double-crowning method are also verified by using three computer simulation examples to illustrate and compare the topographies of tooth flanks, contact ellipses, and transmission errors under various assembly errors of the double-crowned gear pairs with those produced by using the conventional modified hob cutter and dual-lead hob cutter. Computer simulation results reveal the advantages of the proposed hobbing method for involute helical gear manufacturing.

Commentary by Dr. Valentin Fuster

Technical Brief

J. Mech. Des. 2015;137(5):054501-054501-4. doi:10.1115/1.4029587.

This paper investigates dimensional optimization of a 2-UPR-RPU parallel manipulator (where U is a universal joint, P a prismatic pair, and R a revolute pair). First, the kinematics and screws of the mechanism are analyzed. Then, three indices developed from motion/force transmission are proposed to evaluate the performance of the 2-UPR-RPU parallel manipulator. Based on the performance atlases obtained, a set of optimal parameters are selected from the optimum region within the parameter design space. Finally, the optimized parameters are determined for practical applications.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2015;137(5):054502-054502-3. doi:10.1115/1.4029582.

In this study, a new antibacklash gear mechanism design comprising three pinions and a rack is introduced. This mechanism offers several advantages compared to conventional antibacklash mechanisms, such as lower transmission error as well as lower required preload. Nonlinear dynamic modeling of this mechanism is developed to acquire insight into its dynamic behavior. It is observed that the amount of preload required to diminish the backlash depends on the applied input torque and nature of periodic mesh stiffness. Then, an attempt is made to obtain an approximate relation to find the minimum requiring preload to preserve the system’s antibacklash property and reduce friction and wear on the gear teeth. The mesh stiffness of the mated gears, rack, and pinion is achieved via finite element method. Assuming that all teeth are rigid and static transmission error is negligible, dynamic transmission error (DTE) would be zero for every input torque, which is a unique trait, not yet proposed in previous research.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2015;137(5):054503-054503-3. doi:10.1115/1.4029807.

In this study, a harvesting device embedded into a suspended backpack was developed to harness part of a human's biomechanical energy and reduce dynamic force of the backpack on the carrier. The harvester utilized a spring mass damping system to translate the human body's vertical movement during walking into the rotation of a gear train, which then drives rotary generators to produce electricity. A prototype was built to examine the theoretical study, which showed that the experimental tests agreed with the simulation. Compared with previous work, the harvester in this work had a 40% higher harvesting energy efficiency.

Commentary by Dr. Valentin Fuster

Design Innovation Paper

J. Mech. Des. 2015;137(5):055001-055001-8. doi:10.1115/1.4028704.

Bidirectional evolutionary structural optimization (BESO) method has been successfully applied for a wide range of topology optimization problems. In this paper, the BESO method is further extended to the optimal design of an automotive tailor-welded blank (TWB) door with multiple thicknesses. Different from the traditional topology optimization for solid-void designs, topology optimization of the TWB door needs to identify the weld lines which joint sheets with different thicknesses. The finite element (FE) model of the automotive door assembly is established and verified by a series of stiffness experiments. Then, the proposed optimization procedure is applied to the optimization of the automotive TWB indoor panel for the optimal thickness layout and weld lines locations. Numerical results give guidelines for the lightweight design of TWB components to some extent.

Commentary by Dr. Valentin Fuster

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