bluuebunny/crossref_metadata_2025 huggingface dataset:
Design of a Mass-Manufacturable Passive Prosthetic Foot
Authors: Geil Autumn , Irani Urvaksh , Reddie Madison , Shorter Amanda L. , Winter Amos G.
Published: November 2024

A lack of affordable energy storage and return (ESR) prosthetic feet leads many amputees in low- and middle-income countries (LMIC) to use feet with biomechanical performance inappropriate for their activity levels. To create better options, the GEAR Lab at MIT developed the LLTE design framework, which systematically designs ESR feet to enable replication of able-bodied gait. LLTE-optimized foot prototypes have been tested in a long-term field trial in India and in gait labs in the United States. The feet demonstrated robustness to use for activities of daily living in India, and as good or better biomechanical performance and user satisfaction compared to commercial carbon fiber feet sold in the United States. However, these prototypes were not designed to be commercial products, but rather to demonstrate the viability of the LLTE design framework. This work translates these costly proof-of-concept prototypes to affordable commercial products by making the foot mass-manufacturable and improving user satisfaction via aesthetics, coronal compliance, and a sandal toe. The keel is revised to be compatible with injection molding manufacturing constraints. It is then encased in a polyurethane foam overmold with a ruggedized sole that resembles a biological foot. Commercialization of this foot will offer amputees an affordable ESR option with improved biomechanical performance over currently available options.
Simultaneous Thermal and Electrical Cloaking Via Level-Set-Based Topology Optimization With Isotropic Materials
Authors: Xu Xiaoqiang , Chen Shikui
Published: November 2024

Cloaks are devices designed to conceal objects from detection. With the advancement of metamaterials, there is an increasing interest in developing multi-functional cloaks rather than single-functional ones to cater to various application scenarios. This paper proposes to design simultaneous thermal and electrical cloaking devices via a level-set-based shape and topology optimization scheme. Unlike popular methods such as coordinate transformation and scattering cancellation, which are vulnerable to high material anisotropy, the proposed method only employs naturally occurring bulk materials, which can greatly facilitate physical realization. The bifunctional cloak is achieved by reproducing the reference temperature and electrical potential fields in the evaluation domain through the optimal layout of two thermally and electrically conductive materials in the design domain. Due to the clear boundary characteristic of the level-set formulation, no filtering schemes are required to render a “0-1” design as opposed to density-based counterparts. The feasibility and validity of the proposed method are demonstrated through a number of numerical examples with parallel or orthogonal inflow direction combinations. The dependency of the optimization results on the initial designs is also briefly investigated. Despite exhibiting a notable reliance on the initial guesses, the objective functions based on least square error are sufficiently small to be considered an effective cloak. This work has the potential to spark broader exploration into meta-devices carrying multiple functionalities.
An Order-Free End-to-End Multi-AGV Scheduling Method Based on Safe Deep Q-Network and Asynchronous Coordination
Authors: Qu Jingbo , Li Mian , Wang Tianyu
Published: November 2024

Automated Guided Vehicle (AGV) scheduling is critical to enhance the resource utilization and ensure the timely task completion in the shop floors. Some prior works deal with the AGV scheduling problem through heuristic algorithms. Due to the challenge of accurate mathematical modeling, deep reinforcement learning-based methods have been developed by recent works. However, they mostly require the dispatching orders to be generated in advance, which can be difficult for some end users. The computational efficiency and scalability also have been rarely explored. To tackle these issues, this paper proposes an end-to-end, lightweight and scalable scheduling method relying only on high-level descriptions of the ultimate goal. A Markov decision process formulation is specially designed to encode the studied problem involving extra buffer zone and pallet. Then a lightweight deep Q-network is developed to fuse the multi-source state information and learn optimal policy. Considering the physical constraints, a shielding mechanism is further employed to ensure the accessibility of the learnt policy. Finally, a multi-AGV coordination algorithm is designed to realize joint dispatching operations of multiple AGVs. Experiments on a real shop floor of a large plant show the superiority of the proposed method in terms of the total makespan as well as the scalability towards prevalent changes in the shop floor layout.
Bridging Design Gaps: A Parametric Data Completion Approach With Graph-Guided Diffusion Models
Authors: Zhou Rui , Yuan Chenyang , Permenter Frank , Zhang Yanxia , Arechiga Nikos , Klenk Matt , Ahmed Faez
Published: November 2024

This study introduces a generative imputation model leveraging graph attention networks and tabular diffusion models for completing missing parametric data in engineering designs. This model functions as an AI design co-pilot, providing multiple design options for incomplete designs, which we demonstrate using the bicycle design CAD dataset. Through comparative evaluations, we demonstrate that our model significantly outperforms existing classical methods, such as MissForest, hotDeck, PPCA, and tabular generative method TabCSDI in both the accuracy and diversity of imputation options. Generative modeling also enables a broader exploration of design possibilities, thereby enhancing design decision-making by allowing engineers to explore a variety of design completions. The graph model combines GNNs with the structural information contained in assembly graphs, enabling the model to understand and predict the complex interdependencies between different design parameters. The graph model helps accurately capture and impute complex parametric interdependencies from an assembly graph, which is key for design problems. By learning from an existing dataset of designs, the imputation capability allows the model to act as an intelligent assistant that autocompletes CAD designs based on user-defined partial parametric design, effectively bridging the gap between ideation and realization. The proposed work provides a pathway to not only facilitate informed design decisions but also promote creative exploration in design.
Advancing Vehicle Trajectory Prediction: A Probabilistic Approach Using Combined Sequential Models
Authors: Ren Lichuan , Xi Zhimin
Published: November 2024

In the domain of autonomous vehicle control, the foundation often lies in vehicle dynamics models, which simplify the complex behavior of vehicles to design effective controllers. However, these simplified models can introduce biases, particularly in extreme driving scenarios. Additionally, environmental factors introduce uncertainty into vehicle trajectories. Even with identical control actions, vehicles can display varying behaviors in different scenarios. This paper proposes methods for predicting the probabilistic distribution of a vehicle’s trajectory based on initial conditions and control action sequences. We leverage sequential learning and bias learning techniques to enhance learning efficiency. Our systematic study compares the Seq2Seq model, the particle-based model, and the hybrid model regarding prediction accuracy. The performance of these methods is evaluated and compared using both point error metrics and probabilistic prediction metrics. This research introduces novel approaches to probabilistic modeling of vehicle trajectories by combining physics-based and data-driven models, which can further contribute to the development of more robust and adaptable autonomous vehicle control systems that can navigate complex, uncertain, and extreme driving scenarios.
Multifidelity Topology Design for Turbulent Heat Transfer Problems Using a Darcy Flow Model Incorporating Pseudo-Boundary Layer
Authors: Ohtani Kaito , Yaji Kentaro , Fujita Kikuo
Published: November 2024

Topology optimization for turbulent heat transfer problems is challenging due to the multimodality of the solution space caused by the strong nonlinearity of the physical model. In this study, we utilize the framework of multifidelity topology design (MFTD), which is effective for such computationally hard problems. The framework aims to yield promising design solutions by dividing the original problem into low-fidelity (LF) and high-fidelity (HF). In the first step, we aim to generate exhaustive solutions in the solution space by executing TO using a LF model under a wide variety of optimization conditions. The best design is selected from these solutions by evaluation using a HF model. The performance of the final design depends on whether the LF model can generate promising solutions. Therefore, the construction of an appropriate LF model is critical for the MFTD. In this study, we propose a Darcy flow model incorporating pseudo-boundary layer. This model achieves higher accuracy than the conventional Darcy flow model by considering viscous effects. We use the proposed flow model as a LF model of the MFTD and confirm the effectiveness by showing that promising solutions to turbulent heat transfer problems exist among the various solutions generated by the proposed flow model.
Simultaneous Stiffness and Trajectory Optimization for Energy Minimization of Pick-and-Place Tasks of Sea-Actuated Parallel Robots
Authors: Kordik Thomas , Gattringer Hubert , Müller Andreas
Published: November 2024

A major field of industrial robot applications deals with repetitive tasks that alternate between operating points. For these so-called pick-and-place operations, parallel kinematic manipulators (PKM) are frequently employed. These tasks tend to automatically run for a long period of time and therefore minimizing energy consumption is always of interest. The latest developments addressing this issue deal with the use of elastic elements and actuators, where research on series elastic actuators (SEA) appears underrepresented. Motivated by a simplified introductory thought experiment, this paper explores the possibilities of minimizing energy consumption of PKM actuated by SEA during pick-and-place tasks. The idea is to excite eigenmotions that result from the actuator springs and exploit their oscillating characteristics. To this end, a typical cyclic pick-and-place operation is analyzed and a dynamic model of SEA driven PKM is derived. Subsequently, an energy minimizing optimal control problem is formulated where operating trajectories as well as SEA stiffnesses are optimized simultaneously. Here, optimizing the actuator stiffness does not account for variable stiffness actuators. It serves as a tool for the design and sizing process. The hypothesis on energy reduction is tested on two (parallel) robot applications where redundant actuation is also addressed. Lastly, results are shown and the validity of this approach is discussed.
Engineering Design Education: Assessing Student Learning in Cornerstone Through Capstone Programs
Authors: Fazelpour Mohammad , Joshi Shraddha , Mocko Gregory , Murphy Alexander R. , Ramnath Satchit , Renu Rahul S. , Righter James A. , Sen Chiradeep , Summers Joshua D. , Patel Apurva
Published: November 2024

This paper presents a “call to arms” for reconsidering how students’ engineering design learning and skill development are assessed, shifting from a traditional summative assessment to structured, repetitive formative assessment. Students are expected to have developed 21st Century Skills, engineering skills, and design skills by the time that they matriculate through the undergraduate program. These skills are often overlapping and are recognized by faculty as critical and central to student development. However, many of these skills are not directly evaluated and the formative feedback that students need to develop these skills is often lacking. This paper presents an elaboration on these skills, various means of assessment that are common through engineering design curricula, and then a review of different design education approaches used in several mechanical and general engineering programs. The three general phases of an undergraduate students career are detailed with respect to how design is integrated into the first year programs, mid-program, and capstone programs. Finally, a set of recommendations are provided to start the conversation at different institutions. These recommendations are not intended to be the final solution, but can be considered early prototypes that can be refined and edited for the unique needs of each different program. Ultimately, it is recommended that students be provided multiple engineering design project experiences where they are provided feedback and then can use the lessons learned in executing subsequent projects. It is critical that engineering design educators continue to reflect on their current practices, learning from others, and identifying opportunities for improvement, even if it might cause discomfort in a currently well run operation. The student learning should always be at the forefront of all decisions made in engineering design education.
Probability-Based Bounding Box With Confident Object Localization for Autonomous Navigation
Authors: Bae Jinwoo , Xi Zhimin
Published: November 2024

The Object detection, especially for autonomous navigation, requires high accuracy of the localization accuracy on the objects detected. The bounding box is devised to provide the localization of the object detection. However, the confidence score of the bounding boxes does not provide the localization reliability although the bounding boxes are selected by the confidence score through a non-maximum suppression (NMS) task. In this paper, we propose a novel bounding box regression framework to build a probability-based bounding box. The proposed bounding box leverages the spatial and confidence information of the candidate boxes removed by the NMS task. The information enables to capture the object entirely through expanding the localization extent of the tradition bounding boxes reliably, where the extent is asymmetrically expansion on each side to avoids unnecessary localization. In addition, the regression task using the information of the candidate boxes allow us to the extent of the bounding box for desire confidence level. The proposed method is evaluated on sample images from Berkery DeepDrive (BDD) dataset, where the samples include objects only partially detected by the traditional bounding box. Our experiment presents that the probability-based bounding box would provide more reliable localization compared to the tradition bounding box.
Generalized Anisotropic Multiobjective Topology Optimization for Naval Additive Manufacturing
Authors: Steuben John , Iliopoulos Athanasios , Birnbaum Andrew , Graber Benjamin , Michopoulos John
Published: November 2024

The use of topology optimization algorithms for engineering design has become widespread. Motivated by the objective of producing lightweight, high-performance components, a wide variety of approaches have emerged, and many alternatives now exist for every stage of the associated computing pipeline. The present work introduces a generalized topology optimization framework for use in the area of additive manufacturing (AM), with a focus on naval applications. The development of this framework is outlined, with particular attention paid to the development of an anisotropic experimental data-driven material constitutive model. Additionally, methods for handling arbitrary numbers of objectives, design constraints imposed by additive manufacturing process physics, and boundary conditions particular to naval applications are explored. Steps for post-processing and refining the optimizer output in order to produce manufacturing-ready files are also discussed. The approach is demonstrated on a pair of test cases. The first is the design of an optimized “conformal” pressure vessel, which departs from the typical sphere / cylinder forms and allows for increased storage capacity by occupying a non-convex envelope space. The second application problem is the design of an optimized propulsor intended to be produced using large-scale AM. This propulsor is optimized for inertial and hydrodynamic loads, and a parametric study of the effect of outer shell thickness is performed.
Valuing Outliers: a Modeling Framework to Consider Non-Traditional Solutions From Non-Traditional Solvers
Authors: Dharmarajan Athul Chakkithara , Topcu Taylan G. , Panchal Jitesh H. , Szajnfarber Zoe
Published: November 2024

While the engineering design research community has developed numerous models for early-stage design over the years, these models explore the design space corresponding to the dominant solution in the domain. These models work well for making predictions for designs by experts within an organization, but they cannot compare solutions that use alternative principles to solve the same problem. With the increasing popularity of open innovation and crowdsourcing within engineering design organizations, it is important to value the non-traditional solutions from non-traditional solvers in the early-stage design to make informed decisions about structuring the problem-solving mechanism employed by the organization. This paper attempts to create a new modeling framework to predict the design outcome by valuing non-traditional solutions alongside the dominant solution, thereby capturing the potential performance and novelty offered by the solutions from the non-traditional solvers. The model is demonstrated using a case study of a robotic gripper design. The results indicate the model can predict the best solution for the same problem across different principles. In addition, we find that the model is more important for non-traditional solvers than traditional solvers, as a larger fraction of their solutions would have been missed by models considering only the dominant solution.
An Online Sequential Update Method for Reduced-Order-Model-Based Digital Twin
Authors: Tang Yifan , Sajadi Pouyan , Rahmani Dehaghani Mostafa , Wang G. Gary
Published: November 2024

Digital twin (DT) refers to any model that reflects a physical system and remains updated with the real-time data from the physical system. Recently, DT constructed with a reduced-order model (DT-ROM) has been applied to aerodynamics and structure health monitoring tasks, whose partial differential equations (PDEs) are used to design the online update formula for reduced bases and coefficients in DT-ROM. Although such online update methods improve the performance of DT-ROM, they are not applicable when the PDEs of a system are unknown. This paper focuses on the online update task where a system is modeled with a DT-ROM, but the system’s PDEs are unknown. To tackle the task, an online sequential update method is proposed. During the update process, the projection residual of online data on the reduced bases is applied to determine whether to update the reduced bases in ROM, and the prediction residual of online data obtained by the offline DT-ROM is adopted to infer whether to update the coefficient models. By checking both criteria sequentially, the online update method selects the necessary online data for the DT update. Three numerical problems and one engineering problem are designed to test the proposed online update method. Testing results demonstrate that (a) the proposed online update method reduces both the projection and prediction residuals gradually, and (b) the performance of the offline DT-ROM model on the testing data is improved gradually during the online update process. Both indicate that the proposed method could be applied to online DT update tasks where the PDEs of the system are unknown.
Human-AI Collaboration Among Engineering and Design Professionals: Three Strategies of Generative AI Use
Authors: Ma Kevin , Moore George , Shyam Vikram , Villarrubia James , Goucher-Lambert Kosa , Reynolds Brubaker Eric
Published: November 2024

Designers are increasingly using Generative Artificial Intelligence (GenAI) in design processes; however, knowing how designers use GenAI — especially in professional design practice — is under-explored. This paper presents an ethnographic study of an early-stage design team at NASA that explores the natural variation of GenAI use across team members during a speculative design workflow. We aimed to uncover when, how, and why GenAI tools were or were not employed using ethnographic observations to map the team’s speculative design process and follow-up interviews to provide deeper insights into team members’ interactions (or lackthereof) with GenAI. Through inductive qualitative coding, our analysis revealed three strategies of GenAI use observed among professional engineers and designers — intimate co-design with GenAI, selective delegation to GenAI, and minimal use of GenAI — as well as factors that appeared to influence their decisions whether or not to use GenAI. This study proposes new theory in human-AI collaboration that sheds light on the strategies, rationale, and circumstances under which design professionals do and do not use GenAI. These strategies and factors tied to GenAI use offer insights into when, how, and why professionals use GenAI in design and how GenAI could be built to better accommodate designers.
Design of Compliant Pivots Based on Three-Flexure Arrangement
Authors: Serafino Simone , Bruzzone Luca , Fanghella Pietro , Verotti Matteo
Published: November 2024

Cross-axis pivots, thanks to their high precision and large range of motion, are widely used in mechanical systems. However, their axis drift is generally not negligible and reduces the joint accuracy, limiting the range of the possible implementations. In Literature, this issue has been addressed by following different approaches, often based on the optimization of various kinetostatic parameters or on the combination of more than two flexible elements. In this paper, a novel family of compliant joints is presented. The novel layout is based on the arrangement of three flexures. More specifically, the design of the triple-axis pivots consists of the combination of two crossed flexures with initial curvature and of an auxiliary straight beam. The design procedure includes a proper positioning of the flexures’ centroids and the definition of an equivalence criterion for the rotational stiffness. Non-linear finite element analysis simulations have been performed to evaluate the kinetostatic response of the joint. The results are collected in the form design maps representing the effects of the design parameters on accuracy and stiffness characteristics. An application of the design procedure is presented, and the performance of the defined triple-cross axis pivot are compared to the ones referring to a traditional double-axis joint.
A Design Method for a Self-Organizing Closed-Loop Control System Based on Stigmergy and Bi-Level Planning
Authors: Cao Jinhui , Ming Zhenjun , Allen Janet K. , Mistree Farrokh
Published: November 2024

Self-organizing systems are capable of executing tasks in complex and unknown domains that are unsuitable for humans. However, designing of self-organizing systems is challenging due to balancing global and local decision-making and in enabling the system to adaptively generate desired behaviors in different environments. To address these challenges, in this paper we propose a self-organizing system design framework based on stigmergy and bi-level planning. We use the multi-agent box-pushing problem as an example to verify the framework. Within this framework, the upper-level planning (which is used for box position prediction) and the lower-level planning (which is used for agent position allocation) are designed to connect and coordinate between the global and local decisions. The influence of states based on stigmergy creates a closed-loop control mechanism with “pheromones” as input, allowing the system to adapt to various environments. In this paper we use Webots as the simulation platform to conduct multi-agent box-pushing experiments and compare the results with an ant colony handling method, to demonstrate the effectiveness and advantage of our approach.
Towards a Multiphysics-Based Digital Twin for Predicting Aging of Multimaterial Adhesive Step Lap Joints
Authors: Michopoulos John G. , Apetre Nicole A. , Iliopoulos Athanasios P. , Steuben John C.
Published: November 2024

We present the initial steps towards formulating a multiphysics-based digital twin for predicting aging for multi-material adhesive step lap joints (ASLJs) that accounts for mechanical and environmental generalized loading. Initially, we provide a definition of the digital twin context and the particular digital twin architecture considered associated with the data-driven digital twin flavor. Subsequently, focus is given to establishing and exercising a theoretical and computational framework accounting for the degradation of properties for materials participating in ASLJs due to environmentally-aided damage accumulation. A thermodynamics-based theory is derived as a first step. An initial computational implementation of it is developed and applied to two models of multi-material ASLJs involving a titanium Ti-6Al-4V alloy and carbon epoxy composite adherent and an FM-300K adhesive. Several studies have been performed to identify the effect of parameters related to the choice of element order, mesh density, damage parameters and inclusion of damage models for the participating domains. Finally, the predictions obtained from the framework are compared with experimental results to establish the necessary validation for two environmental conditions of room and elevated temperatures at low ambient humidity. These results provide an initial insight into the characteristics of the aging degradation of ASLJ performance when both the adhesive and the composite material adherent are accumulating damage and contribute to aging of the respective materials.
Elicitron: A Framework for Simulating Design Requirements Elicitation Using Large Language Model Agents
Authors: Ataei Mohammadmehdi , Cheong Hyunmin , Grandi Daniele , Wang Ye , Morris Nigel , Tessier Alexander
Published: November 2024

Requirements elicitation, a critical yet time-consuming and challenging step in product development, often fails to capture the full spectrum of user needs. This paper introduces a novel framework that leverages Large Language Models (LLMs) to automate and enhance the requirements elicitation process. LLMs are used to generate a vast array of simulated user agents, enabling the exploration of a much broader range of user needs and unforeseen use cases. These agents then engage in product experience scenarios, based on the Action, Observation, and Challenge framework introduced in this paper, inspired by Chain-of-Thought reasoning to produce structured responses. Subsequent agent interviews uncover valuable user needs. We validate our framework with two experiments. First, we explore different methodologies for diverse agent generation, discussing their advantages and shortcomings. We measure the diversity of identified user needs and demonstrate that serial agent generation leads to greater diversity. Second, we show how our framework effectively mimics empathic lead user interviews, identifying a greater number of latent needs than conventional human interviews. Our work highlights the potential of using LLMs to accelerate early-stage product development, reduce costs, and increase innovation.
Identifying Redesign Pathways Over a Planned Number of Modifications to Support Change Management
Authors: Jacobson Lindsey , Ferguson Scott
Published: November 2024

Complex engineered systems evolve with the world around them as operating environments change and new demands arise. Yet, modifying these systems remains a challenge. Changes can easily propagate, making it difficult to limit the scope of system modifications. Mitigating, or preventing, change propagation enables more efficient redesign. In this paper, we develop a method for identifying feasible system configurations that are accessible within a discrete number of modifications. With this information, designers can consider modifications where change propagation is controlled. We leverage Complete Flexibility Ranges (CFRs), the extent to which a decision variable (DV) can change while maintaining system feasibility, to identify feasible changes. By modifying DVs in their CFRs, designers guarantee change will not propagate further. We also demonstrate how feasible configurations can be produced from strings of modifications, given that the final modification involves a DV changing within its CFR. The proposed method is applied to a high-powered rocket system to identify new configurations that could be created given different modification strings. The results from our study support the belief that modifying sets of DVs can be more efficient than modifying DVs independently and consecutively. We also show that making infeasible changes may enable greater exploration of the design space but may limit further change options. The proposed method could be applied in future work to 1) compare the performance of different redesign strategies, 2) explore the impact of decision selection order, and 3) develop heuristics for design for changeability. This work provides a foundation for further study on how to efficiently design and redesign changeable systems.
Modeling of a Reconfigurable Cable-Driven Parallel Robot Under Low Gravity
Authors: Ang Han Wei (Kelvin) , Nelson Carl A.
Published: November 2024

Cable-driven parallel robots (CDPRs) boast advantages such as large workspace and large payload to weight ratio, among others. In the context of maintenance of space structures, these properties could be beneficial. However, CDPRs have not yet been widely applied in the domain of space exploration. The goal of the work presented in this paper is to model a novel CDPR with a reconfigurable structure which can be placed to traverse large areas of space, with space structure maintenance in mind as a potential target application. Kinematics, pose feasibility, and reconfiguration strategy are discussed, and a simulation example is presented to further illustrate the concept.
A Picture or a Thousand Words: Design Description Crafting to Replicate Human Similarity Judgments in Large Language Models
Authors: Keeler Matthew , Fuge Mark , Peng Aoran , Miller Scarlett
Published: November 2024

Well-studied techniques that enhance diversity in early design concept generation require effective metrics for evaluating human-perceived similarity between ideas. Recent work suggests collecting triplet comparisons between designs directly from human raters and using those triplets to form an embedding where similarity is expressed as a Euclidean distance. While effective at modeling human-perceived similarity judgments, these methods are expensive and require a large number of triplets to be hand-labeled. However, what if there were a way to use AI to replicate the human similarity judgments captured in triplet embedding methods? In this paper, we explore the potential for pretrained Large Language Models (LLMs) to be used in this context.

Using a dataset of crowdsourced text descriptions written about engineering design sketches, we generate LLM embeddings and compare them to an embedding created from human-provided triplets of those same sketches. From these embeddings, we can use Euclidean distances to describe areas where human perception and LLM perception disagree regarding design similarity. We then implement this same procedure but with descriptions written from a template that attempts to isolate a particular modality of a design (i.e., functions, behaviors, structures). By comparing the templated description embeddings to both the triplet-generated and pre-template LLM embeddings, we identify ways of describing designs such that LLM and human similarity perception better agree. We use these results to better understand how humans and LLMs interpret similarity in engineering designs.
Comparison of Several Compliant Variable-Stiffness Mechanism Designs for Industrial Applications
Authors: Zwiener Benjamin L. , Nelson Carl A.
Published: November 2024

Integration of variable-stiffness properties into robotic actuation systems has become a topic of interest in recent decades. Potential advantages include the ability to tailor dynamic behavior of manipulators and customize their interactions with the environment. In this paper, we leverage concepts from compliant mechanisms to develop several candidate mechanisms for integrating variable-stiffness behavior into robotic end effectors, and compare and contrast their relative merits.
An Underwater Gliding Robot Using an Origami-Inspired Buoyancy Regulator
Authors: Ojha Pritam , Saxena Shuchi , Thakur Atul
Published: November 2024

Underwater robots require sophisticated buoyancy regulators for efficient three-dimensional motion. Conventional buoyancy control techniques include ballast-based and pressurized air bladders, which often disturb the surrounding water and are noisy. This paper introduces TOD-UWG, an underwater gliding robot equipped with a noiseless and fixed-mass buoyancy regulating mechanism. This technique employs a swim bladder made with origami. The origami-based swim bladder is compressed using a thread and pulley mechanism actuated by a servo motor, while the stretching takes place when the tension in the thread is relieved. The study focuses on the feasibility of buoyancy control using a soft origami structure, ensuring efficient actuation while reducing energy consumption. We also present the details of selecting suitable materials with a focus on durability and compatibility with the origami design principles, guaranteeing the long-term functionality and reliability of the entire system against water seepage for shallow water applications. Experimental characterization of buoyancy force generated by the origami mechanism has been performed, and it is used to calibrate the robot. We envisage that the developed approach can impart buoyancy regulation-based energy-efficient depth control capabilities to underwater robots that can reduce environmental impact.
Random Wave Energy Conversion of a Spar-Floater System via the Inerter Pendulum Vibration Absorber Integration
Authors: Li Guoxin , Tai Wei-Che
Published: November 2024

Due to the vast potential energy of ocean waves, research on ocean wave energy conversion setups is currently receiving significant attention. However, the high costs of installation and maintenance, combined with the relatively low energy conversion efficiency of current research devices, pose considerable challenges. One solution is to integrate wave energy converters (WECs) with existing offshore floating platforms. This paper studies a system integrating a floater WEC with a floating spar platform via the inerter pendulum vibration absorber with a power take-off mechanism (IPVA-PTO). By simulating its energy harvesting performance, considering drag force and under random ocean wave excitation, where ocean waves are generated using the Harmonic Random Amplitude (HRA) method and hydrodynamic coefficients are derived based on linear wave theory and the boundary element method, it is found that the IPVA system, while achieving comparable levels of hydrodynamic response of the spar, can enhance energy conversion by up to 106.37% compared to the optimal system with linear PTO. The simulation results qualitatively correspond with the bifurcation analysis of the system, conducted after solving for a series of Fourier coefficients, including those for drag damping, under regular wave conditions, providing insightful references for analyzing the performance subjected to random wave excitation.
ParaturboCAD: Automated Parametric Geometry Construction for Gas-Bearing Supported Turbocompressor Design With Python
Authors: Massoudi Soheyl , Bejjani Joseph , Horvath Timothy , Üstün Dogukan , Schiffmann Jürg
Published: November 2024

This research introduces an automated methodology for visualizing gas-bearing supported turbocompressor designs utilizing CadQuery 2, a scripting Computer-Aided Design (CAD) platform. This innovative approach simplifies the traditional design process, which often involves the use of multiple software tools and extensive manual data manipulation, by consolidating it into a singular, efficient, and robust workflow. Moreover, CadQuery 2 enables the rapid visualization of abstract optimization results, enhancing the design phase’s efficiency. A pivotal element of this study is the adept transformation of design variables related to key components such as the rotor, compressor, and bearings into precise three-dimensional turbocompressor models. This intricate procedure is expedited by employing a structured Python dictionary, which serves to encapsulate the geometric parameters of each component comprehensively. The utility of this framework is demonstrated through the creation of turbocompressors featuring diverse geometries, highlighting the methodology’s capacity to produce models with high accuracy and within a reasonable generation timeframe of approximately seven minutes per turbocompressor. While there are minor constraints, notably in parametrization choices and the time efficiency of modeling with CadQuery 2, these do not significantly detract from the method’s overall value. Indeed, this approach represents a substantial advancement in the field of design and manufacturing, promising to refine and expedite the development process of these complex systems.
Dual Lie Algebra Representations of Rigid Body Motion With Dual Cayley Maps: An Overview
Authors: Condurache Daniel
Published: November 2024

The main objective of this research is to develop a new minimal parameterization technique for the displacement and motion of rigid bodies using hypercomplex dual algebra. Our study is based on the properties of dual tensors and dual quaternions, more precisely, their Lie groups and algebras. Based on the higher-order modified fractional Cayley transforms, for the first time, a complete and unitary parameterization framework, which gives the possibility of developing direct unitary solutions for the calculation of the leading entities of kinematic representation of displacement and motion of rigid body: Euler dual vector, higher-order Rodrigues dual vector, dual quaternions, dual sin family parameters and dual orthographic projection. The representation is coordinate-free and in a closed-form.
A Basic Formal Ontology-Based Ontological Modeling for Plan and Occurrence, a Biomanufacturing Process Verification Use Case
Authors: Šormaz Dušan , Seeharit Saruda , Kulvatunyou Boonserm , Drobnjaković Miloš
Published: November 2024

Biomanufacturing has gained significant importance in recent years due to its role in developing new medications, handling pandemics, and increasing the well-being of human populations. The nature of biochemical processes requires complex planning and control, with many controlled and non-controllable variables that impact the quality of bioproducts. Representing biomanufacturing process knowledge, control models, and actual occurrences in coherent ontologies could aid both humans and computers in dealing with the complexity. However, there is a lack of such coherent ontologies. Even though the Industrial Ontology Foundry (IOF) Core ontology has provided a groundwork based on the widely used Basic Formal Ontology (BFO) for such ontological requirements, there are still insufficient constructs and clear guidance on the representation of digital artifacts and their correspondences to the physical counterparts. This paper presents a framework to extend the IOF Core to address the gap. The framework is founded on establishing a counterpart (CR) relation pattern presented in our previous paper. Counterpart relation was selected for its ability to facilitate a more intuitive and concise representation of many kinds of digital artifacts (e.g., planned, designed) and physical entities (e.g., planning process, manufacturing process). We validated the approach with a process verification of a fed-batch bioreactor operation. The paper started by defining the use case requirement, which was followed by an ontology development. A knowledge graph of the bioprocess plan and occurrences of processes in the plan was then instantiated. Competency questions were used to concretize the ontology requirement from the use case, and subsequently, an executable set of queries was created from them and was used to computationally validate the ontology against the requirement. The GraphDB tool was used to support the validation. The result of this research not only showed that the CR pattern described in our previous paper could satisfy the requirements related to the digital thread of digital and physical process information, but it also demonstrated that several visualization approaches on graph data can be used to address competency questions. These findings provide insights into the future of data integration and management within biomanufacturing, highlighting the role of ontologies for improved data interoperability and analysis.
Barriers to Product Repair: Exploring Motivations and Capabilities Among Operators
Authors: Karsli Sami , Otto Kevin , Li Wen , Bilton Amy , Hölttä-Otto Katja
Published: November 2024

This study explores barriers to product repair from the perspective of common ‘do-it-yourself’ users and commercial operators. Through a systematic literature review, we identify and compare repair barriers faced by different operators. Our findings highlight that operators predominantly struggle with limited capability (e.g., lack of skills, tools, spare parts) and motivation (e.g., economic motivation and environmental motivation) to repair. In comparison, commercial operators show greater capability than common users but struggle with the economic motivation of repair due to the necessity of financially compensating their labor. In contrast, common users exhibit a lower capability but benefit from additional motivating factors, such as reduced labor costs and product attachment.

A subsequent case study illustrates how these barriers manifest within the repair procedure of an exemplary product. Further, we show how the varying capability and motivation among operators constrain the range of viable repair strategies.

Navigating this interplay between repair operator and repair viability holds the potential to assess and enable targeted repair strategies for a diverse range of operators, thereby promoting product repairability and circularity.
Manufacturability of Protein-Reinforced Foods With Overhang Designs
Authors: Khalil Md Ibrahim , Maleky Farnaz , Pal Ranadip , Egan Paul F.
Published: November 2024

Printing overhanging designs in additive manufacturing presents considerable challenges, particularly for soft materials printing applications such as food. The principal hurdles lie in designing a food ink that ensures extrudability while also maintaining its shape after printing. This study investigates the impact of overhanging angles on the shape fidelity of printed samples and how the properties of the food ink composition influence structural stability. The investigation focuses on overhanging designs at various angles (0°, 30°, and 60°). Different ratios of mashed potato and pea protein mixtures were studied including 100% mashed potato, a blend of 50% mashed potato and 50% pea protein, and 100% pea protein mixtures. The food ink mixtures were characterized by measuring pH, moisture content, and firmness. Results showed the potato-only mixture (86% moisture content, 0.55 N firmness) exhibited significant shape distortion at higher overhanging angles. However, the equal percentage mixture of mashed potato and pea protein mixtures (81% moisture, 1.46 N firmness) maintained fidelity even at 60° angles. The enhanced dimensional precision of the potato-pea mixture demonstrates how tailored compositional tuning, informed by moisture and texture analysis, can improve shape fidelity for 3D printed overhanging geometries through improved structural stability.
Surfaces of Constant Width With Rotational Symmetry
Authors: Rollans Mason , Nelson W. Seth , Howell Ryan , Monk Ian , Halverson Denise
Published: November 2024

Surfaces of constant width are defined as surfaces embedded in three-dimensional space having the same width in all directions. While spheres are the simplest and most common surface of constant width, non-spherical surfaces of constant width possess unique properties. For example, surfaces of constant width have less volume than spheres of the same width, potentially leading to increased efficiency in designs. In addition, surfaces of constant width typically have a finite number of stable positions. Moreover, surfaces of constant width may be comprised of piece-wise smooth surfaces, having variable curvature. Due to these distinctive qualities, these types of surfaces have applications in various fields including architecture, mathematics, engineering, and mechanism design. Historically, it has been difficult to construct these surfaces and the number of methods of construction is limited, although recently, novel and creative construction methods have been conceived. This work presents the construction of a new class of surfaces with rotational symmetry of order n, denoted Roll[n], where n > 1 is odd. The design method for these surfaces is rooted in the constant width properties of parallel projections of surfaces of constant width. Existing surfaces of constant width can be classified by orthogonal parallel projections and rotational symmetry. The new Roll[n] surfaces fill in previous gaps in classifications these classification schemes.
Life Cycle Analysis of Three-Story Mass Timber and Reinforced Concrete Structures
Authors: Brown David A. , Enarevba Dolor R. , Haapala Karl R.
Published: November 2024

Sustainability initiatives in the construction industry are steadily growing due to the negative impacts of the conventional materials used in this sector. Employing more environmentally friendly materials like mass timber based on its availability in the region, geographical weather conditions, existing technologies, and policies has gained research interest in industry and academia. Previous work exploring the environmental impacts of mass timber structures highlighted specific aspects of building life cycles, including material sourcing and transportation, while failing to discuss structures built with alternative materials. A cradle-to-grave comparative life cycle analysis was conducted for three-story mass timber and reinforced concrete structures in the Pacific Northwest. An open-source life cycle assessment (LCA) software (openLCA) was utilized to evaluate the environmental impacts, with input data and process models from the openlca_lcia_methods database. Mass timber components as an alternative to reinforced concrete exhibited lower impacts on global warming potential, terrestrial acidification, and human carcinogenic toxicity. Utilizing mass timber demonstrates a 33% decrease in greenhouse gas emissions (GHGs). Future advancements of this work will employ a more comprehensive LCA tool, database, and method to explore substituting other renewable and biobased materials for non-load bearing applications to evaluate and improve the sustainability performance of building structures for commercial and residential applications.