Review Article

J. Mech. Des. 2017;139(7):070801-070801-18. doi:10.1115/1.4036352.

This article provides an overview of the operational strategies adopted in microgrippers design. The review covers microgrippers recently proposed in Literature, some of which have been systematically presented in a companion paper, where their topological, kinematic, and structural characteristics are discussed. In the present contribution, the prevalent actuation methods and the operational aspects are discussed: the tip displacement, the tip force, the actuation voltage, and the amplification factor are the reference parameters that are adopted to compare the different types of actuation and operational strategies. In addition, the control strategies and control algorithms currently adopted are reviewed.

Commentary by Dr. Valentin Fuster

Research Papers: Design Theory and Methodology

J. Mech. Des. 2017;139(7):071101-071101-13. doi:10.1115/1.4036567.

Current approaches in computational design synthesis (CDS) enable the human designer to explore large solution spaces for engineering design problems. To extend this to support designers in embodiment and detail design, not only the generation of solution spaces is needed but also the automated evaluation of engineering performance. Here, simulation methods can be used effectively to predict the behavior of a product. This paper builds on a general approach to automatically generate solution spaces for energy and signal-based engineering design tasks using first-order logic and Boolean satisfiability. The generated concept model graphs (CMGs) are now in this paper automatically transformed into corresponding bond-graph-based simulation models. To do this, guidelines for creating partial simulation models for the available synthesis building blocks are presented. The guidelines ensure valid causality in the final simulation model. Considering the connections in the concept model graphs, the simulation models are automatically generated and simulated. The simulation results are then used to calculate different objectives, constraints, and performance metrics. The method is validated using automotive powertrains as a case study. One hundred and sixty-two different powertrain concepts are generated and evaluated, showing the advantages of electric powertrains with respect to CO2 emissions and the importance of considering intelligent control strategies in the future for hybrid ones.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2017;139(7):071102-071102-10. doi:10.1115/1.4036562.

The term “design fixation” refers to a phenomenon where designers unknowingly limit the space within which they search for solutions. In an attempt to study this phenomenon experimentally, researchers typically set participants open-ended design problems, prime them with an example solution, and measure their performance through a variety of subjective metrics. This approach gives rise to various problems, including limited data capture and highly subjective evaluation of design behavior. To address these problems, we studied design fixation with a computer-based task inspired by psychological paradigms used to study “mental set” (also known as the “Einstellung effect”). The task consisted of a gamelike activity requiring participants to design a bridge within a specified budget. The use of a digital environment facilitated continuous data capture during the design activities. The constrained task (and direct quantitative measures) permitted a more objective analysis of design performance, including the occurrence of fixation. The results showed that participants who developed a mental set during the task failed to find alternative, more efficient solutions in trials admitting multiple solutions, compared to the participants who did not fall victim to this mental block. In addition, during the process of designing, the occurrence of mental set resulted in participants adopting a less efficient design behavior and reporting a different subjective experience of the task. The method used and the results obtained show an exciting alternative for studying design fixation experimentally and promote a wider exploration of the variety of design activities in which fixation might occur.

Commentary by Dr. Valentin Fuster

Research Papers: Design Automation

J. Mech. Des. 2017;139(7):071401-071401-12. doi:10.1115/1.4036582.

Quasi-random nanostructures are playing an increasingly important role in developing advanced material systems with various functionalities. Current development of functional quasi-random nanostructured material systems (NMSs) mainly follows a sequential strategy without considering the fabrication conditions in nanostructure optimization, which limits the feasibility of the optimized design for large-scale, parallel nanomanufacturing using bottom-up processes. We propose a novel design methodology for designing isotropic quasi-random NMSs that employs spectral density function (SDF) to concurrently optimize the nanostructure and design the corresponding nanomanufacturing conditions of a bottom-up process. Alternative to the well-known correlation functions for characterizing the structural correlation of NMSs, the SDF provides a convenient and informative design representation that maps processing–structure relation to enable fast explorations of optimal fabricable nanostructures and to exploit the stochastic nature of manufacturing processes. In this paper, we first introduce the SDF as a nondeterministic design representation for quasi-random NMSs, as an alternative to the two-point correlation function. Efficient reconstruction methods for quasi-random NMSs are developed for handling different morphologies, such as the channel-type and particle-type, in simulation-based microstructural design. The SDF-based computational design methodology is illustrated by the optimization of quasi-random light-trapping nanostructures in thin-film solar cells for both channel-type and particle-type NMSs. Finally, the concurrent design strategy is employed to optimize the quasi-random light-trapping structure manufactured via scalable wrinkle nanolithography process.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2017;139(7):071402-071402-9. doi:10.1115/1.4036568.

In reliability-based design optimization (RBDO), dependent input random variables and varying standard deviation (STD) should be considered to correctly describe input distribution model. The input dependency and varying STD significantly affect sensitivity for the most probable target point (MPTP) search and design sensitivity of probabilistic constraint in sensitivity-based RBDO. Hence, accurate sensitivities are necessary for efficient and effective process of MPTP search and RBDO. In this paper, it is assumed that dependency of input random variable is limited to the bivariate statistical correlation, and the correlation is considered using bivariate copulas. In addition, the varying STD is considered as a function of input mean value. The transformation between physical X-space and independent standard normal U-space for correlated input variable is presented using bivariate copula and marginal probability distribution. Using the transformation and the varying STD function, the sensitivity for the MPTP search and design sensitivity of probabilistic constraint are derived analytically. Using a mathematical example, the accuracy and efficiency of the developed sensitivities are verified. The RBDO result for the mathematical example indicates that the developed methods provide accurate sensitivities in the optimization process. In addition, a 14D engineering example is tested to verify the practicality and scalability of the developed sensitivity methods.

Topics: Design
Commentary by Dr. Valentin Fuster
J. Mech. Des. 2017;139(7):071403-071403-16. doi:10.1115/1.4036561.

Multicell tubal structures have generated increasing interest in engineering design for their excellent energy-absorbing characteristics when crushed through severe plastic deformation. To make more efficient use of the material, topology optimization was introduced to design multicell tubes under normal crushing. The design problem was formulated to maximize the energy absorption while constraining the structural mass. In this research, the presence or absence of inner walls were taken as design variables. To deal with such a highly nonlinear problem, a heuristic design methodology was proposed based on a modified artificial bee colony (ABC) algorithm, in which a constraint-driven mechanism was introduced to determine adjacent food sources for scout bees and neighborhood sources for employed and onlooker bees. The fitness function was customized according to the violation or the satisfaction of the constraints. This modified ABC algorithm was first verified by a square tube with seven design variables and then applied to four other examples with more design variables. The results demonstrated that the proposed heuristic algorithm is capable of handling the topology optimization of multicell tubes under out-of-plane crushing. They also confirmed that the optimized topological designs tend to allocate the material at the corners and around the outer walls. Moreover, the modified ABC algorithm was found to perform better than a genetic algorithm (GA) and traditional ABC in terms of best, worst, and average designs and the probability of obtaining the true optimal topological configuration.

Commentary by Dr. Valentin Fuster
J. Mech. Des. 2017;139(7):071404-071404-11. doi:10.1115/1.4036649.

Integrated Computational Materials Engineering (ICME) aims to accelerate optimal design of complex material systems by integrating material science and design automation. For tractable ICME, it is required that (1) a structural feature space be identified to allow reconstruction of new designs, and (2) the reconstruction process be property-preserving. The majority of existing structural presentation schemes relies on the designer's understanding of specific material systems to identify geometric and statistical features, which could be biased and insufficient for reconstructing physically meaningful microstructures of complex material systems. In this paper, we develop a feature learning mechanism based on convolutional deep belief network (CDBN) to automate a two-way conversion between microstructures and their lower-dimensional feature representations, and to achieve a 1000-fold dimension reduction from the microstructure space. The proposed model is applied to a wide spectrum of heterogeneous material systems with distinct microstructural features including Ti–6Al–4V alloy, Pb63–Sn37 alloy, Fontainebleau sandstone, and spherical colloids, to produce material reconstructions that are close to the original samples with respect to two-point correlation functions and mean critical fracture strength. This capability is not achieved by existing synthesis methods that rely on the Markovian assumption of material microstructures.

Commentary by Dr. Valentin Fuster

Research Papers: Design of Mechanisms and Robotic Systems

J. Mech. Des. 2017;139(7):072301-072301-12. doi:10.1115/1.4036648.

This paper presents a novel family of modular flat-foldable rigid plate structures composed by assemblies of 4R-linkages. First, in the field of foldable plates, the proposed system is characterized by being not only foldable but also transformable: the slope of one module over the other is capable of changing not only magnitude but also sign. This transformable behavior extends the range of application of foldable plates from simply larger–smaller configurations to substantially different configurations and usages. The transformable curve is obtained by means of symmetry operations on the spherical length of links. For each module, three configurations can be designed. Various examples are illustrated.

Commentary by Dr. Valentin Fuster

Technical Brief

J. Mech. Des. 2017;139(7):074501-074501-6. doi:10.1115/1.4036644.

Commonality or the use of same components (parts, assemblies, or subsystems) among multiple products can reduce component inventory and simplify processes and logistics while accommodating variations in product demand. Excessive commonality, however, causes some products to use high-performance components and increase product cost. This paper presents an approach for evaluating profitability of component commonality by integrating commonality and supply chain decisions. The proposed approach is demonstrated using commonality of electric-bicycle motors as an illustrative example. This paper presents a sensitivity analysis of the optimum commonality with respect to motor cost, demand variability, inventory-tracking cost, and inventory-ordering cost.

Commentary by Dr. Valentin Fuster

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