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Research Papers: Design Theory and Methodology

J. Mech. Des. 2017;139(5):051101-051101-16. doi:10.1115/1.4036131.

Using electroencephalography (EEG) to predict design outcomes could be used in many applications as it facilitates the correlation of engagement and cognitive workload with ideation effectiveness. It also establishes a basis for the connection between EEG measurements and common constructs in engineering design research. In this paper, we propose a support vector machine (SVM)-based prediction model for design outcomes using EEG metrics and some demographic factors as predictors. We trained and validated the model with more than 100 concepts, and then evaluated the relationship between EEG data and concept-level measures of novelty, quality, and elaboration. The results characterize the combination of engagement and workload that is correlated with good design outcomes. Findings also suggest that EEG technologies can be used to partially replace or augment traditional ideation metrics and to improve the efficacy of ideation research.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2017;139(5):051102-051102-10. doi:10.1115/1.4036134.

This paper shows how to measure the intrinsic complexity and dimensionality of a design space. It assumes that high-dimensional design parameters actually lie in a much lower-dimensional space that represents semantic attributes—a design manifold. Past work has shown how to embed designs using techniques like autoencoders; in contrast, the method proposed in this paper first captures the inherent properties of a design space and then chooses appropriate embeddings based on the captured properties. We demonstrate this with both synthetic shapes of controllable complexity (using a generalization of the ellipse called the superformula) and real-world designs (glassware and airfoils). We evaluate multiple embeddings by measuring shape reconstruction error, pairwise distance preservation, and captured semantic attributes. By generating fundamental knowledge about the inherent complexity of a design space and how designs differ from one another, our approach allows us to improve design optimization, consumer preference learning, geometric modeling, and other design applications that rely on navigating complex design spaces. Ultimately, this deepens our understanding of design complexity in general.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2017;139(5):051103-051103-16. doi:10.1115/1.4036129.

Maps are visual design representations used by engineers to model the information behind a design. This paper evaluates the application of mapping methods supported by the Decision Rationale editor (DRed) in aerospace engineering industry. Specifically, the research investigates what DRed mapping methods are used, where engineers find them useful and why. DRed was selected because it has been formally embedded in the design processes of the partner company and all engineering staff have access to it. The tool was investigated using semistructured interviews with 14 engineers, each already trained with DRed through their work and representing diverse departments and experience levels. Nineteen use cases were collected, ranging from high-profile, multistakeholder projects to everyday individual work. Collected cases were analyzed for the methods applied, common contexts of use, and reasons for use. The results validate baseline DRed mapping methods to capture design rationale and analyze the root causes of engineering problems. Further, it provides empirical evidence for new DRed mapping methods to manage requirements, analyze functional interactions in complex systems and manage personal information. The contexts where mapping methods are most used involve: system-level information that cuts across subsystem boundaries; irregular intervals between map applications; dealing with loosely structured information; individual use or small team collaborations; and addressing on-going problems. The reasons stated by engineers for using maps focus on engineering design thinking, communication, and planning support. Using empirical evidence of its recurrent use, this research establishes that DRed is a powerful and versatile tool for engineers in industry and its mapping methods aid important and otherwise unsupported work. The range and impact of the use cases found in practice suggest that engineers need better support for work with loosely structured information. Organizations involved in the design of complex systems should make greater use of semiformal, graph-based visual tools like DRed. The understanding of mapping software gained through this research demonstrates a shift in emphasis from the enrichment of the engineering record to the provision of immediate cognitive benefits for engineers. The results also support an incremental, adaptive approach for deploying this emerging class of tools in other organizations.

Commentary by Dr. Valentin Fuster

Research Papers: Design Automation

J. Mech. Des. 2017;139(5):051401-051401-11. doi:10.1115/1.4035861.

A flexure strip has constraint characteristics, such as stiffness properties and error motions, that govern its performance as a basic constituent of flexure mechanisms. This paper presents a new modeling approach for obtaining insight into the deformation and stiffness characteristics of general three-dimensional flexure strips that exhibit bending, shear, and torsion deformation. The approach is based on the use of a discretized version of a finite (i.e., nonlinear) strain spatial beam formulation for extracting analytical expressions that describe deformation and stiffness characteristics of a flexure strip in a parametric format. This particular way of closed-form modeling exploits the inherent finite-element assumptions on interpolation and also lends itself for numeric implementation. As a validating case study, a closed-form parametric expression is derived for the lateral support stiffness of a flexure strip and a parallelogram flexure mechanism. This captures a combined torsion–bending dictated geometrically nonlinear effect that undermines the support bearing stiffness when the mechanism moves in the intended degree of freedom (DoF). The analytical result is verified by simulations and experimental measurements.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2017;139(5):051402-051402-11. doi:10.1115/1.4036130.

The focus of this paper is a strategy for making a prediction at a point where a function cannot be evaluated. The key idea is to take advantage of the fact that prediction is needed at one point and not in the entire domain. This paper explores the possibility of predicting a multidimensional function using multiple one-dimensional lines converging on the inaccessible point. The multidimensional approximation is thus transformed into several one-dimensional approximations, which provide multiple estimates at the inaccessible point. The Kriging model is adopted in this paper for the one-dimensional approximation, estimating not only the function value but also the uncertainty of the estimate at the inaccessible point. Bayesian inference is then used to combine multiple predictions along lines. We evaluated the numerical performance of the proposed approach using eight-dimensional and 100-dimensional functions in order to illustrate the usefulness of the method for mitigating the curse of dimensionality in surrogate-based predictions. Finally, we applied the method of converging lines to approximate a two-dimensional drag coefficient function. The method of converging lines proved to be more accurate, robust, and reliable than a multidimensional Kriging surrogate for single-point prediction.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2017;139(5):051403-051403-13. doi:10.1115/1.4036132.

In this article, a class of architecture design problems is explored with perfect matchings (PMs). A perfect matching in a graph is a set of edges such that every vertex is present in exactly one edge. The perfect matching approach has many desirable properties such as complete design space coverage. Improving on the pure perfect matching approach, a tree search algorithm is developed that more efficiently covers the same design space. The effect of specific network structure constraints (NSCs) and colored graph isomorphisms on the desired design space is demonstrated. This is accomplished by determining all unique feasible graphs for a select number of architecture problems, explicitly demonstrating the specific challenges of architecture design. With this methodology, it is possible to enumerate all possible architectures for moderate scale-systems, providing both a viable solution technique for certain problems and a rich data set for the development of more capable generative methods and other design studies.

Commentary by Dr. Valentin Fuster

Research Papers: Design for Manufacture and the Life Cycle

J. Mech. Des. 2017;139(5):051701-051701-13. doi:10.1115/1.4036135.

Assembly variation should be predicted accurately in the design process of a product to ensure the performance of the assembled parts. One important issue in predicting assembly variation is to search propagation paths along which variation accumulates. In this paper, a new searching algorithm of multibranch propagation paths of assembly variation for rigid body assemblies is proposed. First, the concepts of feature set and relation set are proposed to express the information of geometric tolerances and assembly constraints among features. Second, the actual constraint directions of a reference relation considering the precedence level are obtained. Third, the search of multibranch propagation paths is conducted by intersecting the actual constraint directions of different reference relations. Finally, the accuracy and efficiency of the proposed method are validated by comparing with the commercial computer-aided tolerancing (CAT) software package, 3DCS, for predicting assembly variation of the body structure of an aircraft. The outcomes of the paper can treat geometric tolerances, which overcome the drawback of traditional dimension-chain-based methods in predicting assembly variation. It is expected that a synthetic use of the proposed method and the dimension-chain-based methods can provide a computationally efficient substitute for the classical Monte Carlo simulation in predicting assembly variation.

Commentary by Dr. Valentin Fuster

Research Papers: Design Education

J. Mech. Des. 2017;139(5):052001-052001-9. doi:10.1115/1.4036128.

Most definitions of engineering give machines and mechanical objects a central role. Engineers are makers and users of mechanical objects in their environment. Research supports the notion that interactions with engineered artifacts enhance engineering learning. This study introduces a task simulating a real-world engineering application and uses this task to examine how aptitudes, interests, and direct manipulation of mechanical objects influence performance. We hypothesized that engineering students would generate better assembly instructions when they had the box of component parts (BOP) than when they had the engineering drawing only. We also hypothesized that student's mechanical aptitude (MA) and interests in things each would interact with experimental condition's impact on performance. First-year engineering students (N = 383) created assembly instructions in a mixed experimental and correlational design. A random half was assigned to create instructions with a drawing only, whereas the other half created with both a drawing and a box of component parts present. Assembly instructions were evaluated by professional engineers blind to experimental conditions. They rated instructions from the BOP group as superior to those coming from the control group. Students with greater mechanical aptitude received better evaluations, but there was no evidence the experimental variable was moderated either by mechanical aptitude or by thing orientation (TO). This study suggests that mechanical objects can enhance engineering instruction, especially when they are aligned with professional practice.

Commentary by Dr. Valentin Fuster

Research Papers: Design of Energy, Fluid, and Power Handing Systems

J. Mech. Des. 2017;139(5):053401-053401-13. doi:10.1115/1.4036133.

Minimizing energy loss and improving system load capacity and compactness are important objectives for fluid power systems. Recent studies reveal that microtextured surfaces can reduce friction in full-film lubrication, and that asymmetric textures can reduce friction and increase normal force simultaneously. As an extension of these previous discoveries, we explore how enhanced texture design can maximize these objectives together. We design surface texture using a set of distinct parameterizations, ranging from simple to complex, to improve performance beyond what is possible for previously investigated texture geometries. Here, we consider a rotational tribo-rheometer configuration with a fixed textured bottom disk and a rotating top flat disk with controlled separation gap. To model Newtonian fluid flow, the Reynolds equation is formulated in cylindrical coordinates and solved using a pseudospectral method. Model assumptions include incompressibility, steady flow, constant viscosity, and a small gap height to disk radius ratio. Multi-objective optimization problems are solved using the epsilon-constraint method along with an interior-point (IP) nonlinear programming algorithm. The trade-off between competing objectives is quantified, revealing mechanisms of performance enhancement. Various geometries are explored and optimized, including symmetric and asymmetric circular dimples, and novel arbitrary continuous texture geometries represented using two-dimensional cubic spline interpolation. Shifting from simple dimpled textures to more general texture geometries resulted in significant simultaneous improvement in both performance metrics for full-film lubrication texture design. An important qualitative result is that textures resembling a spiral blade tend to improve performance for rotating contacts.

Commentary by Dr. Valentin Fuster

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