bluuebunny/crossref_metadata_2025 huggingface dataset:
Data-Driven Multifidelity Topology Design With a Latent Crossover Operation
Authors: Kii Taisei , Yaji Kentaro , Fujita Kikuo , Sha Zhenghui , Seepersad Carolyn C.
Published: November 2023

Topology optimization is one of the most flexible structural optimization methodologies. However, in exchange for its high degree of design freedom, typical topology optimization cannot avoid multimodality, where multiple local optima exist. This study focuses on developing a gradient-free topology optimization framework to avoid being trapped in bad local optima. Its core is a data-driven multifidelity topology design (MFTD) method, in which design candidates generated by solving low-fidelity topology optimization problems are updated based on evolutionary algorithms (EAs) through high-fidelity evaluation. The key component of the data-driven MFTD is a deep generative model that compresses the dimension of the original data into a low-dimensional manifold, i.e., the latent space. In the original framework, convergence variability and premature convergence problems arise as the generative process is performed randomly in the latent space. Inspired by a popular crossover operation, we propose a data-driven MFTD framework incorporating a new crossover operation called latent crossover. We apply the proposed method to a maximum stress minimization problem in 2D structural mechanics. The results demonstrate that the latent crossover improves convergence stability compared to the original method. Furthermore, the optimized designs exhibit performance comparable to or better than that in conventional gradient-based topology optimization using the P-norm measure.
Multi-Material Topology Optimization Considering the Bounding Box Dimension Constraint and Assemblability Based on the Extended Level Set Method in Two Dimensions
Authors: Feng Yukun , Noguchi Yuki , Yamada Takayuki
Published: November 2023

This paper proposes a multi-material topology optimization method considering the bounding box dimension constraint and assemblability of the optimized structures in two dimensions. To handle multi-material topology optimization problems, we first introduce the concept of the extended level set method and the topological derivative. Second, we introduce the dimensional constraint of the two-dimensional bounding box and the assembly constraint for assemblability of the multi-material structure. We also elaborate their mathematical models. We then compute the design sensitivities of the two constraints based on the topological derivative and adjoint method. Third, we formulate the problem of multi-material topology optimization with constraints and explain the algorithm flow based on the finite element method. Finally, we verify our proposed algorithm on two numerical examples in two dimensions. The proposed method with two-dimensional geometry constraints on the optimized structures is applicable to machining methods. A representative application is milling, in which the largest and second-largest dimensions of the work-pieces are limited by the working area of the milling machine. In addition, our method is helpful for the assembly structure composed of multiple components. Traditional topology optimization method does not consider the assemblability, which makes the structure difficult to be assembled and decomposed in practical engineering. Our work will assist the manufacturability and assemblability of topology-optimized multi-material structures.
Evaluating the Dynamics of Designers’ Moods Throughout the Collaborative Design Process
Authors: Harris Megan , Toh Christine
Published: November 2023

In this paper, we use sentiment analysis to understand the change in moods of individual designers and teams throughout a collaborative design project. Although there is much research on design psychology, the impact of psychological and emotional characteristics on creativity, and group affective tone, there is limited longitudinal research on the dynamic nature of mood and affective tone over the course of a design project. This limited knowledge leaves a gap in our understanding of how the complex emotional states and interactions among team members affects the ability of the team to work together effectively, and ultimately, achieve expected design outcomes. Research that fills this gap can help improve design outcomes and educational strategies. Thus, a longitudinal study was conducted on student design teams using self-reflections collected from three different points over the course of a semester-long design project, in order to understand changes in designers’ moods during a collaborative project. Our results show that time was a significant predictor of change in moods amongst teams, but not necessarily for individuals. These results highlight the complex relationship between individual and team moods during different phases of the design process and provide a foundation for future systematic investigations of team-based affect on design outcomes.
Design and Evaluation of an Automatic Scheduling-Manual Operation Tool to Bring Precision Irrigation to Resource-Constrained Farmers
Authors: Van de Zande Georgia D. , Sheline Carolyn , Amrose Susan , Costello Jeffrey , Ghodgaonkar Aditya , Grant Fiona , Winter Amos G.
Published: November 2023

As populations increase and freshwater supplies decrease, adopting water- and energy-efficient irrigation practices is crucial, particularly in resource-constrained regions. Here, farmers are often unable to purchase the equipment used in precision irrigation, a practice that implements the automatic scheduling of irrigation events to achieve high efficiency. Currently, no irrigation methods exist that combine the automatic scheduling of irrigation events with the manual operation of valves, a common practice on low-income farms. This work introduces a design concept for an automatic scheduling and manual operation (AS-MO) tool that addresses the efficiency needs of resource-constrained farms and integrates into current manual practices. However, it is unknown how farmers would value such a tool. Through interviews and focus groups facilitated by a series of storyboards and a physical prototype, the proposed concept was evaluated by farmers and key market stakeholders in Kenya, Jordan, and Morocco. Results showed that farmers in Kenya and Jordan in particular valued the proposed AS-MO concept because they want increased efficiency on their farms but did not want to install automatic valves for cost and complexity concerns. A possible market was also found in Morocco, but a majority of interviewed farms preferred automatic valve operation due to large farm sizes. Interviewees provided feedback on how to improve the tool’s design in future iterations. If adopted at scale, this AS-MO tool could increase efficiency on farms that otherwise cannot afford current precision irrigation technology, improving sustainable agriculture worldwide.
Semi-Supervised Autoencoder With Joint Loss Learning for Bearing Fault Detection
Authors: Zhou Kai , Zhang Yang , Tang Jiong
Published: November 2023

Timely and accurate bearing fault detection plays an important role in various industries. Data-driven deep learning methods have recently become a prevailing approach for bearing fault detection. Despite the success of deep learning, fault diagnosis performance is hinged upon the size of labeled data, the acquisition of which oftentimes is expensive in actual practice. Unlabeled data, on the other hand, are inexpensive. To fully utilize a large amount of unlabeled data together with limited labeled data to enhance fault detection performance, in this research, we develop a semi-supervised learning method built upon the autoencoder. In this method, a joint loss is established to account for the effects of both the labeled and unlabeled data, which is subsequently used to direct the backpropagation training. Systematic case studies using the Case Western Reserve University (CWRU) rolling bearing dataset are carried out, in which the effectiveness of this new method is verified by comparing it with other benchmark models.
Factors Hindering the Implementation of Prototyping in the Early Stages of New Business Development
Authors: Mitomi Keita , Honmmi Masashi , Yasugi Masao , Tanaka Satoshi , Ohnaga Nobuyuki , Shirasaka Seiko
Published: November 2023

In order to commercialize a new technology as a product, t is necessary to decide which customers to target and identify their needs. Furthermore, prototyping is an effective way to identify customer needs. In recent years, there has been an increasing number of studies on prototyping in the private sector, and we are beginning to understand what types of prototyping are effective. However, trying to use prototyping in the private sector, cultural differences, and other aspects can be a disincentive to using prototyping in the private sector, and it does not always work. In other words, prototyping is necessary to develop a business using new technology within a company, but it is not enough to simply implement prototyping; we need to understand what is holding it back. Therefore, this study explores the factors that hinder effective prototyping in the early stages of new business development with new technologies in the private sector. Specifically, we used a grounded theory approach to interview members of new business development projects using new technologies at a leading Japanese manufacturer, as well as the collection and analysis of documents. The results of the study suggested two inhibiting phenomena: 1) prototyping skills depend on the individual, and implementation methods often involve tacit knowledge, and 2) development teams are reluctant to communicate with customers. Then, we presented hypotheses for countermeasures against each of these phenomena.
An Experimental Study of Drag Coefficients of a Quadrotor Airframe
Authors: Morris Daniel , Pretty Christopher
Published: November 2023

This paper investigates the aerodynamic drag coefficients of a standard cross-geometry quadrotor airframe at varying pitch and yaw angles, using a wind tunnel. The quadrotor airframe was mounted in the wind tunnel at fixed pitch and yaw angles, and subjected to a range of wind flow speeds. Drag forces created by the mounting systems used were measured and removed from subsequent drag force measurements. The drag force on the airframe at these speeds and attitudes was measured, and used to identify lumped drag-area coefficients. The variability of these coefficients was observed both with and without unpowered propellers mounted on the airframe. It was found that these parameters increased with increasing angle, and were larger with propellers mounted on the airframe. Measurements were also taken at mixed pitch/yaw orientations to determine whether a relationship exists between these drag-area coefficients and ones taken at individual pitch and yaw angles. Validation was performed using computer vision estimates of projected areas and comparison with drag coefficients of geometric shapes. Fourier series models were fit to the drag-area coefficient trends to create estimator functions. The pitch axis estimator functions had adjusted R2 coefficients of 0.976 and 0.932, while the adjusted R2 coefficients for the yaw axis were 0.733 and 0.508 (without and with propellers respectively). The measured drag-area parameter values and fitted estimator functions provide reference values for quadrotor airframe design, simulation and controller development for model-based controllers.
Mitigating the Effects of Source-Dependent Bias and Noise on Multi-Source Bayesian Optimization: Application to Materials Design
Authors: Zanjani Foumani Zahra , Yousefpour Amin , Shishehbor Mehdi , Bostanabad Ramin
Published: November 2023

Bayesian optimization (BO) is a sequential optimization strategy that is increasingly employed in a wide range of areas including materials design. In real world applications, acquiring high-fidelity (HF) data through physical experiments or HF simulations is the major cost component of BO. To alleviate this bottleneck, multi-fidelity (MF) methods are increasingly used to forgo the sole reliance on the expensive HF data and reduce the sampling costs by querying inexpensive low-fidelity (LF) sources whose data are correlated with HF samples. Existing multi-fidelity BO (MFBO) methods operate under the following two assumptions: (1) Leveraging global (rather than local) correlation between HF and LF sources, and (2) Associating all the data sources with the same noise process. These assumptions dramatically reduce the performance of MFBO when LF sources are only locally correlated with the HF source or when the noise variance varies across the data sources. To dispense with these incorrect assumptions, we propose an MF emulation method that (1) learns a noise model for each data source, and (2) enables BO to leverage highly biased LF sources which are only locally correlated with the HF source. We illustrate the performance of our method through analytical examples and engineering problems on materials design.
Deep Generative Model-Based Synthesis of Four-Bar Linkage Mechanisms Considering Both Kinematic and Dynamic Conditions
Authors: Lee Sumin , Kim Jihoon , Kang Namwoo
Published: November 2023

Mechanisms are essential components designed to perform specific tasks in various mechanical systems. However, designing a mechanism that satisfies both kinematic and dynamic requirements is a challenging task. The kinematic requirements may include the workspace of a mechanism, where the maximum scalar displacement of the mechanism can be derived from this region. On the other hand, the dynamic requirements of a mechanism may include its torque transmission, which refers to the ability of the mechanism to transfer power and torque effectively. In this paper, we propose a deep generative model that can generate multiple crank-rocker four-bar linkage mechanism samples that satisfy both the kinematic and dynamic requirements aforementioned. The proposed model is based on a conditional generative adversarial network (cGAN) with some modifications for mechanism synthesis, which is trained to learn the relationship between the requirements of a mechanism with respect to linkage lengths, and generates multiple mechanism samples that satisfy given requirements. The results demonstrate that our proposed method can successfully generate multiple mechanisms that satisfy specific requirements. Our approach has several advantages over traditional design methods. It enables designers to explore a larger design space and efficiently generate multiple diverse and feasible designs. Also, the proposed method considers both the kinematic and dynamic requirements, which can lead to more efficient and effective mechanisms for real-world use, making it a promising tool for linkage mechanism design.
Implementing Application-Driven Data Analytics in Engineering and Engineering Technology Courses With Student Feedback Assessment
Authors: Yu Zhiyuan , Liu Chenang
Published: November 2023

The integration of Application-Driven Data Analytics into Engineering and Engineering Technology courses has become increasingly important due to the growing significance of data in the modern industry. This research aims to explore the potential of introducing new application-driven data analytics modules to advance the teaching effectiveness of the existing fundamental Engineering and Engineering Technology courses in undergraduate education. This pilot project involves one study module called “The Value of Data Analytics in Engineering” in a Machine Design course at Miami University Engineering Technology Department and a Data Analytics Programming course at Oklahoma State University Industrial Engineering & Management School. The lecture in this module includes topics of data collection, manipulation, analysis, and visualization. A lab session is also conducted to analyze the collected tensile test data using Python programming, where various analyses are performed to understand the material properties of the test samples. These analyses include stress-strain curves learning and visualization, material properties calculation, and results comparison to reference data. To gather feedback on the effectiveness of this new study module, students are given surveys after the lectures and lab sessions. The surveys focus on the outcomes of this approach in helping students understand the course material, the ease of tools usage, and the student’s interests in learning further of Data Analytics topics in their future courses. In total 39 students are surveyed for feedback on the lecture and lab session. Overall, the feedback gathered from the students indicates that the approach has been effective. The survey results show that 72% of the students have gained better understanding of data analytics, and 79% of the students are interested in integrating more of the topics into existing curriculum. The use of Python in Google Colab has also been well received by the students, with many finding them easy to use with limited prior data analytics programming experience. Additionally, the students have expressed the usefulness of the skills acquired in their future careers and are interested in learning more about the topics in their curriculum. Therefore, this pilot studies recommend that an application-driven data analytics module can be effectively integrated into more courses in Engineering and Engineering Technology curricula. The future research involves courses such as Introduction to Programming, Manufacturing Processes, Quality Control, Machine Design, Thermodynamics, and Materials Science.
A Multi-Fidelity Gaussian Process Regression Method for Probabilistic Wind Farm Power Curve Estimation
Authors: Li Honglin , Feng Cong , Zhang Jie
Published: November 2023

Accurate estimation of the power curve for wind turbines or wind farms is crucial to ensure their efficient operation and management. However, conventional methods for power curve estimation rely either on expensive and infrequent measurements or on low-quality numerical simulations. Moreover, the majority of previous studies on power curve estimation for wind turbines or wind farms focused on deterministic estimation, which provides a point estimate of the relationship between wind speed and power generation. Nevertheless, the deterministic approach fails to consider the inherent uncertainty associated with wind energy production resulting from varying turbine characteristics. This can lead to inaccurate power generation estimation and suboptimal decisions regarding energy management. In this paper, a kernel density estimation (KDE) based Multi-Fidelity Gaussian Process Regression (MFGPR) model is proposed to fuse theoretical power curve data and the ground true measurements to create a mapping of wind speed and wind power. By conducting a case study on an actual wind farm in China, the efficacy of the proposed MFGPR model was demonstrated in characterizing the variability of wind power. The probabilistic MFGPR model was also able to generate confidence intervals that encompassed the measured power, thereby improving the accuracy and confidence in wind power estimation or wind resource assessment. Overall, the proposed MFGPR model offers a reliable approach to integrate high-fidelity ground measurements and theoretical power curve data, resulting in precise wind resource assessment and power estimation.
A Backbone-Driven Approach for the Positioning of Continuum Robots
Authors: Li Yucheng , Murray Andrew P. , Myszka David H.
Published: November 2023

The area of continuum robotics is showing potential in a variety of applications including surgical robotics, search and rescue operations, manufacturing, and assembly. Shape-changing mechanisms provide the theoretical tools for the synthesis of spatial chains of rigid bodies that position themselves along curves of any complexity. This article introduces a method for generating the backbone of a continuum robot that closely approximates a given spatial curve. The approach combines the kinematic model of the continuum robot, which operates under the constant curvature assumption, with the design profile and target profile concepts from established spatial shape-changing mechanism theory. This approach involves an analysis of the accessibility of the terminal orientation of the spatial curve, which subsequently identifies a rapid synthesis method. This method facilitates the creation of a backbone with a predetermined number of segments that closely aligns with the tangential direction of the reference points of the target profile while simultaneously approximating the target profile itself. Optimization techniques demonstrate the efficacy of using the generated backbone of the rapid approximation method as the initial design for the optimization. In a situation where a close approximation is not needed at interim steps, optimization is not needed to provide a displacement sequence to move the robot.
CyberRiskDELPHI: Towards Objective Cyber Risk Assessment for Complex Systems
Authors: Papakonstantinou Nikolaos , Van Bossuyt Douglas L. , Hale Britta , Arlitt Ryan , Salonen Jarno , Suomalainen Jani
Published: November 2023

Risk assessment is an essential step for architecting the resilience (safety/security) of a mission critical software-intensive system as well as a regular maintenance procedures. It closely relates to estimating the (cyber) insurance needs of the system. Managing of cyber risk involves gathering threat intelligence, prioritizing the current threats against the system of interest, and planning mitigation strategies. While reliability engineering can rely on a relatively stable set of failure modes and statistical data related to their probabilities of occurrence, security deals with a dynamic threat environment. This reality has dictated the use of qualitative methods (like STRIDE and DREAD), relying on the experience and the specific background of the person performing the study. This subjectivity leads to criticism, since results calculated by different experts for the same system can vary significantly. This challenge has been addressed in the past with a method called DELPHI aiming to reduce subjectivity using a group of experts. The scientific contribution of this paper is the development of the CyberRiskDELPHI, a modified version of original DELPHI method for the identification and prioritization of cyber risks. It is demonstrated over a case study of a 5G tactical bubble covering the communication needs of a critical operation. An early evaluation of the use of a large language model (ChatGPT) in risk identification and prioritization for this case study is also included as a complementary side-activity giving an indication of future developments in the risk assessment domain.
Cross-Domain Health Conditions Identification Based on Joint Distribution Modeling of Fused Prototypes
Authors: Xie Tingli , Huang Xufeng , Choi Seung-Kyum
Published: November 2023

Intelligent diagnostic models with deep learning networks have been widely used for health conditions identification of complex mechanical systems. However, the distributional shift and data scarcity phenomenon inevitably exist in practical and real-world scenarios, which further affect the generalization and representation capability of neural networks. Recently, meta-learning has provided an effective way to solve domain shifts and extremely limited data problems. By introducing non-parametric similarity measures into meta-knowledge learning, metric-based meta-learning can seek embedding spaces to compare class prototypes to identify unseen testing samples. This article proposes a novel meta-neural network based on joint distribution modeling of fused prototypes (IFJPN) to alleviate distribution discrepancy in embedding spaces between different domains. First, multi-source time-series data are transformed into images for modality alignment and information aggregation. Second, a feature embedding module with multiple convolutions is developed to obtain fused prototypes from converted images. Then, the Brownian distance covariance is used for efficient joint distribution modeling of fused prototypes, which improves the discriminative capability of cross-domain health conditions with extremely limited data. Extensive experiments on a benchmark dataset in different cross-domain scenarios under two limited data settings show that the proposed IFJPN has more robust recognition and generalization capability than typical transfer-learning or meta-learning methods.
Identification of the Piezoelectric Properties of Materials From Impulsive Tests on Cantilever Harvesters
Authors: Tommasino Domenico , Tonan Michele , Moro Federico , Doria Alberto
Published: November 2023

There are many promising applications of vibration energy harvesting based on piezoelectric materials, but some efficient piezoelectric materials e.g. PZT being used inside MFCs include Pb and other elements that are harmful for the environment. Hence, there is a great research effort to develop new eco-friendly piezoelectric materials. In order to make the best use of these materials, a complete characterization of their piezoelectric and mechanical properties is needed. In this paper an alternative identification methodology for the characterization of piezoelectric materials is proposed. A layer of piezoelectric material is bonded to a structural layer making up a cantilever harvester that is excited with impulsive forces. The identification is carried out in the frequency domain, because on the one hand the impact force generates a significant excitation in a wide frequency band and on other hand the cantilever harvester is characterized by a well-distinct resonance peak. The measured frequency response function is fitted with a mathematical model that includes the unknown parameters of the material, which are reconstructed by using an optimization process. In the paper the proposed method is validated identifying the properties of well-known harvesters. Finally, piezoelectric properties obtained by applying an impulsive excitation are compared with those obtained by applying a harmonic excitation generated by a shaker.
Hydroplaning of Tires: A Review of Numerical Modeling and Novel Sensing Methods
Authors: Vilsan Alexandru , Sandu Corina
Published: November 2023

In this article, previous literature on tire hydroplaning has been reviewed, especially focusing on real-time estimation methodologies and numerical modeling of the partial and total hydroplaning phenomenon. Hydroplaning is a challenging problem for the passenger car tire industry. The current active safety features that equip the most technologically advanced passenger cars cannot predict and prevent the occurrence of the hydroplaning phenomenon. Hydroplaning is a complex phenomenon that depends on a multitude of factors. The total hydroplaning is a phenomenon which occurs when the tire is no longer capable of evacuating the water from the grooves, resulting in total loss of control of the motor vehicle. This represents a situation where the entire contact patch is lifted from the ground to the layer of water that was formed on the road. However, the reduction of the contact between the tire and the road surface is progressive, this situation being called partial hydroplaning. The threshold speed between the partial and total hydroplaning is called the critical hydroplaning speed. These concepts are universally accepted by every researcher in the hydroplaning field, however in our literature review we discovered that the detection of the critical hydroplaning speed threshold varies from one experimental investigation to another.
Knowledge Transfer in Self-Organizing Systems and Impact of Social Ability
Authors: Huang Bingling , Jin Yan
Published: November 2023

Multiagent systems have evolved in both research and practice domains over the past decades. Application domains have also broadened into robotic and vehicular systems, among others. A self-organizing system comprises multiple agents and can adapt to changing operational environments. Early work on the self-organizing system has tackled the system design problem by introducing information fields and logical rules. As tasks become more complex, the difficulty of devising the fields and the rules has become harder. To overcome this limitation, a multiagent reinforcement learning based approach has been explored, aiming to let agents acquire the task knowledge by themselves. The research results thus far have verified the effectiveness of the learning-based approach. However, the training process of reinforcement learning takes a long time and a vast amount of computational resources. The question arises: can the previously trained team knowledge, embedded in neural networks, be reused so that the new training time can be significantly reduced? This paper addresses this overall question by investigating the effectiveness of transferring the knowledge of neural networks learned by a certain agent team to another team of the same domain but with different team sizes. Furthermore, the learning team has social abilities: they not only focus on the task states of the environment while learning but also observe the behavior of other agents. The results of this study have demonstrated the potential benefits as well as limitations of knowledge transfer between teams of the same domain but with different team sizes.
Heat Sink Design Optimization via GAN-CNN Combined Deep-Learning
Authors: Flynn Nathan , Qian Xiaoping
Published: November 2023

This work proposes a combined deep learning based approach to improve thermal component heat sinks involving turbulent fluid flow. A Generative Adversarial Network (GAN) is trained to learn and recreate the new ellipse based heat sinks. Simulation data for new designs is efficiently generated using OpenFOAM 7 (Open Source Computational Fluid Dynamics software) along with high throughput computing. To improve the speed of design evaluation, a Convolutional Neural Network (CNN) is trained to predict the entire temperature field for a given design. The trained CNN is able to predict the entire temperature field for the design with a mean average error of 1.140 degrees kelvin in 0.04 seconds (22,500 times faster than the simulation). A combined model is formed using the trained CNN and GAN networks to create and simulate new designs. The combined model optimizes the latent representation of 64 random designs on a Graphical Processing Unit (GPU) in ten minutes. The optimized designs perform fourteen degrees kelvin better on average than the non-optimized designs. The highest preforming design outperforms any design in the training data by 1.83 degrees kelvin.
A Closed-Form Command Shaping Control of a Rotating Flexible Beam With Nonzero Initial Conditions
Authors: Alhazza Khaled A. , Alfares Mohammed , Alawadhi Abdulaziz
Published: November 2023

This paper proposes a single-mode smooth wave-form command shaping controller to eliminate residual vibrations induced by rest-to-rest maneuver of rotating flexible beams. The proposed shaper is designed to eliminate residual vibration when nonzero initial conditions exist at the start of rest-to-rest maneuver. The nonlinear equation of motion of a rotating flexible beam is derived, discretized, and finally linearized. To eliminate the residual vibrations, the solution of the linear system, with the suggested shaper as an input, is used to determine the shaper parameters analytically. The shaper parameters were calculated by considering the effect of the initial deflection of the beam tip. To demonstrate the shaper performance, two numerical simulations with different initial conditions are conducted. The results of these simulations show the ability of the proposed shaper to eliminate the first-mode residual vibrations of a flexible rotating beam with nonzero initial conditions.
Dynamic Offloading and Selective Redistribution of Plantar Pressure Using Connected Shallow Arches
Authors: Maharana Priyabrata , Shaikh Ahmad , Ananthasuresh G. K.
Published: November 2023

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

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

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

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

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

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

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

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

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

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.