bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dynamic Offloading and Selective Redistribution of Plantar Pressure Using Connected Shallow Arches
Authors: Maharana Priyabrata , Shaikh Ahmad , Ananthasuresh G. K. 
Published: November 2023

Abstract
Current practice of designing therapeutic footwear for managing the neuropathic foot relies on static offloading in two ways. One way is to relieve vulnerable portions of the plantar region from contact by pocketing out specific portions of the insole. The other is to contour the insole to evenly distribute pressure across the plantar region. Both methods have proven to be effective, but the footwear cannot adapt to altered gait over an extended period of use leading to complications such as transfer ulcers. This can be attributed to the premise of “static” offloading. In this paper, we present an alternative method aimed at dynamic offloading that adapts to changing gait of the user. We achieve this by suitably redistributing the plantar pressure by using a pair of arches of opposite curvature connected with a hinge that creates a see-saw-like motion. This is a significant advancement over our previous, clinically validated, prototype consisting of individual snap-through arches that dynamically offload but do not transfer the pressure to another region. In contrast, the connected arches are designed such that no part of the plantar region experiences more than a prescribed threshold pressure at any time over a long period of use. This is enabled by a design methodology that uses closed-form analytical criteria for deciding if the arches can seesaw or attain a second stable state until the loading is reversed. We analytically designed an array of connected, snap-through and bistable arches for the entire plantar region to dynamically redistribute plantar pressure. We also built a musculoskeletal model of the foot comprising bones, ligaments, muscle forces, and enclosing soft tissue, and used it to computationally validate the efficacy of the novel therapeutic footwear. The results of computational validation are useful to obtain ethical approvals for clinical validation and eventual clinical trials.
The Impact of a Product Teardown Activity on Systems Thinking
Authors: Murphy Alexander R. , Whittle Abigail S. , Summers Joshua D. 
Published: November 2023

Abstract
Product teardowns or related activities may help novice engineers develop an understanding of systems and products. These activities allow for novices to directly explore physical components and the connections between those components in a given product, which are both aspects of robust systems thinking. In this study, students’ mental models of common products are examined before and after a product teardown activity using a previously published mental model elicitation method. Participants completed the product teardown activity in groups, identifying components, functionality, and opportunities for design improvement. Results suggest that a product teardown activity improves mental model representations of the same given product. However, the results also show that a product teardown activity has no impact on mental model representations of other closely related products. This suggests that product teardowns do not necessarily foster knowledge transfer as expected. Examples of a student’s mental model representations before and after the product teardown activity are provided. This study sets the foundation for future research on the relationships between systems thinking, mental models, engineering expertise, and other educational interventions within the context of engineering education. In future work, the results of this study may be compared with an identical study with 4th-year undergraduate students to investigate the role of expertise on knowledge transfer and its relationship to the development of robust systems thinking skills.
Supporting Design for Circular Economy Using Lifecycle Co-Simulation
Authors: Aher Gaurav , Macedo Hugo Daniel , Larsen Peter Gorm , Ramanujan Devarajan 
Published: November 2023

Abstract
Design for Circular Economy (DfCE) is becoming a principal focus in industries to replace the traditional linear system with a closed-loop approach. Holistically supporting DfCE requires developing decision-making tools that are able to model the behaviour of a product or system over its entire lifecycle. Thus, the ability to link potentially different simulation models across a product’s lifecycle to the product’s circular economy (CE) performance is needed. To this end, this paper proposes a method for supporting DfCE decision-making using co-simulation. Specifically, the use of Overture/VDM and the INTOCPS toolchain as a co-simulation environment is presented for simulating the lifecycle behaviour of a product in relation to the performance requirements placed on a system, and the consequent CE performance, measured via user-selected micro-level CE indicators. This concept is demonstrated using a design study on a fluid delivery system with the objective of analysing the incorporation of recycled materials in its pipes while meeting the system constraints. The co-simulation environment simulates the use phase behaviour of the fluid delivery system under different control strategies and assesses the resulting CE performance of the system, measured using the material circularity indicator.
An Empirical, Deterministic Design Theory for Compact Drip Emitter Labyrinths
Authors: Ghodgaonkar Aditya , Welsh Emily , Judge Benjamin , Bono Michael , Winter Amos G. 
Published: November 2023

Abstract
Growing food demand, climate change, and constrained natural resources create the need for large-scale, sustainable agricultural intensification. Despite drip irrigation’s ability to be more water efficient than traditional irrigation technologies, its adoption and retention is limited to due to its high hydraulic equipment costs, particularly in low/middle-income countries. As a commodity product, drip emitters contribute directly to raw material costs and additionally dictate tube thickness and related material consumption. This work introduces a new empirical, deterministic design theory for creating compact, low-cost labyrinths, which are otherwise a volume-intensive component of drip irrigation emitters. To simplify design analysis a review of current commercial art, manufacturing process constraints and symmetry-based geometric relationships was conducted, resulting in the labyrinth’s tooth tip gap being selected as a key design variable. The tip gap is correlated with the hydraulic performance of a test labyrinth geometry via a Design of Experiments approach. The experiments shed light on two distinct fluid dynamic regimes in the labyrinth based on the tip gap size and provide an empirical expression between the two. This work demonstrates that simultaneous consideration of symmetry, manufacturing process and design goals enables rapid synthesis of labyrinths that are 43.77% shorter than comparable commercial designs.
On the Connectedness of the Topology Optimization Predictors
Authors: Behzadi Mohammad Mahdi , Ilies Horea T. 
Published: November 2023

Abstract
Deep learning-based topology optimization predictors have been shown to be effective in generating optimal designs. However, these predictors are prone to topological errors, particularly for high-resolution domains. Although various methods have been developed to enhance the accuracy of predicted structures, such as using large training datasets, complex networks, and physics-based loss functions, they do not include topological metrics in the deep learning models. Similar issues arise in other applications, such as blood vessels, neurons, or road segmentation from images, and several modifications to typical loss functions have been proposed to improve the topological validity of the predictions. In this study, we evaluate and compare four distinct topological loss functions to explore their influence on the performance of deep learning-based topology optimization predictors. Our findings offer insights into the advantages and limitations of these modified loss functions and provide a basis for future research and development aimed at improving the accuracy and efficiency of deep learning predictors in topology optimization.
Off-Board Testing Device for Battery Diagnostics and Market Analysis for Battery Reuse
Authors: Ponso Alberto , Bonfitto Angelo , Silvagni Mario , Luciani Sara 
Published: November 2023

Abstract
The growth in electric vehicles market share is one of the main actions taken to fight greenhouse gases emissions, but it also brings new environmental challenges to the table. Because of the high costs connected to the extractions of Lithium and other battery raw materials, the environmental risks posed by battery disposal and the intermittent nature of electricity production from renewable sources, batteries which are not suitable anymore for traction use can turn from waste to a resource, through their recycling for stationary applications. This paper presents a stand-alone device for the diagnostics of battery modules with the following applications: a) it can be used by EV owners for predictive maintenance; b) it enables end-of-line quality control in manufacturing plants after the production of battery cells; c) it can be used to test batteries exhausted for traction use, matching batteries with similar quality for stationary storage units based on batteries in second life. The application of the proposed device can have noticeable impacts on battery manufacturing procedures, accelerating the transition towards EV-based mobility. The diagnostics is performed through Artificial Intelligence trained with experimental characterization of battery cells. This paper in particular presents the hardware selected for a 48 V 25 Ah battery, but the general architecture of the device and the methodology of the training procedure can be extended to any battery size.
Incorporating Social, Policy, and Ethical Considerations in Engineering and Design Education: an Examination of Barriers and Resources
Authors: Saadi Jana I. , Das Madhurima , Roeder Gillian , Ostrowski Anastasia K. , Lee Stella , Santos Marina , Breazeal Cynthia , D’Ignazio Catherine , Yang Maria C. , Verma Aditi 
Published: November 2023

Abstract
Within higher education, there are increasing calls for design-focused fields, such as engineering and urban planning, to incorporate social, policy, and ethical considerations into their courses to create and develop more responsible, society-minded designers to solve today’s complex societal issues. At the same time, not all instructors of design courses may feel equipped or comfortable engaging social, policy, and ethical considerations in their courses. Previous work has shown that engineering courses engage less with these considerations than other design fields. More perspectives are necessary to understand how instructors consider engaging these considerations and their motivations behind it. We present a survey of instructors regarding their curricular practices and goals as they relate to social, policy, and ethical considerations to understand the barriers, resources, and changes of these considerations in design education. Our results demonstrate there is an increased awareness for the need for this type of content that is promising for the future of engaging social, policy, and ethical considerations in design curriculum. However, for those instructors interested in including such considerations there are barriers such as the identifiable relevance, lack of time and educational materials inhibiting their inclusion. This can be supported by more educational resources, financial support, and prioritization in design education.
Computational Analysis of Buckling-Based Stiffness Programming of Liquid Crystal Elastomer Beams
Authors: Hertlein Nathan , Shen Beijun , Kang Sung Hoon , Buskohl Philip R. 
Published: November 2023

Abstract
Liquid crystal elastomers (LCEs) are a novel class of polymer networks with tunable, anisotropic mechanical properties that can be programmed through applied strain, and have demonstrated advantages for actuation, energy absorption and reconfigurable microstructures. Uniaxial tensile strain is often applied to polydomain LCE systems to achieve monodomain director orientations and program new, spatially uniform material properties. In this study, we investigate the ability to embed non-uniform LCE stiffness distributions by harnessing the spatially varying strain fields of post-buckled beam deformations. We demonstrate the post-buckling-based strain programming with a class of tilted, bistable beams that have utility in a range of architected materials. The behavior of these strain-programmed structures is evaluated with a finite element analysis with spatially varying mechanical properties by scaling the shear modulus in a elementwise fashion within a Neo-Hookean material model. A parameter sweep of beam geometry and loading conditions revealed relationships between beam geometry, LCE programming, and the resulting effects on postbuckled shape of the beam during buckling. In particular, the study highlights that while geometry is a dominant factor for buckling behavior, transition points exist in this beam design space where modest, non-uniform changes in stiffness distribution can switch the direction of buckling. Collectively, the study motivates the exploration of alternative strain-to-stiffness mappings to amplify this effect of non-uniform stiffness programming, and the development of experimental systems to validate this buckling-based, strain programming concept.
Higher-Order Centrodes and Bresse’s Circles of Slider-Crank Mechanisms
Authors: Lanni Chiara , Figliolini Giorgio , Tomassi Luciano 
Published: November 2023

Abstract
This paper deals with the formulation via the instantaneous geometric invariants of a specific algorithm to determine the higher-order centrodes and Bresse’s circles for the coupler link of slider-crank mechanisms. In particular, the first, second and third order centrodes can be obtained in any configuration of the mechanism by showing the successive positions of the instant center of rotation and the acceleration and jerk poles. Several graphical and numerical results for a given slider-crank mechanism in different configuration, are also shown.
Applicability of Load Separation Method to Additively Manufactured ABS
Authors: Imeri Astrit , Wilson Christopher D. 
Published: November 2023

Abstract
In recent years, polymer material extrusion additive manufacturing has been increasingly utilized for end-use load bearing applications. Because each layer is created by filament deposition, there are voids and air gaps between layers and welds that make the parts vulnerable to failure. Thus, understanding the fracture properties becomes critical for design and manufacturing of robust components. The fracture toughness of ductile materials is described in terms of J-integral, which evaluates the energy released per unit area of crack surface. In evaluating J-integral, a plastic work factor ηpl is used, which is inferred experimentally. The values of ηpl have been investigated for metals and conventionally manufactured ductile polymers. ηpl can be evaluated using load separation criterion, in which if valid, load is represented as a multiplication of two separate functions: crack geometry and material functions, respectively. When compared to multiple specimen methods for evaluation of ηpl, load separation parameter is an attractive alternative method that uses single specimen records. Hence, saving time and material by printing less number of specimens. This study investigated the applicability of the load separation parameter to single-edge notched bending ABS specimens, as a typical feedstock material for material extrusion additive manufacturing process. Blunt notched specimens with varying notch length to width ratios have been tested. By fitting a power law, ηpl value was calculated and found to be 2.05, which is in good agreement with the 2.0 value for conventionally metals and polymers. Additionally, work factor for total displacement η was also calculated and values were compared with ηpl. Although, both values were close to 2.0, there was a larger amount of scatter for the total displacement case at different displacement records.
Natural Motion Grasping Design Using Control-Oriented Kinematic Models
Authors: Robson Nina , Audrey Vanessa , Hernandez Severino , Golden John , Ton Vincent 
Published: November 2023

Abstract
The paper explores the possibility of creating grasping devices based on a recently developed and experimentally validated control-oriented planar kinematic model for natural human hand configuration based on the geometry of a thumb-finger system, assumed to be softly enclosing the surface of a virtual cylindrical object. The model is able to calculate independently the values for the natural joint rotation angles, as well as joint velocities and accelerations of a two degrees of freedom (DOFs) thumb and a three DOFs long finger system by a single parameter, the radius of the virtual cylindrical object R. Based on the results obtained from the model, we explore the possibility of modifying the design of a four bar thumb and an eight bar index finger of the recently developed Closed Loop Articulated Wearable (CLAW) hand to enhance the wearability. The latter is done through assessing how closely the motion of the modified thumb-index finger pilot prototype resembles the thumb-index finger natural grasping motion. Future applications of the research include the synthesis and design of enhanced wearable hand devices and articulated robotic hands for natural motion.
Design Transformation Cards for Reconfigurable Robotics: Case Study of an Expandable Robot for Cleaning Tasks
Authors: Kalimuthu Manivannan , Hayat Abdullah Aamir , Pathmakumar Thejus , Veerajagadheswar Prabakaran , Elara Mohan Rajesh , Wood Kristin Lee 
Published: November 2023

Abstract
Reconfigurable robotic systems (RRS) have demonstrated their usefulness in various sectors, including surveillance, rescue missions, space exploration, maintenance, and cleaning. However, designing adaptable and flexible robotic systems is challenging, and researchers have developed innovative mechanical designs that can optimize the performance of these systems in different applications. This paper introduces a method for designing a reconfigurable robot using earlier proposed design transformation cards (DTC) that illustrate mechanical mechanisms based on the transformation design theory. These cards serve as a visual tool for ideation and design-by-analogy, enabling designers to explore the best possible combinations of mechanical mechanisms for their robot design. The case study application of the DTC is presented by designing the block of an expandable Tetris-inspired robot named e-Smorphi (expandable-Smorphi) which consists of four units of expandable blocks that can change their dimensions in length and width, allowing for adaptation to different environments in two dimensions. The proposed design has potential application to the cleaning and maintenance industry, where the objective is to be maximizing the area coverage. The paper also describes experiments with the selected prototype, highlighting the reconfiguration in two dimensions while accessing the environment for cleaning and maintenance (CnM) tasks. The limitations and advantages of using DTC are also discussed as insights into the development of new design tools and techniques for reconfigurable robotics, paving the way for further innovation and advancements in the field.
Modeling Distributed Situation Awareness in Resilience-Based Design of Complex Engineered Systems
Authors: Irshad Lukman , Hulse Daniel 
Published: November 2023

Abstract
Human operators play a major role in the resilience of complex systems — while human error is one of the biggest contributors to hazardous events, operators additionally play a critical role in mitigating hazardous events. A key factor underlying this operator resilience is situation awareness — the ability of operators to understand their environment and each other to achieve desired system functions. In contrast to situation awareness-related accident models in the literature, which are largely conceptual in nature, this work proposes the use of a dynamic simulation framework to concretely model both the effects of situation awareness-related human errors and situation awareness-related hazard-mitigating properties using the distributed situation awareness theory. This work then presents specialized model constructs to enable agents’ individual perceptions of the system state and transactions with other agents (and thus distributed situation awareness) to be represented in simulation. To demonstrate this framework, it is then adapted to an aircraft taxiway case study, where it is used to model aircraft conflicts due to lack of vision and poor communications from the air traffic controller. This demonstration shows the potential of using simulation models to rigorously understand situation awareness-related human errors and thus inform the design of resilience.
Fewer Triplets Than You Think: Novelty Error Converges Faster Than Triplet Violations in Ordinal Embeddings
Authors: Keeler Matthew , Fuge Mark 
Published: November 2023

Abstract
A practical and well-studied method for computing the novelty of a design is to construct an ordinal embedding via a collection of pairwise comparisons between items (called triplets), and use distances within that embedding to compute which designs are farthest from the center. Unfortunately, ordinal embedding methods can require a large number of triplets before their primary error measure — the triplet violation error — converges. But if our goal is accurate novelty estimation, is it really necessary to fully minimize all triplet violations? Can we extract useful information regarding the novelty of all or some items using fewer triplets than classical convergence rates might imply? This paper addresses this question by studying the relationship between triplet violation error and novelty score error when using ordinal embeddings. Specifically, we compare how errors in embeddings produced by Generalized Non-Metric Dimensional Scaling (GNMDS) converge under different sampling methods, for different numbers of embedded items, sizes of latent spaces, and for the top K most novel designs.
We find that estimating the novelty of a set of items via ordinal embedding can require significantly fewer human-provided triplets than is needed to converge the triplet error, and that this effect is modulated by the type of triplet sampling method (random versus uncertainty sampling). We also find that uncertainty sampling causes unique converge behavior in estimating most novel items compared to non-novel items. Our results imply that in certain situations one can use ordinal embedding techniques to estimate novelty error in fewer samples than is typically expected. Moreover, the convergence behavior of top K novel items motivates new potential triplet sampling methods that go beyond typical triplet reduction measures.