Design space exploration can reveal the underlying structure of design problems. In a set-based approach, for example, exploration can map sets of designs or regions of the design space that meet specific performance requirements. For some problems, promising designs may cluster in multiple regions of the input design space, and the boundaries of those clusters may be irregularly shaped and difficult to predict. Visualizing the promising regions can clarify the design space structure, but design spaces are typically high-dimensional, making it difficult to visualize the space in three dimensions. To convey the structure of such high-dimensional design regions, a two-stage approach is proposed to (1) identify and (2) visualize each distinct cluster or region of interest in the input design space. This paper focuses on the visualization stage of the approach. Rather than select a singular technique to map high-dimensional design spaces to low-dimensional, visualizable spaces, a selection procedure is investigated. Metrics are available for comparing different visualizations, but the current metrics either overestimate the quality or favor selection of certain visualizations. Therefore, this work introduces and validates a more objective metric, termed preservation , to compare the quality of alternative visualization strategies. Furthermore, a new visualization technique previously unexplored in the design automation community, t-Distributed Neighbor Embedding, is introduced and compared to other visualization strategies. Finally, the new metric and visualization technique are integrated into a two-stage visualization strategy to identify and visualize clusters of high-performance designs for a high-dimensional negative stiffness metamaterials design problem.

One of the challenges in designing for additive manufacturing (AM) is accounting for the differences between as-designed and as-built geometries and material properties. From a designer’s perspective, these differences can lead to degradation of part performance, which is especially difficult to accommodate in small-lot or one-of-a-kind production. In this context, each part is unique, and therefore, extensive iteration is costly. Designers need a means of exploring the design space while simultaneously considering the reliability of additively manufacturing particular candidate designs. In this work, a design exploration approach, based on Bayesian network classifiers (BNC), is extended to incorporate manufacturability explicitly into the design exploration process. The example application is the design of negative stiffness (NS) metamaterials, in which small volume fractions of negative stiffness (NS) inclusions are embedded within a host material. The resulting metamaterial or composite exhibits macroscopic mechanical stiffness and loss properties that exceed those of the base matrix material. The inclusions are fabricated with microstereolithography with features on the scale of tens of microns, but variability is observed in material properties and dimensions from specimen to specimen. In this work, the manufacturing variability of critical features of a NS inclusion fabricated via microstereolithography are characterized experimentally and modelled mathematically. Specifically, the variation in the geometry of the NS inclusions and the Young’s modulus of the photopolymer are measured and modeled by both nonparametric and parametric joint probability distributions. Finally, the quantified manufacturing variability is incorporated into the BNC approach as a manufacturability classifier to identify candidate designs that achieve performance targets reliably, even when manufacturing variability is taken into account.

Design space exploration can reveal the underlying structure of design problems of interest. In a set-based approach, for example, exploration can identify sets of designs or regions of the design space that meet specific performance requirements. For some problems, promising designs may cluster in multiple regions of the design space, and the boundaries of those clusters may be irregularly shaped and difficult to predict. Visualizing the promising regions can clarify the design space structure, but design spaces are typically high-dimensional, making it difficult to visualize the space in three dimensions. Techniques have been introduced to map high-dimensional design spaces to low-dimensional, visualizable spaces. Before the promising regions can be visualized, however, the first task is to identify how many clusters of promising designs exist in the high-dimensional design space. Unsupervised machine learning methods, such as spectral clustering, have been utilized for this task. Spectral clustering is generally accurate but becomes computationally intractable with large sets of candidate designs. Therefore, in this paper a technique for accurately identifying clusters of promising designs is introduced that remains viable with large sets of designs. The technique is based on spectral clustering but reduces its computational impact by leveraging the Nyström Method in the formulation of self-tuning spectral clustering. After validating the method on a simplified example, it is applied to identify clusters of high performance designs for a high-dimensional negative stiffness metamaterials design problem.

Design exploration methods seek to identify sets of candidate designs or regions of the design space that yield desirable performance. Commonly, the dimensionality of the design space exceeds the limited dimensions supported by standard graphical techniques, making it difficult for human designers to visualize or understand the underlying structure of the design space. With standard visualization tools, it is sometimes challenging to visualize a multi-dimensional Pareto frontier, but it is even more difficult to visualize the collections of design (input) variable values that yield those Pareto solutions. It is difficult for a designer to determine not only how many distinct regions of the design (input) space may offer desirable performance but also how those design spaces are structured. In this paper, a form of spectral clustering known as ε-neighborhood clustering is proposed for identifying satisfactory regions in the design spaces of multilevel problems. By exploiting properties of graph theory, the number of satisfactory design regions can be determined accurately and efficiently, and the design space can be partitioned. The method is demonstrated to be effective at identifying clusters in a 10 dimensional space. It is also applied to a multilevel materials design problem to demonstrate its efficacy on a realistic design application. Future work intends to visualize each individually identified design region to produce an intuitive mapping of the design space.

Recent research has indicated that embedding small volume fractions of negative stiffness (NS) inclusions within a host material can create composites with macroscopic mechanical stiffness and loss properties that exceed conventional composites. To design these composites, a multi-level, set-based approach that employs Bayesian network classifiers was developed to identify sets of satisfactory designs at each level of the multilevel design space. In this paper, manufacturing uncertainties are incorporated to further refine the design space mappings created by the set-based approach. Manufacturing uncertainty refers to the random deviations in dimensions and other properties that often arise when fabricating a specimen. Joint probability distributions are used to model this manufacturing uncertainty. The joint probability distributions are formulated as kernel density estimates that can be based on manufacturing data. The joint probability distributions are incorporated within the set-based approach to identify sets of designs that not only yield satisfactory performance but also offer robustness to manufacturing uncertainty. The approach is demonstrated in the context of hierarchical composite materials, but it can be applied to other multi-level design problems to efficiently yield sets of robustly manufacturable, high performance designs.

The evaluation of design concepts is a time consuming and resource intensive process. Crowdsourcing evaluations has been examined in previous work as a means to reduce the need for expert raters, while achieving similar evaluation results. This paper examines the impact of empathically priming novice raters on their evaluation of alternative design concepts. The rating system is based on a pairwise comparison method that requires minimal training of novice raters. In a pilot study the pairwise method for crowdsourcing evaluations is compared with crowdsourced evaluations using non-pairwise rating scales and with the evaluations of expert raters. The primary study incorporates written and empathic priming strategies to determine their impact on novice raters’ evaluation of concepts. Raters are asked to consider several criteria, including novelty, feasibility, clarity (of the concept), usefulness, ease of use, and overall worthiness of further development. Results offer insight into the criteria that are most relevant to novice raters and whether empathic priming has a significant effect on those evaluations.

Small-scale residential solar photovoltaic (PV) systems are becoming increasingly common. In some cases, governments or individual homeowners promote PV technology because of concerns about climate change and a desire to reduce global greenhouse gas emissions (GHGs). While solar PV directly emits no GHGs during use, the panels are associated with a significant amount of embedded GHG emissions, resulting from the manufacturing of the panels, for instance. A review of relevant literature reveals that the life cycle GHG emissions of solar PV panels are significantly influenced by contextual factors, such as the location of the panels during use. The purpose of this paper is to illustrate the many ways context could affect the GHG emissions associated with solar PV systems and to demonstrate — via calculations from a simple analytical model — the potential magnitude of the GHG emissions differences associated with using PV panels in different contexts.

A set-based approach is presented for solving multi-scale or multi-level design problems. The approach incorporates Bayesian network classifiers (BNC) for mapping design spaces at each level and flexibility metrics for intelligently narrowing the design space as the design process progresses. The approach is applied to a hierarchical composite materials design problem, specifically, the design of composite materials with macroscopic mechanical stiffness and loss properties surpassing those of conventional composites. This macroscopic performance is achieved by embedding small volume fractions of negative stiffness (NS) inclusions in a host material. To design these materials, the set-based, multilevel design approach is coupled with a hierarchical modeling strategy that spans several scales, from the behavior of microscale NS inclusions to the effective properties of a composite material containing those inclusions and finally to the macroscopic performance of components. The approach is shown to increase the efficiency of multi-level design space exploration, and it is particularly appropriate for top-down, performance-driven design, as opposed to bottom-up, trial-and-error modeling. The design space mappings also build intuitive knowledge of the problem and promising regions of the design space, such that it is almost trivial to identify designs that yield preferred system-level performance.

Customer Needs Analysis (CNA) is a common method used to help non-user designers to identify latent user needs. However, efforts by engineers in developed countries to conduct CNA with lead users from developing countries is complicated by differences in culture, geographical location, and language among participants. To overcome these issues, we present in this paper an alternative strategy to create Empathic Lead Users (ELU) from non-user product design engineers through the use of simulated lead user experiences. The strategy for creating ELU includes two exercises to build empathic understanding of the needs of lead users. The problem selected for this case study was to improve lower-limb prosthetics for amputees in developing countries. The first exercise was an academic, lecture-style training session — based on videos, photos, and literature — to build understanding of the needs of amputees worldwide and in a particular developing country, Bolivia. The second exercise simulated the experiences of lead users by allowing participants to don, ambulate, and doff a mock-prosthetic limb. Each participant was interviewed before, between, and after the experiences and customer needs were interpreted from their responses. For comparison, lead users in Bolivia and typical users in the United States were similarly interviewed and customer needs interpreted from their responses. The ELU participants were able to identify a majority of the lead user group’s needs and the traditional user group’s needs, 95% and 95% respectively. Further, by completing a CNA with the ELU group, there was a 100% increase in the number of latent needs that could be utilized in future design efforts in this study, relative to the 5 latent needs identified by the lead users themselves. As a result of this study, it appears that customer needs analysis with ELU helps to identify more latent needs, and possibly more avenues for product innovations, than interviewing lead or traditional users alone.

Recent research in the field of composite materials has shown that it is theoretically possible to produce composite materials with macroscopic mechanical stiffness and loss properties that surpass those of conventional composites. This research explores the possibility of designing and fabricating these composite materials by embedding small volume fractions of negative stiffness inclusions in a continuous host material. Achieving high stiffness and loss from these materials by design, however, is a nontrivial task. This paper presents a hierarchical multiscale material model for these materials, coupled with a set-based, multilevel design approach based on Bayesian network classifiers. Bayesian network classifiers are used to map promising regions of the design space at each hierarchical modeling level, and then the maps are intersected to identify sets of multilevel or multiscale solutions that are likely to provide desirable system performance. Length scales range from the behavior of the structured microscale negative stiffness inclusions to the effective properties of mesoscale composite materials to the performance of an illustrative macroscale component — a vibrating beam coated with the high stiffness, high loss composite material.

Estimation of density algorithms (EDAs) have been developed for optimization of discrete, continuous, or mixed discrete and continuous simulation-based design problems. EDAs construct a probability distribution on the set of highest performing designs and sample the distribution for the next generation of solutions. In previous work, the authors have demonstrated how classifier-guided sampling can also be used for discrete variable, discontinuous design space exploration. In this paper we develop the rationale for using classifier-guided sampling as a simple step beyond EDAs that not only improves the characterization of the highest performing designs but also identifies the poorly performing designs and exploits that information for faster convergence to optimal solutions. The resulting method is novel in its use of Bayesian priors to model the inherent uncertainty in a probability distribution that is based on a limited number of samples from the design space. The new classifier-guided method is applied to several example problems and convergence rates are presented that compare favorably to random search and a basic EDA implementation.

In what ways and to what extent are bioinspired designs (BIDs) and the biological systems that inspire them ‘efficient’? The answer to this question provides insight into the potential role BID could have in the generation of energy- and materials-efficient designs. By qualifying and clarifying efficiency-related claims in the context of biological evidence, this paper contributes to the theoretical foundation for BID for efficiency and provides guidance to those developing design tools and methodologies aimed at using BID to enhance the efficiency of engineered products and systems. This paper is organized into three main sections. First, a study is presented examining the reasons authors in the BID community cite when motivating their work. Of the 127 sources analyzed, 40 referenced ‘efficiency’, establishing empirically that efficiency of BIDs and the biological organisms that inspire them is a central motivation for work in BID. Second, efficiency-related claims most commonly made by authors in BID are explained and analyzed using authoritative biological and BID literature. Ultimately, some of the claims prove problematic. However, when qualified appropriately, these claims provide significant insight into how the oft-cited examples of efficiency in biology and BID have arisen. Finally, a study uncovering trends in sustainable BIDs is presented, and the efficiency-related trends are discussed. This study provides examples of some efficiency-related characteristics of biological systems that are successfully being transferred to engineered products and systems through BID. Specifically, the sustainable BIDs analyzed are shown to frequently meet four efficiency-related green design guidelines (GDGs) better than functionally-equivalent non-bioinspired alternatives. Additionally, passive mechanisms, multifunctional designs, and optimized geometries are discussed and shown to be prevalent in the sample of sustainable BIDs studied.

The U.S. Department of Defense (DoD) has recently shown an interest in incorporating resource efficiency into decision-making processes, including decisions that pertain to Forward Operating Military Base Camp (FOB) equipment. Often deployed in environments without access to grid utilities, FOBs require costly deliveries by land or air of resources such as fuel and fresh water. Resource-efficient FOB designs have the potential to reduce supply costs, but competing objectives and uncertain operational conditions complicate the design process. For example, integration of solar photovoltaic panels into existing designs has the potential to reduce the need to burn fuel in generators, however solar panels have up-front logistical and monetary costs that limit widespread use. There are also uncertainties associated with available solar energy and camp electrical loads. The research described here uses computer modeling and simulation of a real FOB subsystem under different operational scenarios to develop configurations of solar panels and batteries that, when integrated with an existing FOB design, maximize resource savings but minimize logistical and monetary costs, showing the benefit of a holistic design strategy that accounts for scenario variation. This research will also show that while different hardware configurations prove most efficient under different scenarios and objectives, certain hardware configurations provide good performance under all scenarios and objectives.