Research Papers

J. Mech. Des. 2014;136(4):041001-041001-13. doi:10.1115/1.4026029.

Competitive wheelchair sport performance is dependent on three factors: the athlete, the wheelchair, and the interaction between the athlete and the wheelchair (Goosey-Tolfrey, 2010, “Supporting the Paralympic Athlete: Focus on Wheeled Sports,” Disabil Rehabil., 32(26), pp. 2237–2243). In order to effectively refine the user interphase design of the wheelchair, it is essential to narrow down the key dimensions within the design space, which are likely to have an effect on the performance of an individual athlete. This paper provides a case study analysis of the test data obtained from five elite wheelchair rugby athletes, using a purpose-built adjustable wheelchair on a wheelchair ergometer. Four design factors (wheel diameter, camber angle, seat height, and camber bar depth) were tested at incremental dimensional levels to the athlete's current chair configuration; and tests were performed according to an L9 Taguchi orthogonal array. The case study analyzes acceleration, velocity, and time in the push phase of the propulsion cycle; as well as recovery time for each of the participating athletes performing a linear sprint task. The Taguchi method is applied to determining the positive/negative contribution of each of the four design factors to the outlined performance variables as well as their combined effect in a specific wheelchair configuration model. A performance ranking system and magnitude-based inferences on the true value of the effect statistic are used to define a high performance design space for individual athlete wheelchairs. Finally, the athlete's preferred ergonomics are considered to assess the narrowed high performance wheelchair options. As such, when adopting the approach presented in this paper, it becomes possible to customize an athlete's wheelchair design to meet the athlete's anthropometric needs as well as their performance requirements.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2014;136(4):041002-041002-15. doi:10.1115/1.4026032.

Reconfigurable and multifunctional products are breeds of products that cater to the increased diversification of customer needs. Unlike single-state static products which can perform only one primary function, these products cater to different customer needs by performing more than one function with or without changing their configuration. However, there is a lack of systematic methods to support the conceptual task of combining two existing single-state products into an integrated product that provides multiple functions. In this work, a function based approach is proposed which provides more rigorous support to assess the feasibility of integrating two products. The function structures of the existing products are combined to obtain the overall function structure of the reconfigurable product. Function sharing, based on quantified functional similarity, is proposed and applied to identify functions that can be shared by the same component. The information obtained from the function structure is then mapped to the components of two existing products to analyze their roles in the final reconfigurable product architecture. A case study illustrates the proposed approach by analyzing the integration of a power drill and a dust buster.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2014;136(4):041003-041003-14. doi:10.1115/1.4026281.

In this paper, a value-based global optimization (VGO) algorithm is introduced. The algorithm uses kriging-like surrogate models and a sequential sampling strategy based on value of information (VoI) to optimize an objective characterized by multiple analysis models with different accuracies. VGO builds on two main contributions. The first contribution is a novel surrogate modeling method that accommodates data from any number of different analysis models with varying accuracy and cost. Rather than interpolating, it fits a model to the data, giving more weight to more accurate data. The second contribution is the use of VoI as a new metric for guiding the sequential sampling process for global optimization. Based on information about the cost and accuracy of each available model, predictions from the current surrogate model are used to determine where to sample next and with what level of accuracy. The cost of further analysis is explicitly taken into account during the optimization process, and no further analysis occurs if the expected value of the new information is negative. In this paper, we present the details of the VGO algorithm and, using a suite of randomly generated test cases, compare its performance with the performance of the efficient global optimization (EGO) algorithm (Jones, D. R., Matthias, S., and Welch, W. J., 1998, “Efficient Global Optimization of Expensive Black-Box Functions,” J. Global Optim., 13(4), pp. 455–492). Results indicate that the VGO algorithm performs better than EGO in terms of overall expected utility—on average, the same quality solution is achieved at a lower cost, or a better solution is achieved at the same cost.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2014;136(4):041004-041004-10. doi:10.1115/1.4026151.

Although design novelty is a critical area of research in engineering design, most research in this space has focused on understanding and developing formal idea generation methods instead of focusing on the impact of current design practices. This is problematic because formal techniques are often not adopted in industry due to the burdensome steps often included in these methods, which limit the practicality and adoption of these methods. This study seeks to understand the impact of product dissection, a design method widely utilized in academia and industry, on design novelty in order to produce recommendations for the use or alterations of this method for supporting novelty in design. To investigate the impact of dissection, a study was conducted with 76 engineering students who completed a team-based dissection of an electric toothbrush and then individually generated ideas. The relationships between involvement in the dissection activity, the product dissected, the novelty and quantity of the ideas developed were investigated. The results reveal that team members who were more involved in the dissection activity generated concepts that were more novel than those who did not. In addition, the type of the dissected product also had an influence on design novelty. Finally, a positive correlation between the number of ideas generated and the novelty of the design concepts was identified. The results from this study are used to provide recommendations for leveraging product dissection for enhancing novelty in engineering design education and practice.

Topics: Design , Teams
Commentary by Dr. Valentin Fuster
J. Mech. Des. 2014;136(4):041005-041005-8. doi:10.1115/1.4026463.

Disassembly sequence planning at the early conceptual stage of design leads to enormous benefits including simplification of products, lower assembly and disassembly costs, and design modifications which result in increased potential profitability of end-of-life salvaging operations. However, in the early design stage, determining the best disassembly sequence is challenging. First, the required information is not readily available and very time-consuming to gather. In addition, the best solution is sometimes counterintuitive, even to those with experience and expertise in disassembly procedures. Integrating analytical models with immersive computing technology (ICT) can help designers overcome these issues. A two-stage procedure for doing so is introduced in this paper. In the first stage, a stochastic programming model together with the information obtained through immersive simulation is applied to determine the optimal disassembly sequence, while considering uncertain outcomes, such as time, cost, and the probability of causing damage. In the second stage, ICT is applied as a tool to explore alternative disassembly sequence solutions in an intuitive way. The benefit of using this procedure is to determine the best disassembly sequence, not only by solving the analytic model but also by capturing human expertise. The designer can apply the obtained results from these two stages to analyze and modify the product design. An example of a Burr puzzle is used to illustrate the application of the method.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2014;136(4):041006-041006-12. doi:10.1115/1.4026572.

A dynamic model that includes friction and tooth profile error excitation for herringbone gears is proposed for the dynamic analysis of variable speed processes. In this model, the position of the contact line and relative sliding velocity are determined by the angular displacement of the gear pair. The translational and angular displacements are chosen as generalized coordinates to construct the dynamic model. The friction is calculated using a variable friction coefficient. The tooth profile error excitation is assumed to depend on the position along the contact line and to vary with the angular displacement of the driving gear. Thus, the proposed model can be used in the dynamic analysis of the variable speed process of a herringbone gear transmission system. An example acceleration process is numerically simulated using the model proposed in this paper. The dynamics responses are compared with those from the model utilizing a constant friction coefficient and without friction in cases where the profile error excitations are included and ignored.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2014;136(4):041007-041007-10. doi:10.1115/1.4026647.

Key parameters may be used to turn a bad design into a good design with comparatively little effort. The proposed method identifies key parameters in high-dimensional nonlinear systems that are subject to uncertainty. A numerical optimization algorithm seeks a solution space on which all designs are good, that is, they satisfy a specified design criterion. The solution space is box-shaped and provides target intervals for each parameter. A bad design may be turned into a good design by moving its key parameters into their target intervals. The solution space is computed so as to minimize the effort for design work: its shape is controlled by particular constraints such that it can be reached by changing only a small number of key parameters. Wide target intervals provide tolerance against uncertainty, which is naturally present in a design process, when design parameters are unknown or cannot be controlled exactly. In a simple two-dimensional example problem, the accuracy of the algorithm is demonstrated. In a high-dimensional vehicle crash design problem, an underperforming vehicle front structure is improved by identifying and appropriately changing a relevant key parameter.

Commentary by Dr. Valentin Fuster

Technical Brief

J. Mech. Des. 2014;136(4):044501-044501-9. doi:10.1115/1.4026263.

This paper explores the simulation-based design optimization of a variable geometry spray (VGS) fuel injector. A multi-objective genetic algorithm (MOGA) is interfaced with commercial computational fluid dynamics (CFD) software and high performance computing capabilities to evaluate the spray characteristics of each VGS candidate design. A three-point full factorial experimental design is conducted to identify significant design variables and to better understand possible variable interactions. The Pareto frontier of optimal designs reveals the inherent tradeoff between two performance objectives—actuator stroke and spray angle sensitivity. Analysis of these solutions provides insight into dependencies between design parameters and the performance objectives and is used to assess possible performance gains with respect to initial prototype configurations. These insights provide valuable design information for the continued development of this VGS technology.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2014;136(4):044502-044502-8. doi:10.1115/1.4026147.

The objective of this work is to analytically study the nonlinear dynamics of beam flexures with a tip mass undergoing large deflections. Hamilton's principle is utilized to derive the equations governing the nonlinear vibrations of the cantilever beam and the associated boundary conditions. Then, using a single mode approximation, these nonlinear partial differential equations are reduced to two coupled nonlinear ordinary differential equations. These equations are solved analytically using the multiple time scales perturbation technique. Parametric analytical expressions are presented for the time domain response of the beam around and far from its internal resonance state. These analytical results are compared with numerical ones to validate the accuracy of the proposed analytical model. Compared with numerical solution methods, the proposed analytical technique shortens the computational time, offers design insights, and provides a broader framework for modeling more complex flexure mechanisms. The qualitative and quantitative knowledge resulting from this effort is expected to enable the analysis, optimization, and synthesis of flexure mechanisms for improved dynamic performance.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2014;136(4):044503-044503-4. doi:10.1115/1.4026571.

The space curve meshing wheel (SCMW) is an innovative gear mechanism based on the space curve meshing theory, and the helix curve meshing wheel (HCMW) is the most common SCMW. In this study, we propose the polyhedral helix curve meshing reducer (HCMR), which is based on the HCMW group. The installation dimension chains of the HCMW pairs are described for different included-angle cases and the HCMW group is produced by combining HCMW pairs. Finally, a polyhedral HCMR is designed with a single input shaft and multiple output shafts, with an adjustable gear box, for which a trial version was produced. The polyhedral HCMR has good application prospects because of its compact structure, high design flexibility, and low cost.

Topics: Dimensions , Chain , Design , Gears , Wheels
Commentary by Dr. Valentin Fuster

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