Research Papers: Design Theory and Methodology

J. Mech. Des. 2019;141(3):031101-031101-13. doi:10.1115/1.4042335.

Though little research has been done in the field of over-design as a product development strategy, an over-design approach can help products avoid the issue of premature obsolescence. This paper compares over-design to redesign as approaches to address the emergence of future requirements. Net present value (NPV) analyses of several real world applications are examined from the perspective of manufacturers (i.e., defense contractors, automobile, pharmaceutical, and microprocessor manufactures) and customers (i.e., purchases of vehicles, televisions, cell phones, washing machines, and buildings). This analysis is used to determine the conditions under which an over-design approach provides a greater benefit than a redesign approach. Over-design is found to have a higher NPV than redesign when future requirements occur soon after the initial release, discount rates are low, initial research, and development cost or price is high, and when the incremental costs of the future requirements are low.

Topics: Design
Commentary by Dr. Valentin Fuster
J. Mech. Des. 2019;141(3):031102-031102-10. doi:10.1115/1.4042341.

Increasing the modularity of system architectures is generally accepted as a good design principle in engineering. In this paper, we explore whether modularity comes at the expense of robustness. To that end, we model three engineering systems as networks and measure the relation between modularity and robustness to random failures. We produced four types of network models of systems—component-component, component-function, component-parameter, and function-parameter—to further test the relation of robustness to the type of system representation, architectural or behavioral. The results show that higher modularity is correlated with lower robustness (p <0.001) and that the estimated modularity of the system can depend on the type of system representation. The implication is that there is a tradeoff between modularity and robustness, meaning that increasing modularity might not be appropriate for systems for which robustness is critical and modularity estimates differ significantly between the types of system representation.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2019;141(3):031103-031103-11. doi:10.1115/1.4042048.

Many tools, techniques, and other interventions have been developed to support idea generation within the design process. In previous research, we explored the separate effects of three such design interventions: teaming, problem framing, and design heuristics. In the teaming intervention, participants discussed a design prompt together but recorded their own ideas separately. In problem framing, multiple versions (framings) of each design prompt were used to elicit different solutions. In design heuristics, participants used specially designed cards to prompt new ways of thinking about the given design problem. In the current work, we compared the effects of these three interventions on students' design ideas with respect to one idea attribute in particular—quality. In total, 1088 design concepts were collected from 171 undergraduate students in engineering and industrial design from two universities. Individual cognitive style was also assessed using Kirton's Adaption–Innovation inventory (KAI). Six metrics taken from the design literature were used to assess the quality of each concept, namely: acceptability, applicability, clarity, effectiveness, implementability, and implicational explicitness. Paired t-tests and Pearson correlations were used to assess differences in quality between concepts generated with and without the three interventions; in addition, secondary effects were sought based on the cognitive styles and academic standings of the participants. Statistically significant differences were observed in design concept quality for the teaming and design heuristics interventions over the full sample and for some subgroups separated by cognitive style and academic standing. These results have implications for how educators teach design interventions and how students choose and apply interventions to affect the quality of their own design solutions.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2019;141(3):031104-031104-9. doi:10.1115/1.4042339.

Building prototypes is an important part of the concept selection phase of the design process, where fuzzy ideas get represented to support communication and decision making. However, the previous studies have shown that prototypes generate different levels of user feedback based on their fidelity and esthetics. Furthermore, prior research on concept selection has shown that individual risk attitude effects how individuals select ideas, as creative ideas are perceived to be riskier in comparison to less creative ideas. While the role of risk has been investigated in concept selection, there is lack of research on how risk is related to the selection of prototypes at various levels of fidelity. Thus, the purpose of this study was to investigate the impact of prototype fidelity, concept creativity, and risk aversion on perceived riskiness and concept selection through a between-subjects study with 72 engineering students. The results revealed that there was a “goldilocks” effect in which students choose concepts with “just the right amount” of novelty, not too much and not too little, as long as quality was adequate. In addition, the prototype fidelity of a concept had an interaction with uniqueness, indicating that unique concepts are more likely to be perceived as less risky if presented at higher levels of fidelity.

Commentary by Dr. Valentin Fuster

Research Papers: Design Automation

J. Mech. Des. 2019;141(3):031401-031401-9. doi:10.1115/1.4042113.

The optimization of black-box models is a challenging task owing to the lack of analytic gradient information and structural information about the underlying function, and also due often to significant run times. A common approach to tackling such problems is the implementation of Bayesian global optimization techniques. However, these techniques often rely on surrogate modeling strategies that endow the approximation of the underlying expensive function with nonexistent features. Further, these techniques tend to push new queries away from previously queried design points, making it difficult to locate an optimum point that rests near a previous model evaluation. To overcome these issues, we propose a gold rush (GR) policy that relies on purely local information to identify the next best design alternative to query. The method employs a surrogate constructed pointwise, that adds no additional features to the approximation. The result is a policy that performs well in comparison to state of the art Bayesian global optimization methods on several benchmark problems. The policy is also demonstrated on a constrained optimization problem using a penalty method.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2019;141(3):031402-031402-14. doi:10.1115/1.4042149.

Accurately predicting the reliability of a physical system under aleatory uncertainty requires a very large number of physical output testing. Alternatively, a simulation-based method can be used, but it would involve epistemic uncertainties due to imperfections in input distribution models, simulation models, and surrogate models, as well as a limited number of output testing due to cost. Thus, the estimated output distributions and their corresponding reliabilities would become uncertain. One way to treat epistemic uncertainty is to use a hierarchical Bayesian approach; however, this could result in an overly conservative reliability by integrating possible candidates of input distribution. In this paper, a new confidence-based reliability assessment method that reduces unnecessary conservativeness is developed. The epistemic uncertainty induced by a limited number of input data is treated by approximating an input distribution model using a bootstrap method. Two engineering examples and one mathematical example are used to demonstrate that the proposed method (1) provides less conservative reliability than the hierarchical Bayesian analysis, yet (2) predicts the reliability of a physical system that satisfies the user-specified target confidence level, and (3) shows convergence behavior of reliability estimation as numbers of input and output test data increase.

Commentary by Dr. Valentin Fuster

Research Papers: Design for Manufacture and the Life Cycle

J. Mech. Des. 2019;141(3):031701-031701-10. doi:10.1115/1.4042189.

The effects of stress gradient and size effect on fatigue life are investigated based on the distribution of stress at the notch root of notched specimens of GH4169 alloy. The relationship between the life of notched specimens and smooth specimens is correlated by introducing the stress gradient impact coefficient, and a new life model of predicting notched specimens based on the Walker modification for the mean stress effect is established. In order to improve the prediction precision of life model with the equation parameters having a definite physical significance, the relationships among fatigue parameters, monotonic ultimate tensile strength, and reduction of area are established. Three-dimensional elastic finite element (FE) analysis of a vortex reducer is carried out to obtain the data of stress and strain for predicting its life. The results show that there is a high-stress gradient at the edge of the air holes of the vortex reducer, and it is thus a dangerous point for fatigue crack initiation. The prediction result of the vortex reducer is more reasonable if the mean stress, the stress gradient, and the size effect are considered comprehensively. The developed life model can reflect the effects of many factors well, especially the stress concentration. The life of notched specimens predicted by this model give a high estimation precision, and the prediction life data mainly fall into the scatter band of factor 2.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2019;141(3):031702-031702-15. doi:10.1115/1.4042211.

In a multireservoir system, ensuring adequate water availability while managing conflicting goals is critical to making the social–ecological system sustainable in the presence of considerable uncertainty. The priorities of multiple user groups and availability of the water resource vary with time, weather, and other factors. Uncertainties such as variations in precipitation can intensify the discrepancies between water supply and water demand. To reduce such discrepancies, we seek to satisfice conflicting goals, considering typical uncertainties. We observe that models are incomplete and inaccurate, which calls into question using a single point solution and suggests the need for solutions, which are robust to uncertainties. So, we explore satisficing solutions that are relatively insensitive to uncertainties, by incorporating different design preferences, identifying sensitive segments, and improving the design accordingly. In this article, we present an example of the exploration of the solution space to enhance sustainability in multidisciplinary systems, when goals conflict, preferences are evolving, and uncertainties add complexity, which can be applied in mechanical design. In this paper, we focus on the method rather than the results.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2019;141(3):031703-031703-12. doi:10.1115/1.4042213.

Advances in three-dimensional (3D) printing are enabling the design and fabrication of tailored lattices with high mechanical efficiency. Here, we focus on conducting experiments to mechanically characterize lattice structures to measure properties that inform an integrated design, manufacturing, and experiment framework. Structures are configured as beam-based lattices intended for use in novel spinal cage devices for bone fusion, fabricated with polyjet printing. Polymer lattices with 50% and 70% porosity were fabricated with beam diameters of 0.41.0mm, with measured effective elastic moduli from 28MPa to 213MPa. Effective elastic moduli decreased with higher lattice porosity, increased with larger beam diameters, and were highest for lattices compressed perpendicular to their original build direction. Cages were designed with 50% and 70% lattice porosities and included central voids for increased nutrient transport, reinforced shells for increased stiffness, or both. Cage stiffnesses ranged from 4.1kN/mm to 9.6kN/mm with yielding after 0.360.48mm displacement, thus suggesting their suitability for typical spinal loads of 1.65kN. The 50% porous cage with reinforced shell and central void was particularly favorable, with an 8.4kN/mm stiffness enabling it to potentially function as a stand-alone spinal cage while retaining a large open void for enhanced nutrient transport. Findings support the future development of fully integrated design approaches for 3D printed structures, demonstrated here with a focus on experimentally investigating lattice structures for developing novel biomedical devices.

Commentary by Dr. Valentin Fuster

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