The Next Generation NATO Reference Mobility Model (NG-NRMM) plays a vital role in vehicle mobility prediction and mission planning. The complicated vehicle-terrain interactions and the presence of heterogeneous uncertainty sources in the modeling and simulation (M&S) result in epistemic uncertainty/errors in the vehicle mobility prediction for given terrain and soil conditions. In this paper, the uncertainty sources that cause the uncertainty in mobility prediction are first partitioned into two levels, namely uncertainty in the M&S and uncertainty in terrain and soil maps. With a focus on the epistemic uncertainty in the M&S, this paper presents a testing design optimization framework to effectively reduce the uncertainty in the M&S and thus increase the confidence in generating off-road mobility maps. A Bayesian updating approach is developed to reduce the epistemic uncertainty/errors in the M&S using mobility testing data collected under controllable terrain and soil conditions. The updated models are then employed to generate off-road mobility maps for any given terrain and soil maps. Two types of design strategies, namely testing design for model selection and testing design for uncertainty reduction, are investigated in the testing design framework to maximize the information gain subject to limited resources. Results of a numerical example demonstrate the effectiveness of the proposed mobility testing design optimization framework.

This research proposes the self-similarity design concept of flexible mechanisms by studying the out-of-plane, piston motion of a compliant device. Self-similar compliant mechanisms can be formed by connecting flexible units of scaled-down, identical geometry in series and/or parallel. We study a folded-architecture, compact mechanism class formed of multiple flexible, circular, and concentric segments that are serially connected. The device is capable of producing large displacements by summing the small deformations of its units. A simple analytical model is derived, which predicts the mechanism piston compliance/stiffness in terms of configuration, geometry, and material parameters. Experimental testing of a prototype and finite element simulation of various designs confirm the validity of the mathematical model. Several particular designs resulting from the generic architecture are further characterized based on the analytical model to highlight the mechanism stiffness performance and the way it scales with its defining parameters and unit stiffness.

A new one-stage type cycloid drive reducer named China Bearing Reducer (CBR) is designed and its tooth contact analysis is investigated. First, the CAD model is built in SolidWorks and the structure of CBR reducer is introduced. Its advantages and disadvantages are compared with rotor vector (RV) reducer and harmonic drive reducer. Second, the mathematic model of cycloid profile and modified cycloid profile are established based on gear meshing and differential geometry, and the tooth shapes of three different modifications are compared with CBR25 reducer. Third, the conventional TCA method is described and a new TCA method by using discretized points is proposed to calculate transmission error and contact force of cycloid drive. Both the methods are used to compute the unloaded transmission error of CBR25 reducer to compare the computational efficiency. Finally, three different modified methods of tooth profile are investigated by using the new TCA method to calculate transmission error and contact force of CBR25 reducer, and the results show that the negative isometric and negative offset modified method is best for CBR25 reducer to reduce transmission error and contact force. The prototype of CBR25 reducer is manufactured and the transmission error and hysteresis curve are measured by using a testing bench. The results show that the new TCA method can be used to calculate transmission error and help better design the CBR reducer.

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 0.4 – 1.0 m m , with measured effective elastic moduli from 28 M P a to 213 M P a . 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 4.1 k N / m m to 9.6 k N / m m with yielding after 0.36 – 0.48 m m displacement, thus suggesting their suitability for typical spinal loads of 1.65 k N . The 50 % porous cage with reinforced shell and central void was particularly favorable, with an 8.4 k N / m m 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.

A method is presented to optimize the shape and size of a passive, energy-storing prosthetic foot using the lower leg trajectory error (LLTE) as the design objective. The LLTE is defined as the root-mean-square error between the lower leg trajectory calculated for a given prosthetic foot's deformed shape under typical ground reaction forces (GRFs), and a target physiological lower leg trajectory obtained from published gait data for able-bodied walking. Using the LLTE as a design objective creates a quantitative connection between the mechanical design of a prosthetic foot (stiffness and geometry) and its anticipated biomechanical performance. The authors' prior work has shown that feet with optimized, low LLTE values can accurately replicate physiological kinematics and kinetics. The size and shape of a single-part compliant prosthetic foot made out of nylon 6/6 were optimized for minimum LLTE using a wide Bezier curve to describe its geometry, with constraints to produce only shapes that could fit within a physiological foot's geometric envelope. Given its single part architecture, the foot could be cost effectively manufactured with injection molding, extrusion, or three-dimensional printing. Load testing of the foot showed that its maximum deflection was within 0.3 cm (9%) of finite element analysis (FEA) predictions, ensuring the constitutive behavior was accurately characterized. Prototypes were tested on six below-knee amputees in India—the target users for this technology—to obtain qualitative feedback, which was overall positive and confirmed the foot is ready for extended field trials.

Testing of components at higher-than-nominal stress level provides an effective way of reducing the required testing effort for system reliability assessment. Due to various reasons, not all components are directly testable in practice. The missing information of untestable components poses significant challenges to the accurate evaluation of system reliability. This paper proposes a sequential accelerated life testing (SALT) design framework for system reliability assessment of systems with untestable components. In the proposed framework, system-level tests are employed in conjunction with component-level tests to effectively reduce the uncertainty in the system reliability evaluation. To minimize the number of system-level tests, which are much more expensive than the component-level tests, the accelerated life testing (ALT) design is performed sequentially. In each design cycle, testing resources are allocated to component-level or system-level tests according to the uncertainty analysis from system reliability evaluation. The component-level or system-level testing information obtained from the optimized testing plans is then aggregated to obtain the overall system reliability estimate using Bayesian methods. The aggregation of component-level and system-level testing information allows for an effective uncertainty reduction in the system reliability evaluation. Results of two numerical examples demonstrate the effectiveness of the proposed method.

Many decisions within engineering systems design are typically made by humans. These decisions significantly affect the design outcomes and the resources used within design processes. While decision theory is increasingly being used from a normative standpoint to develop computational methods for engineering design, there is still a significant gap in our understanding of how humans make decisions within the design process. Particularly, there is lack of knowledge about how an individual's domain knowledge and framing of the design problem affect information acquisition decisions. To address this gap, the objective of this paper is to quantify the impact of a designer's domain knowledge and problem framing on their information acquisition decisions and the corresponding design outcomes. The objective is achieved by (i) developing a descriptive model of information acquisition decisions, based on an optimal one-step look ahead sequential strategy, utilizing expected improvement maximization, and (ii) using the model in conjunction with a controlled behavioral experiment. The domain knowledge of an individual is measured in the experiment using a concept inventory, whereas the problem framing is controlled as a treatment variable in the experiment. A design optimization problem is framed in two different ways: a domain-specific track design problem and a domain-independent function optimization problem (FOP). The results indicate that when the problem is framed as a domain-specific design task, the design solutions are better and individuals have a better state of knowledge about the problem, as compared to the domain-independent task. The design solutions are found to be better when individuals have a higher knowledge of the domain and they follow the modeled strategy closely.

Crowdsourcing is the practice of getting ideas and solving problems using a large number of people on the Internet. It is gaining popularity for activities in the engineering design process ranging from concept generation to design evaluation. The outcomes of crowdsourcing contests depend on the decisions and actions of participants, which in turn depend on the nature of the problem and the contest. For effective use of crowdsourcing within engineering design, it is necessary to understand how the outcomes of crowdsourcing contests are affected by sponsor-related, contest-related, problem-related, and individual-related factors. To address this need, we employ existing game-theoretic models, empirical studies, and field data in a synergistic way using the theory of causal inference. The results suggest that participants' decisions to participate are negatively influenced by higher task complexity and lower reputation of sponsors. However, they are positively influenced by the number of prizes and higher allocation to prizes at higher levels. That is, an amount of money on any following prize generates higher participation than the same amount of money on the first prize. The contributions of the paper are: (a) a causal graph that encodes relationships among factors affecting crowdsourcing contests, derived from game-theoretic models and empirical studies, and (b) a quantification of the causal effects of these factors on the outcomes of GrabCAD, Cambridge, MA contests. The implications of these results on the design of future design crowdsourcing contests are discussed.

Consumers often use a product's visual design as a mental shortcut to judge its unobservable attributes. Mental associations between visual design and unobservable attributes aid consumers in their judgments, and hypothetically reduce consumers' mental load. This paper describes a study that shows the possibility of quickly creating an association in subjects' minds between a holistic visual cue of a product—its body shape—and the general idea of “environmentally friendly” versus “not environmentally friendly,” a typically unobservable attribute. In this study, products' actual environmental friendliness was not measured. Subjects completed an association-building task, in which they developed mental associations between a product's visual cues and its “environmental friendliness” rating, an arbitrarily predetermined rating the authors supplied. The body shape was successfully used as a cue to subliminally communicate to subjects the product's “environmental friendliness.” As a comparison, an individual feature of the product was also used to cue; however, that was unsuccessful. An eye-tracking device was used to identify where subjects were focusing their eyes and for how long. In both the association-building task and a testing task that followed, subjects spent a greater percentage of time looking at the product's cued areas (the body and the selected feature). But during the testing task, subjects spent an even higher percentage of their time looking at the cued areas than they did during the association-building task. This indicates that mental associations, or cues, work to distribute mental load more efficiently.

Quantifying the ability of a digital design concept to perform a function currently requires the use of costly and intensive solutions such as computational fluid dynamics. To mitigate these challenges, the authors of this work propose a deep learning approach based on three-dimensional (3D) convolutions that predict functional quantities of digital design concepts. This work defines the term functional quantity to mean a quantitative measure of an artifact's ability to perform a function. Several research questions are derived from this work: (i) Are learned 3D convolutions able to accurately calculate these quantities, as measured by rank, magnitude, and accuracy? (ii) What do the latent features (that is, internal values in the model) discovered by this network mean? (iii) Does this work perform better than other deep learning approaches at calculating functional quantities? In the case study, a proposed network design is tested for its ability to predict several functions (sitting, storing liquid, emitting sound, displaying images, and providing conveyance) based on test form classes distinct from training class. This study evaluates several approaches to this problem based on a common architecture, with the best approach achieving F scores of >0.9 in three of the five functions identified. Testing trained models on novel input also yields accuracy as high as 98% for estimating rank of these functional quantities. This method is also employed to differentiate between decorative and functional headwear, which yields an 84.4% accuracy and 0.786 precision.

Design reviews are typically used for three types of design activities: (1) identifying errors, (2) assessing the impact of the errors, and (3) suggesting solutions for the errors. This experimental study focuses on understanding the second issue as it relates to the number of errors considered, the existence of controls, and the level of domain familiarity of the assessor. A set of design failures and associated controls developed for a completed industry sponsored project is used as the experimental design problem. Nondomain generalists (students from an undergraduate psychology class), domain generalists (first year engineering students), and domain specialists (graduate mechanical engineering students) are provided a set of failure modes and asked to provide their own opinion or confidence on whether the system would still successfully achieve the stated objectives. The confidence level for all domain populations decreased significantly as the number of design errors increased (largest p-value = 0.0793), and this decrease in confidence is more significant as the number of design errors increases. The impact on confidence is lower when solutions (controls) are provided to prevent the errors (largest p-value = 0.0334) as the confidence decreased faster for domain general engineers as compared to domain specialists (p = < 0.0001). The domain specialists showed higher confidence in making decisions than domain generalists and nondomain generalists as the design errors increase.

The paper describes an innovative fixture created within the AUTORECON project of the European Commission's 7th Framework Program. The fixture is designed to respond to the automotive industry's needs of high modularity and full flexibility, by allowing the secure multishape grasping of a very wide range of (often large and heavy) metal workpieces typical for car-assembly operations. The fixture is used as an end-effector of an industrial robot, which in turn acts in cooperation with other machines, such as other robots performing machining or other processes on the part. The fixturing device is both a highly reconfigurable structure and a robotic mechanism: moving as a programmable robot to reconfigure and acting as a supporting structure once a hold on the part has been established. This dual functioning has been made possible by the development of a key component, a stepless lockable revolute joint, described herein. In order to get a readily modifiable system, all parts of the fixture are designed as modules. Prototypes of the main parts and the whole fixture have been fabricated and experiments validating the design are reported.

Topology optimization of an air-cooled heat sink considering heat conduction plus side-surface convection is presented. The optimization formulation is explained along with multiple design examples. A postprocessing procedure is described to synthesize manifold or “water-tight” solid model computer-aided design (CAD) geometry from three-dimensional (3D) point-cloud data extracted from the optimization result. Using this process, a heat sink is optimized for confined jet impingement air cooling. A prototype structure is fabricated out of AlSi12 using additive layer manufacturing (ALM). The heat transfer and fluid flow performance of the optimized heat sink are experimentally evaluated, and the results are compared with benchmark plate and pin-fin heat sink geometries that are conventionally machined out of aluminum and copper. In two separate test cases, the experimental results indicate that the optimized ALM heat sink design has a higher coefficient of performance (COP) relative to the benchmark heat sink designs.

Understanding user needs and preferences is increasingly recognized as a critical component of early stage product development. The large-scale needfinding methods in this series of studies attempt to overcome shortcomings with existing methods, particularly in environments with limited user access. The three studies evaluated three specific types of stimuli to help users describe higher quantities of needs. Users were trained on need statements and then asked to enter as many need statements and optional background stories as possible. One or more stimulus types were presented, including prompts (a type of thought exercise), shared needs, and shared context images. Topics used were general household areas including cooking, cleaning, and trip planning. The results show that users can articulate a large number of needs unaided, and users consistently increased need quantity after viewing a stimulus. A final study collected 1735 needs statements and 1246 stories from 402 individuals in 24 hr. Shared needs and images significantly increased need quantity over other types. User experience (and not expertise) was a significant factor for increasing quantity, but may not warrant exclusive use of high-experience users in practice.

Consumers' product purchase decisions typically involve comparing competing products' visual features and functional attributes. Companies strive for “product differentiation” (Liu et al., 2013, “Product Family Design Through Ontology-Based Faceted Component Analysis, Selection, and Optimization,” ASME J. Mech. Des., 135(8), p. 081007; Thevenot and Simpson, 2009, “A Product Dissection-Based Methodology to Benchmark Product Family Design Alternatives,” ASME J. Mech. Des., 131(4), p. 041002; Kota et al., 2000, “A Metric for Evaluating Design Commonality in Product Families,” ASME J. Mech. Des., 122(4), pp. 403–410; Orfi et al. 2011, “Harnessing Product Complexity: Step 1—Establishing Product Complexity Dimensions and Indicators,” Eng. Econ., 56(1), pp. 59–79; and Shooter et al. 2005, “Toward a Multi-Agent Information Management Infrastructure for Product Family Planning and Mass Customisation,” Int. J. Mass Customisation, 1(1), pp. 134–155), which makes consumers' product comparisons fruitful but also sometimes challenging. Psychologists who study decision-making have created models of choice such as the cancellation-and-focus (C&F) model. C&F explains and predicts how people decide between choice alternatives with both shared and unique attributes: The shared attributes are “canceled” (ignored) while the unique ones have greater weight in decisions. However, this behavior has only been tested with text descriptions of choice alternatives. To be useful to designers, C&F must be tested with product visuals. This study tests C&F under six conditions defined by: The representation mode (text-only, image-only, and image-with-text) and presentation (sequentially or side-by-side) of choice alternatives. For the products tested, C&F holds for only limited situations. Survey and eye-tracking data suggest different cognitive responses to shared text attributes versus shared image features: In text-only, an attribute's repetition cancels its importance in decisions, while in images, repetition of a feature reinforces its importance. Generally, product differences prove to attract more attention than commonalities, demonstrating product differentiation's importance in forming consumer preferences.

In this paper, we present the design and development of a portable, hand-operated composite compliant mechanism for estimating the failure-load of cm-sized stiff objects whose stiffness is of the order of 10 s of kN/m. The motivation for the design comes from the need to estimate the failure-load of mesoscale cemented sand specimens in situ, which is not possible with traditional devices used for large specimens or very small specimens. The composite compliant device, developed in this work, consists of two compliant mechanisms: a force-amplifying compliant mechanism (FaCM) to amplify sufficiently the force exerted by hand in order to break the specimen and a displacement-amplifying compliant mechanism (DaCM) to enable measurement of the force using a proximity sensor. The two mechanisms are designed using the selection-maps technique to amplify the force up to 100 N by about a factor of 3 and measure the force with a resolution of 15 mN. The composite device, made using a FaCM, a DaCM, and a Hall effect-based proximity sensor, was tested on mesoscale cemented sand specimens that were 10 mm in diameter and 20 mm in length. The results are compared with those of a large commercial instrument. Through the experiments, it was observed that the failure-load of the cemented sand specimens varied from 0.95 N to 24.33 N, depending on the percentage of cementation and curing period. The estimation of the failure-load using the compliant device was found to be within 1.7% of the measurements obtained using the commercial instrument and thus validating the design. The details of the design, prototyping, specimen preparation, testing, and the results comprise the paper.

This paper proposes an approach to investigate associations between design concepts and design outcomes. In the proposed approach, three deliverables (concept sketches, proof-of-concept (POC) prototypes, and final products) are evaluated using metrics called creative product semantic scale (CPSS). CPSS scores are analyzed using two methods: correlation analysis and classification tree analysis. Correlation analysis is used to investigate associations between the concept sketches or the POC prototypes and the final products. Classification tree analysis (together with leave-one-out cross-validation (LOOCV) and permutation test) is used to investigate relationships between CPSS scores and the retention of design concepts (i.e., concepts that are embodied in the final products). The proposed approach is illustrated using deliverables in a project-based design class.

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.

Computational simulation models support a rapid design process. Given model approximation and operating conditions uncertainty, designers must have confidence that the designs obtained using simulations will perform as expected. The traditional approach to address this need consists of model validation efforts conducted predominantly prior to the optimization process. We argue that model validation is too daunting of a task to be conducted with meaningful success for design optimization problems associated with high-dimensional space and parameter spaces. In contrast, we propose a methodology for maximizing confidence in designs generated during the simulation-based optimization process. Specifically, we adopt a trust-region-like sequential optimization process and utilize a Bayesian hypothesis testing technique to quantify model confidence, which we maximize by calibrating the simulation model within local domains if and when necessary. This ensures that the design iterates generated during the sequential optimization process are associated with maximized confidence in the utilized simulation model. The proposed methodology is illustrated using a cantilever beam design subject to vibration.

Lead-user methods were applied to develop product design principles that encourage resource-conscious behavior in individuals. Old Order Mennonites (OOMs) were chosen as lead users because of their low-resource consumption lifestyles. Ethnographic analysis revealed that discretizing resource consumption facilitates and encourages conservation behaviors in OOMs. An experimental study demonstrated the effectiveness of discretization in reducing water consumption by non-OOMs. We then generated concepts for products that applied discretization and tested them with users. Concepts were revised and a prototype for saving water was created.