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Research Papers: Design Theory and Methodology

J. Mech. Des. 2014;136(10):101101-101101-11. doi:10.1115/1.4027986.

Past studies have identified the following cognitive skills relevant to conceptual design: divergent thinking, spatial reasoning, visual thinking, abstract reasoning, and problem formulation (PF). Standardized tests are being developed to assess these skills. The tests on divergent thinking and visual thinking are fully developed and validated; this paper focuses on the development of a test of abstract reasoning in the context of engineering design. Similar to the two previous papers, this paper reports on the theoretical and empirical basis for skill identification and test development. Cognitive studies of human problem solving and design thinking revealed four indicators of abstract reasoning: qualitative deductive reasoning (DR), qualitative inductive reasoning (IR), analogical reasoning (AnR), and abductive reasoning (AbR). Each of these is characterized in terms of measurable indicators. The paper presents test construction procedures, trial runs, data collection, norming studies, and test refinement. Initial versions of the test were given to approximately 250 subjects to determine the clarity of the test problems, time allocation and to gauge the difficulty level. A protocol study was also conducted to assess test content validity. The beta version was given to approximately 100 students and the data collected was used for norming studies and test validation. Analysis of test results suggested high internal consistency; factor analysis revealed four eigenvalues above 1.0, indicating assessment of four different subskills by the test (as initially proposed by four indicators). The composite Cronbach’s alpha for all of the factors together was found to be 0.579. Future research will be conducted on criterion validity.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2014;136(10):101102-101102-16. doi:10.1115/1.4028093.

Design-by-analogy is a powerful approach to augment traditional concept generation methods by expanding the set of generated ideas using similarity relationships from solutions to analogous problems. While the concept of design-by-analogy has been known for some time, few actual methods and tools exist to assist designers in systematically seeking and identifying analogies from general data sources, databases, or repositories, such as patent databases. A new method for extracting functional analogies from data sources has been developed to provide this capability, here based on a functional basis rather than form or conflict descriptions. Building on past research, we utilize a functional vector space model (VSM) to quantify analogous similarity of an idea's functionality. We quantitatively evaluate the functional similarity between represented design problems and, in this case, patent descriptions of products. We also develop document parsing algorithms to reduce text descriptions of the data sources down to the key functions, for use in the functional similarity analysis and functional vector space modeling. To do this, we apply Zipf's law on word count order reduction to reduce the words within the documents down to the applicable functionally critical terms, thus providing a mapping process for function based search. The reduction of a document into functional analogous words enables the matching to novel ideas that are functionally similar, which can be customized various ways. This approach thereby provides relevant sources of design-by-analogy inspiration. As a verification of the approach, two original design problem case studies illustrate the distance range of analogical solutions that can be extracted. This range extends from very near-field, literal solutions to far-field cross-domain analogies.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2014;136(10):101103-101103-8. doi:10.1115/1.4028102.

Every year design practitioners and researchers develop new methods for understanding users and solving problems. This increasingly large collection of methods causes a problem for novice designers: How does one choose which design methods to use for a given problem? Experienced designers can provide case studies that document which methods they used, but studying these cases to infer appropriate methods for a novel problem is inefficient. This research addresses that issue by applying techniques from content-based and collaborative filtering to automatically recommend design methods, given a particular problem. Specifically, we demonstrate the quality with which different algorithms recommend 39 design methods out of an 800+ case study dataset. We find that knowing which methods occur frequently together allows one to recommend design methods more effectively than just using the text of the problem description itself. Furthermore, we demonstrate that automatically grouping frequently co-occurring methods using spectral clustering replicates human-provided groupings to 92% accuracy. By leveraging existing case studies, recommendation algorithms can help novice designers efficiently navigate the increasing array of design methods, leading to more effective product design.

Commentary by Dr. Valentin Fuster

Research Papers: Design Automation

J. Mech. Des. 2014;136(10):101401-101401-13. doi:10.1115/1.4027879.

Systems such as electronics, cars, computers, and robots are assembled from modular components for specific applications. Photovoltaic reverse osmosis (PVRO) systems, which can be custom-tailored for the water demands and solar properties of particular communities, are an important potential application of modular systems. Clearly, to be financially viable, such systems must be assembled from commercially available components and subsystems (modules). Designing a system from modular components for a specific application is not simple. Even for a relatively small inventory of modular components, the number of possible system configurations that exist is extremely large. For a small community, determining the best system configuration is an overwhelming task due to lack of expertise. This paper presents a modular design architecture that can be implemented on a laptop so nonexperts can configure systems from modular components. The method uses a hierarchy of filters, which can be provided from an expert system, to limit the large design space. Optimization methods and detailed models are then used to configure the location-specific system from the reduced design space. The method is applied here to community-scale PVRO systems and example cases demonstrate the effectiveness of the approach.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2014;136(10):101402-101402-10. doi:10.1115/1.4027982.

In the second-order reliability method (SORM), the probability of failure is computed for an arbitrary performance function in arbitrarily distributed random variables. This probability is approximated by the probability of failure computed using a general quadratic fit made at the most probable point (MPP). However, an easy-to-use, accurate, and efficient closed-form expression for the probability content of the general quadratic surface in normalized standard variables has not yet been presented. Instead, the most commonly used SORM approaches start with a relatively complicated rotational transformation. Thereafter, the last row and column of the rotationally transformed Hessian are neglected in the computation of the probability. This is equivalent to approximating the probability content of the general quadratic surface by the probability content of a hyperparabola in a rotationally transformed space. The error made by this approximation may introduce unknown inaccuracies. Furthermore, the most commonly used closed-form expressions have one or more of the following drawbacks: They neither do work well for small curvatures at the MPP and/or large number of random variables nor do they work well for negative or strongly uneven curvatures at the MPP. The expressions may even present singularities. The purpose of this work is to present a simple, efficient, and accurate closed-form expression for the probability of failure, which does not neglect any component of the Hessian and does not necessitate the rotational transformation performed in the most common SORM approaches. Furthermore, when applied to industrial examples where quadratic response surfaces of the real performance functions are used, the proposed formulas can be applied directly to compute the probability of failure without locating the MPP, as opposed to the other first-order reliability method (FORM) and the other SORM approaches. The method is based on an asymptotic expansion of the sum of noncentral chi-squared variables taken from the literature. The two most widely used SORM approaches, an empirical SORM formula as well as FORM, are compared to the proposed method with regards to accuracy and computational efficiency. All methods have also been compared when applied to a wide range of hyperparabolic limit-state functions as well as to general quadratic limit-state functions in the rotationally transformed space, in order to quantify the error made by the approximation of the Hessian indicated above. In general, the presented method was the most accurate for almost all studied curvatures and number of random variables.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2014;136(10):101403-101403-11. doi:10.1115/1.4027983.

Although energy consumption during product use can lead to significant environmental impacts, the relationship between a product's usage context and its environmental performance is rarely considered in design evaluations. Traditional analyses rely on broad, average usage conditions and do not differentiate between contexts for which design decisions are highly beneficial and contexts for which the same decision may offer limited benefits or even penalties in terms of environmental performance. In contrast, probabilistic graphical models (PGMs) provide the capability of modeling usage contexts as variable factors. This research demonstrates a method for representing the usage context as a PGM and illustrates it with a lightweight vehicle design example. Factors such as driver behavior, alternative driving schedules, and residential density are connected by conditional probability distributions derived from publicly available data sources. Unique scenarios are then defined as sets of conditions on these factors to provide insight into sources of variability in lifetime energy use. The vehicle example demonstrates that implementation of realistic usage scenarios via a PGM can provide a much higher fidelity investigation of use stage energy savings than commonly found in the literature and that, even in the case of a universally beneficial design decisions, distinct scenarios can have significantly different implications for the effectiveness of lightweight vehicle designs.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2014;136(10):101404-101404-12. doi:10.1115/1.4027985.

Traditionally, consumer preference is modeled in terms of preference for the aesthetic and functional features of a product. This paper introduces a new means to model consumer preference that accounts for not only for how a product looks and functions but also how it feels to interact with it. Traditional conjoint-based approaches to preference modeling require a participant to judge preference for a product based upon a static 2D visual representation or a feature list. While the aesthetic forms and functional features of a product are certainly important, the decision to buy or not to buy a product often depends on more, namely, the experience or feel of use. To address the importance of the product experience, we introduce the concept of experiential conjoint analysis, a method to mathematically capture preference for a product through experience-based (experiential) preference judgments. Experiential preference judgments are made based upon the use, or simulated use, of a product. For many products, creating enough physical prototypes to generate a preference model is cost prohibitive. In this work, virtual reality (VR) technologies are used to allow the participant an interactive virtual product experience, provided at little investment. The results of this work show that providing additional interaction-based (interactional) information about a product through a product experience does not affect the predictive ability of the resulting preference models. This work additionally demonstrates that the preference judgments of virtual product representations are more similar to preference judgments of real products than preference judgments of 2D product representations are. When examining similarity of modeled preference, experiential conjoint is found to be superior to visual conjoint with respect to mean absolute error (MAE), but with respect to correlation no significant difference between visual and experiential is found.

Topics: Preferences , Design
Commentary by Dr. Valentin Fuster
J. Mech. Des. 2014;136(10):101405-101405-8. doi:10.1115/1.4028016.

Quality characteristics (QCs) are important product performance variables that determine customer satisfaction. Their expected values are optimized and their standard deviations are minimized during robust design (RD). Most of RD methodologies consider only a single QC, but a product is often judged by multiple QCs. It is a challenging task to handle dependent and oftentimes conflicting QCs. This work proposes a new robustness modeling measure that uses the maximum quality loss among multiple QCs for problems where the quality loss is the same no matter which QCs or how many QCs are defective. This treatment makes it easy to model RD with multivariate QCs as a single objective optimization problem and also account for the dependence between QCs. The new method is then applied to problems where bivariate QCs are involved. A numerical method for RD with bivariate QCs is developed based on the first order second moment (FOSM) method. The method is applied to the mechanism synthesis of a four-bar linkage and a piston engine design problem.

Commentary by Dr. Valentin Fuster

Research Papers: Design for Manufacturing

J. Mech. Des. 2014;136(10):101701-101701-12. doi:10.1115/1.4027981.

Lean can be applied to product development processes to improve value creation management. However, allocating resources to a project or a development program in order to maximize the value generated by project activities can be difficult in complex product development processes. This paper discusses how value creation activities can be better managed by regulating the resource allocation process. A mathematical model is proposed to describe value growth and its application to resource allocation is demonstrated that gives insight into value creation trajectories. The application is demonstrated with scenarios developed from the computer industry and a design project.

Commentary by Dr. Valentin Fuster

Design Innovation Paper

J. Mech. Des. 2014;136(10):105001-105001-9. doi:10.1115/1.4027782.

This paper presents the mechanical design of a novel surgical robotic platform, specifically developed for single-port laparoscopy (SPL). The greatest constraint is the small size of the skin incision through which the robot must operate. Several technical and technological challenges have been tackled to meet the stringent requirements imposed by the surgical procedure at hand. In this paper, a detailed mechanical description of the system is provided, fulfilling the necessary design requirements. The main outcome of this work is a compact, light-weight (total weight approximately 6 kg) and highly dexterous bimanual robot capable of overcoming the current drawbacks experienced in SPL when using traditional medical devices. The system has been assessed in terms of tracking accuracy, resulting in satisfactory and promising performance.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2014;136(10):105002-105002-10. doi:10.1115/1.4027880.

Puncture access procedures are frequent in medicine but can lead to complications due to over-puncture. When tissue membranes yield under applied stress, the device suddenly accelerates forward into the patient. Factors contributing to greater acceleration and increased risk of over-puncture are identified. A novel flexure-based tip-retraction mechanism is proposed and relevant analysis presented. A preload feature improves functionality and ease of use, and accompanying modified design equations are presented. Prototypes are tested to validate analysis and reliability. The proposed device has the potential to improve safety during puncture access procedures by actively opposing forward acceleration of the device upon break-through thus reducing over-puncture incidents.

Commentary by Dr. Valentin Fuster

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