bluuebunny/crossref_metadata_2025 huggingface dataset:
Quality & Error Tendencies of Gears Printed by Stereolithography Three-Dimensional Printer
Authors: Duan Kaiwen , Osawa Keisuke , Tanaka Eiichiro
Published: November 2023

To ensure the high quality of three-dimensional (3D) printed spur and helical gears, it is important to measure and identify errors in the base pitch introduced during the printing process. This study utilized the span measurement method to quantify the errors in the base pitch of Stereolithography apparatus (SLA) printed gears. Results showed that errors in z-overcure and shrinkage caused a series of tendencies that can guide the production of high-quality gears. Additionally, the study discussed the importance of choosing a suitable printing posture, which was determined through precision checks. Since 3D printed gear is not as symmetric as the traditional manufactured gear, the base pitch on both the left and right sides will be different, hence some explanations about it were provided along with the example. By understanding and addressing errors in the base pitch of 3D-printed gears, manufacturers can produce high-quality gears that meet the needs of various applications.
Supporting Condition-Based Maintenance for Rotary Systems Under Multiple Fault Scenarios
Authors: Marshall Larry , Jensen David , Hu Han
Published: November 2023

This paper aims to conduct an evaluation of a range of supervised learning algorithms, which are utilized for the purpose of classifying single and multi-fault scenarios associated with rotary systems. Furthermore, the paper aims to develop a proof-of-concept prototype for in-situ fault detection and classification. The contemporary industry has been moving away from scheduled-based maintenance to condition-based maintenance, primarily due to the enhanced cost savings and flexibility offered by the latter. However, to achieve condition-based maintenance, it is crucial for operators to have access to fast and accurate methods of component fault detection and classification. Presently, various techniques are employed by companies for classifying machinery faults, which primarily involve a binary classification problem that focuses on the determination of whether a particular component is healthy or not. While this approach is useful, it often results in a loss of valuable information such as knowledge pertaining to the degraded component, the extent and location of degradation, and other critical details that can help companies make more informed decisions. By having access to more information, companies can become more fault-adaptive and can modify their processes and sequences to maintain product throughput. In order to achieve this, fault classification must occur at the system level, rather than solely through specific component monitoring. In the rotary machine domain, researchers primarily concentrate on the classification of single ball-bearing faults. However, little research has been conducted on the classification of multiple faulted bearings, warped shafts, or combinations of these scenarios, all of which are major and very plausible fault scenarios. This paper attempts to address these gaps by proposing a low-cost method for in-situ multi-fault classification. The approach aims to provide an effective solution for enhanced condition-based maintenance and improved decision-making in the rotary machine domain.
Effects of Wave Energy Converter Array Configurations and Geometries on Wave Field and Power Output
Authors: Vitale Olivia , Haji Maha
Published: November 2023

Wave energy converters (WECs) have the potential to be major players in the renewable energy sphere. WEC arrays are well suited for grid-scale energy, offshore aquaculture, water desalination, coastal communities, and other niche applications. Understanding the effect of an array on the surrounding wave field is crucial to accurate modeling, determining interactions between each individual WEC, and finding power output. Array configuration and WEC geometry can alter power output, either constructively or destructively. This study models different configurations and WEC geometries in a 4-WEC array using a Boundary Element Method (BEM) solver. The geometries considered are heaving sphere and cylinder point absorbers. The software considers the bodies to be fixed while altering the incoming wave field. The calculations analyze the perturbed wave field and power output by determining radiation and diffraction effects from the array as well as body motion. Two power take-off (PTO) schemes are considered to analyze energy extraction. Obtained results show that regular arrays (1 × 4) of all geometric configurations tend to have milder effects on the wave field. When mixing geometries in the 1 × 4 arrays, similar levels of disturbance are found, but the wake lacks symmetry. Arrays with multiple rows (2 × 2) display higher disturbances. Inline array configurations, where WECs are placed directly behind each other relative to the incoming waves, are most sensitive to varying geometries. All regular arrays demonstrated constructive effects on power output, while the staggered configurations displayed the greatest degree of destructive effects. To extract the most energy, an optimized spring-damper is preferred, but this is more challenging to realistically implement. If the designer’s primary goal is increasing array efficiency, the regular array formation is most favorable. However, if the designer looks to decrease ocean space used, potentially share mooring lines, and produce meaningful energy, the inline formation is a reasonable choice.
Kirigami Corrugations: Strong, Modular, and Programmable Plate Lattices
Authors: Parra Rubio Alfonso , Mundilova Klara , Preiss David , Demaine Erik D. , Gershenfeld Neil
Published: November 2023

Plate lattices are high-performance lightweight structures, exhibiting up to twice the yield strength and stiffness compared to truss lattices of similar geometric arrangement and relative density. Although they are of great interest for research and structural engineering applications, their complex manufacturing and assembly processes limit their practical use, with sandwich panels being an exception. This paper presents a novel approach to the design and modular assembly of folded custom 3-dimensional plate lattices as structural corrugations for use in structural engineering and robotics applications. The plate lattice structural corrugation uses a building block strategy and incorporates custom modified unit cells based on the Miura-ori. This transformation involves expanding the top and bottom zig-zag crease lines into facets and orienting them in space. The resulting modified pattern is referred to as the Kirigami Expanded Miura. The unique structure of these lattices not only provides exceptional mechanical performance as static structures, but also allows for the design of anisotropies in their flexural stiffness by alternating between the Maxwell criterion on bending-dominated or stretchdominated cells. These anisotropies can have value differences of up to 24 with the same geometry, making them ideal for robotic morphing applications. We validate our proposed technology by characterizing the mechanical performance of this new building system and comparing it with state-of-the-art corrugations. We demonstrate the potential of this approach by designing, manufacturing, and modularly assembling multiple structures and robots with single and double curvature.
Quantifying the Sustainability and Robustness of Manufacturing Systems Using Energy and Ecological Network Analyses
Authors: Hassan Hadear , Payne Emily , Layton Astrid
Published: November 2023

Global issues, such as supply chain disruptions, have increased awareness of the importance of manufacturing systems being able to quickly bounce back from disturbances. This necessary response is in addition to the importance of mitigating climate change, maintaining market competitiveness, and eliminating unnecessary waste. Two analysis types are compared here: 1) a thermodynamic exergy analysis to quantify a manufacturing system’s energy and material efficiency and 2) an ecological network analysis as a quantitative representation of the system’s sustainability and robustness. Several manufacturing structures, including different processes ranging from the traditional to advanced, like injection molding and binder jetting, are examined in terms of the system response to changes. The findings indicate that the thermodynamic approach efficiently evaluates the efficacy of energy and resource conversion to create a final product. The ecological network approach was also found to provide useful insights on both the environmental efficiency of the systems as well as the resilience. These results are useful when combined for suggesting system layouts and operations that holistically improve a manufacturing system’s design. The findings indicate that existing manufacturing infrastructure needs to be redesigned to better withstand and recover from unforeseen disruptions. Introducing features such as recyclability and combining multiple types of manufacturing processes can enhance the overall resilience of the system. The work suggests that the bio-inspired systems analysis approach when coupled with connectivity and energy-related factors can lead to enhanced manufacturing system designs.
Early Prediction of Human Intention for Human-Robot Collaboration Using Transformer Network
Authors: Zhang Xinyao , Tian Sibo , Liang Xiao , Zheng Minghui , Behdad Sara
Published: November 2023

Activity recognition is a crucial aspect in smart manufacturing and human-robot collaboration, as robots play a vital role in improving efficiency and safety by accurately recognizing human intentions and proactively assisting with tasks. Current human intention recognition applications only consider the accuracy of recognition but ignore the importance of predicting it in advance. Given human reaching movements, we want to equip the robot with the ability to predict human intent not only with precise recognition but also at an early stage. In this paper, we propose a framework to apply Transformer-based and LSTM-based models to learn motion intentions. Second, based on the observation of distances of human joints along the motion trajectory, we explore how we can use the hidden Markov model to find intent state transitions, i.e., intent uncertainty and intent certainty. Finally, two data types are generated, one for the full data and the other for the length of data before state transitions; both data are evaluated on models to assess the robustness of intention prediction. We conducted experiments in a manufacturing workspace where the experimenter reaches multiple scattered targets and further this experimental scenario was designed to examine how intents differ, but motions are only slightly different. The proposed models were then evaluated with experimental data, and further performance comparisons were made between models and between different intents. Finally, early predictions were validated to be better than using full-length data.
A Geometric Method for Static Balancing of Spatial Mechanisms Using Springs
Authors: Wang Jieyu , Kong Xianwen , Tian Yingzhong
Published: November 2023

This paper presents a geometric method for static balancing of spatial mechanisms using only springs. The method applies to mechanisms composed of revolute (R) joints with arbitrary parameters. This method leads to systems with constant potential energy, including the potential energy associated with gravity and the elastic potential energy stored in the springs, hence make the systems more energy-efficient. In the literature, the static balancing of the 1-link manipulator and spatial manipulators without the consideration of the intermediate link weights has been discussed, using the algebraic method which needs lots of derivations or auxiliary parallelograms. This paper is to provide an efficient way to construct statically balanced spatial mechanisms with all the link weights considered. The proposed method is a geometric approach and can achieve static balancing without or with few derivations. In addition, an auxiliary mechanism with 3-degree of freedom (DOF) translational motions is introduced for the accommodation of the springs. In addition, the method can be applied to mechanisms with multiple modes. An 8R linkage and a 3-4R parallel mechanism with multiple motion modes are presented as examples to illustrate the proposed method. The method of equivalent virtual center of mass is also adopted for the 3-4R parallel mechanism to reduce the number of springs.
Measuring the Health of Makerspaces During Large Disruptions Such As the COVID-19 Pandemic
Authors: Crose Claire , Blair Samuel , Layton Astrid , Linsey Julie
Published: November 2023

As the popularity of makerspaces and maker culture has skyrocketed over the past two decades, numerous studies have been conducted to investigate the benefits of makerspaces for university students and how to best establish an inclusive, welcoming environment in these spaces on college campuses. However, unprecedented disruptions, such as the COVID-19 pandemic, have the potential to greatly affect the way that students interact with makerspaces and the benefits that result. In this study, a survey asking about prior makerspace involvement, tool usage, and student demographics was administered to students who use academic makerspaces at two large public universities. Survey data was collected for three semesters (Fall 2020, Spring 2021, and Spring 2022) and spanned both during and after the height of the COVID-19 pandemic. To quantify the differences between the semesters, nestedness and connectance metrics inspired by ecological plant-pollinator networks were utilized. These ecological metrics allow for the structure of the interactions of a network to be measured, with nestedness highlighting how students interact with tools and connectance with the quantity of student-to-tool interaction. The network analysis was used to better gauge the health of the makerspace and the type and frequency of interactions between tools. The raw survey data combined with the ecological metrics provided unique insight into the struggles the makerspaces encountered throughout the pandemic. It was found that nestedness, a measure of system stability, decreases with a decrease in tool usage. Additionally, the higher the connectance the more students interacted with the space. Utilizing metrics such as these and better understanding student tool interactions can aid makerspaces in monitoring their success and maintaining a healthy and welcoming space, as well as tracking the current health of the space. In combination with the survey results, a deep understanding of what challenges the space is facing can be captured.
Finite Element Contact Analysis and Redesign of Rotorcraft Planetary Gear Sets With Asymmetric Profiles
Authors: Marambedu Karthikeyan , Hurrell Michael , Vijayakar Sandeep , Krantz Timothy , Valco Mark
Published: November 2023

This article explores the use of asymmetric gear teeth for rotorcraft main rotor planetary gearsets. Two baseline designs having symmetric gear teeth were selected for redesign studies. Finite element-based contact analyses were used to compare designs. Bending stress predictions for the two existing baseline designs were compared to strain gage measurements to confirm the suitability of the modeling. The same modeling method is then used to assess design changes to improve the bending stress and contact pressure magnitudes using asymmetric tooth profiles. For the first redesign study, the selected gearset is mildly asymmetric, with a 4° difference in pressure angles between the two sides of a tooth. For the second redesign study, the selected gearset is strongly asymmetric with a 18° difference in pressure angles. The results demonstrate that the asymmetric tooth approach provides additional design flexibility toward balancing the contact stresses and bending stresses of the sun, planet, and ring members.
Theoretical Piezoelectric Energy Generation From Thoracic Expansion During Respiration for Smart Wearables
Authors: Carlson Sam , Martini Angela K. , Clarke-Sather Abigail R. , Alves Paulo H.
Published: November 2023

Energy generation and storage is vital to power many sensors used in research and industry. The process by which energy is generated and stored can have critical impacts on the environment. Piezoelectric energy harvesting is one method of renewable energy generation that can have reduced environmental impacts compared to other non-renewable forms of electricity generation. Additionally, it is capable of being portable, which is ideal for powering smart wearables. The respiratory rate monitoring garment is a smart wearable that monitors the breathing rate of the wearer. Piezoelectric energy harvesting was explored as a power source for the respiratory rate monitoring garment by initial theoretical calculations of potential maximum voltage output from the wearer’s chest motion during respiration to improve the portability of the design. The constitutive equations in combination with a simple circuit representation of the piezoelectric element in series with the respiratory rate sensor was used to generate equations to calculate the maximum voltage available during normal respiration. The maximum voltage during one minute of simulated respiration was determined to be 3.3 mV, which is much lower than the necessary 6.8–7.15 V needed to power the respiratory rate monitoring garment. To make piezoelectric energy harvesting suitable for the respiratory rate monitoring garment, the voltage demands of the microcontroller and LoRa wireless transceiver would have to be significantly lowered, or a new method of data collection would have to be developed. While it was determined that piezoelectric energy harvesting could not provide enough voltage for the existing design of the respiratory rate monitoring garment, it may be suitable for other low voltage sensors or applications.
Effect of Reflection and Incubation on Artificial-Intelligence-Assisted Design Space Exploration
Authors: Song Hyeonik , Viros-i-Martin Antoni , Selva Daniel
Published: November 2023

Design space exploration is a method for gaining insights into a space of design alternatives to make important design decisions. Current research in design space exploration is studying the use of Artificial Intelligence (AI) agents (called cognitive assistants) to provide cognitive support to human designers and help them obtain insights from large datasets. This work explores the collaborative exploration of design spaces between human designers and AI cognitive assistants. A human subject study was conducted to 1) investigate whether cognitive interventions during a design task, specifically a question-based reflection period or an incubation period, influence the way designers collaborate with cognitive assistants; and 2) characterize the effects of those interventions on the design space exploration process and outcomes (performance and designer learning). The study aims to determine whether cognitive interventions that prompt designers to reflect on their collaboration with the AI agent could improve the quality of their collaboration. The results indicate that the two intervention methods have opposing effects on design performance. Participants who had an incubation period amid design space exploration improved their design performance significantly more than participants who had a self-reflection period amid design space exploration. Participants who had different interventions during the design task exhibited different forms of human-AI collaboration which possibly explain the opposing effects of reflection and incubation periods on their design performance. The findings could serve as a basis for the development of AI-assisted design tools that guide designers toward more effective human-AI collaboration.
Study the Impact of Cross-Sections on the Energy Absorption of Architected Materials
Authors: Wang Xinnian , Rastegarzadeh Sina , Pan Yayue , Huang Jida
Published: November 2023

Advances in additive manufacturing (AM) facilitate the fabrication of as-designed architected metamaterials with complex cellular structures. The mechanical properties of these materials can be tailored by their design, i.e., the geometries of the cellular structures, which results in a design-by-demand paradigm and can be employed in a broad range of tasks such as lightweight and energy absorption applications. Despite the extensive studies on designing the geometries of cellular structures, limited works have investigated the impact of cross-sections on the final properties of architected materials. The cross-section is critical since the cellular structures are connected with repeated unit cells, and detrimental stress concentrations are found at all intersections of structural members, which leads to low strength and poor recoverability. In this work, we investigate how the cross-sections of cellular structure affect its energy absorption performance. Specifically, we studied both bending and stretching-dominated cellular structures: five typical strut-based cells were chosen, and each structure is designed with four different types of cross-sections — circular, square, diagonal-square, and rectangular. The structure specimens are printed with lab-developed stereolithography (SLA) printer. Physical compression tests are conducted, and the impacts of cross-sections on final properties are compared and discussed. Designed strategies based on the experimental results are summarized.
Gearbox Fault Detection via Physics-Informed Parallel Deep Learning Model Architecture
Authors: Zhou Qianyu , Tang Jiong
Published: November 2023

Ensuring the safety and reliability of mechanical system significantly hinges on accurate fault diagnosis of critical components comprising the system. Automating the diagnosis process can enhance efficiency and consistency of accuracy in the diagnosis stage. Deep learning methods tend to be playing increasingly indispensable roles in the establishment of the automated fault detection framework; however, they consistently require abundant data resources to leverage the uninterpretable features for fault detection tasks, which is posing major hindrances especially for situations where data collection for fault scenarios is expensive. Moreover, the physically unexplainable feature extraction process renders the nongenerative data augmentation preferrable by feeding the raw information repetitively without being informed of physics integrated in the data. The opaqueness of model greatly hinders the efficient exploitation of the raw data. To address these challenges, it is proposed in this research that the physics-informed parallel model architecture can accommodate miscellaneous physically meaningful features in addition to the raw information. Specifically, the scalogram extracted by continuous wavelet transform is designated to be input together with raw data to the binary branches respectively in the parallel deep learning model architecture. By incorporating the extra interpretable features and information, our approach can completely leverage the data of restricted availability and render the model training physics-guided and understandable. The proposed algorithm is implemented on gear fault experimental data acquired from laboratory testbed to exhibit the physicsguided training process and the outstanding performances on varying size of data.
Design Verification of a Multi-Modal Compliant Mechanism Intended to Enhance Safety in Handheld Laparoscopic Tissue Grasping
Authors: Aguirre Milton E. , Fernández Montoya Mauricio , Gallego Sanchez Juan Andres , Kommuri Krishna D.
Published: November 2023

This work shows preliminary results on the process of design verification for a multi-modal compliant mechanism using computational and experimental comparisons. The technology solution replaces the conventional stiff-hinged tooltip of a laparoscopic surgical grasper with a monolithic contact-aided compliant mechanism tooltip that exhibits multi-modal stiffness properties for enhancing haptic feedback and safety during soft tissue grasping. A finite element model is developed to predict the force transmission behavior of the compliant mechanism undergoing large deformations, beam buckling, and intentional contact during its operational conditions. The authors define and compare metrics of mechanism performance (actuation force and pinch force as a function of input displacement) to verify the accuracy of predicting these nonlinear behaviors using Finite Element Modeling. The computational predictions show a −8.89% change in actuation force between the first and second stiffness regions and +70.60% change between the second and third stiffness regions, as compared to 3.69% and +90.32% corresponding to the experiments, respectively. The computational predictions of pinch force show a change of −21.57% versus a change of −21.52% for the experiment between stiffness regions 1 and 2; and a change of +17.09% compared to +15.19% between stiffness regions 2 and 3 for the simulation and experiments, respectively. Overall, the computational tool was capable of modeling the nonlinear behaviors of this multi-model compliant mechanism. Future work will refine the computational model to improve prediction accuracy and study the influence of physical and geometric parameters on mechanism performance.
Sensitivity Analysis of Tension Distribution Algorithms for Cable-Driven Parallel Robots
Authors: Di Paola Vincenzo , Caro Stéphane , Zoppi Matteo
Published: November 2023

Cable-Driven Parallel Robots (CDPRs) constitute a class of robots which displace a load by means of cables, pulleys and electric motors. Since cable tensions have to be bounded above and below, their use to guide the end-effector continues to be a challenging task. Indeed, several Tension Distribution Algorithms (TDAs) have been developed so far to properly steer the load. Although comparisons between them have been made, no performance index is established to evaluate numerically such methods. Therefore, this paper aims at defining a performance index establishing the sensitivity of a TDA by looking at the maximum tension variation that stems from a change in the external wrench. This would help both comparing and choosing the proper TDA among the existing ones. The definition of the index pops out naturally from the linearization of the equations defining the optimization problems. Hence, in this paper, commonly used TDAs are reproduced and then compared using the defined index. In order to show an application of it, a planar CDPR with four cables and a point mass load is considered. The simulations aim at characterizing its Wrench-Feasible Workspace (WFW) by means of the sensitivity index hereby defined. This will allow quantitative comparisons between above-mentioned TDAs.
Understanding the User Perception Gap: Older Adults and Sit-to-Stand Assistance
Authors: Stansfield Stephan , Schelhaas Booker , Hogan Neville , Yang Maria
Published: November 2023

There is an urgent need to provide ways to help a fast-growing older adult population to maintain daily mobility. A great deal of work exists in medical devices and robotics to generate effective assistive solutions, yet at the same time, limits have been observed in the adoption of such systems. In this paper we explore possible factors in adoption from a user-centered design perspective. We investigated user needs surrounding the act of standing up from a seated position and older users’ attitudes toward assistive device prototypes.

Older adults completed a standard timed-up-and-go mobility assessment, rated their own difficulty standing, participated in interviews, and shared responses to “look-and-feel” prototypes, all in an effort to uncover latent needs. A licensed physical therapist rated videos of the subjects while standing up and the two ratings were compared. While the physical therapist’s rating of difficulty increased as subjects’ performance on the clinical mobility assessment worsened, subjects’ self-ratings did not significantly correlate with mobility performance, even when timed-up-and-go performance indicated a risk of falling.

Subjects expressed preferences for potential assistive devices that were more discreet, lightweight, and flexible over devices that were bulkier, heavier, and rigid. In general, subjects’ attitudes toward assistive devices for their own sit-to-stand use were similar regardless of their demonstrated need. The results highlight the challenges designers may face when creating products for older adult users and underline the importance of a user-centered design process. Implications for assistive technology design are discussed.
Design and Optimization of Quasi-Periodic Volumetric Microstructures by Aperiodic Tiling and Implicit Function
Authors: Wang Jun , Rai Rahul
Published: November 2023

Materials with intricate microstructures exhibit unique properties, including a large surface area and a highly distributed load path. This makes them very effective in engineering applications where weight reduction and structural strength are critical. In this paper, we propose two different frameworks to model quasi-periodic microstructures. The quasi-periodic structure can be formed following aperiodic tiling rules and isoparametric transformation, which infills single-type unit cells into the aperiodic hexahedral tiles. For the other method, the proposed implicit function-based representation provides compact, precise, and arbitrarily parameterized models of coherent microstructures, which contain more than one type of unit cell that follows the cubic periodic repeating manner. Compared to periodic structures that possess perfect regularity and non-periodic structures in a disordered random manner, a quasi-periodic structure shows a perfect long-range order with short-range variability, which overcomes the shortcomings of periodic structures with limited design space and non-periodic structures with unmanageable random style. This paper also demonstrates the practicability of the proposed quasi-periodic structures by utilizing a surrogate model-based optimization scheme to design lightweight but high-strength functional parts with the optimized quasi-periodic structures filled internally.
Design of Self-Organizing Systems Using Multi-Agent Reinforcement Learning and the Compromise Decision Support Problem Construct
Authors: Jiang Mingfei , Ming Zhenjun , Li Chuanhao , Mistree Farrokh , Allen Janet K.
Published: November 2023

How can multi-robot self-organizing systems be designed so that they show the desired behaviors and are able to perform tasks specified by designers?

Multi-robot self-organizing systems, e.g., swarm robots, have great potential for adapting when performing complex tasks in a changing environment. However, such systems are difficult to design due to the stochasticity of the system performance and the non-linearity between the local actions/interaction and the desired global behavior. In order to address this, in this paper we propose a framework for designing self-organizing systems using Multi-Agent Reinforcement Learning (MARL) and the compromise Decision-Support Problem (cDSP) construct.

The design framework consists of two stages — preliminary design and design improvement. In the preliminary design stage, MARL is used to help designers train the robots so that they show stable group behavior for performing the task. In the design improvement stage, the cDSP construct is used to enable designers to explore the design space and identify satisfactory solutions considering several performance indicators based on the trained system established in the previous stage. Between the two stages, surrogate models are used to map the relationship between local parameters and global performance indicators utilizing the data generated in preliminary design. The surrogate models represent the goal functions in the cDSP. A multi-robot box-pushing problem is used as an example to test the efficacy of the proposed framework. The framework is general and can be extended to design other multi-robot self-organizing systems. Our focus in this paper is in describing the framework.
Transfer Reinforcement Learning: Feature Transferability in Ship Collision Avoidance
Authors: Wang Xinrui , Jin Yan
Published: November 2023

The integration of artificial intelligence into engineering work has become increasingly prevalent. Engineering work processes can be highly complex, and learning from scratch requires large computation resources. Transfer learning has emerged as a promising technique for improving learning efficiency by leveraging knowledge gained from related tasks to the target task. To achieve optimal performance, one of the key challenges is to figure out how transferrable the features are among different work processes and within training networks. Simulation-based ship collision avoidance is used for case studies due to its inherent complexity and diversity. Two transfer reinforcement learning methods, feature extraction, and finetuning, are implemented and evaluated against the baseline. Instead of introducing large-scaled pre-trained models as the backbone, a light CNN model pre-trained in a related base case has been proven to transfer essential features to target cases. Simplified ship dynamics is introduced into the training process to make it more realistic and applicable, and the delay caused by the large moment of inertia is addressed by modifying the model-environment interaction mechanism. Work process features for the ship collision avoidance process are concluded from crucial aspects. The effects on transferability are displayed by experimental results discussed from the feature category and similarity perspective.
Mechanism Designs of a Robot for Charging Multiple Electric Vehicles
Authors: Gao Dalong
Published: November 2023

Automobile industry is experiencing a major transformation into the era of electric vehicles. General Motors Global Research and Development Center developed a concept robotic system to charge multiple electric vehicles. This work demonstrated the process of designing the optimal robot mechanisms that can fulfill the functionalities of this system.

Due to the application specific constraints, this system demands a robot that can cover a very large workspace, enter narrow gaps between parked vehicles, and reach vehicle inlets at a large height span at the same time. To navigate among a large number of potential mechanism options and to identify the optimal designs without the overuse of actuators, a Key DOF was proposed for this application and the design process was unfolded based on the different realizations of the Key DOF. Our results showed that the system might have limited applications when using a 5 degree-of-freedom robot. Redundancies were evaluated for their benefits in this application. The first redundant joint could be used to achieve the long-range travel in the parking lot drive aisle direction. This redundancy enabled many designs and two were recommended for the application towards different volumed products. Additional redundancy in the robot mechanism didn’t bring in enough benefits for the application to justify the increase in cost and complexity.
Experimental Verification of Sobol Sensitivity Indices in a Space Latching Mechanism
Authors: Johnson Nathan , Roberts Christopher , Slaboch Brian , Voglewede Philip
Published: November 2023

Effectively and efficiently designing latching mechanisms is important for meeting precise design requirements of these mechanisms. This paper attempts to verify the use of a particular sensitivity measure, Sobol indices, to design latching mechanisms more systematically by applying it to a space mechanism. The latch studied was adapted from the Schmidt latch and was to be finely adjustable to test for the effects of small changes in the latch design. A simulation of the latch system yielded a set of Sobol indices which were compared to experimental results. The results were inconclusive due to larger than anticipated error when measuring the potential energy of the torsion springs in the latch, which made the effects of small design changes indistinguishable from noise in the system.
Using Machine Learning to Predict the Adoption of Building Electrification Technologies in US Households
Authors: Majowicz Andrew , Odonkor Philip
Published: November 2023

This paper explores the use of machine learning to predict the adoption of building electrification technologies within US households. This is important due to the increasing prevalence of building electrification as a pathway to addressing climate change, which inadvertently poses a threat to the energy resilience of households during power outages. A non-intrusive, data-driven means of predicting the level of technology adoption can help guide mitigation and adaptation strategies aimed at minimizing the risks vulnerable households may face when power outages are compounded by extreme weather events. This study develops machine learning models based on the energy consumption dynamics of US households to predict the presence of critical electric appliances, including furnace, water heater, induction stove, cooling system, and solar panels. The models are trained using a large dataset of building end-use load consumption for buildings located in New Jersey. The results show that the models are reasonably accurate in predicting the presence of appliances in homes, although there is still significant potential for improvement in model accuracy.
Evaluation of CAE Design Change Updates – A Case Study on Gas Turbine Airflow Disruptors
Authors: Procopio David , Morris John , Wagner John
Published: November 2023

The rapid growth of digitalization in design and advanced manufacturing, characterized by computer tools, has emphasized software tools throughout the process. To compete in the global marketplace, companies need to embrace virtual design and digital manufacturing methods. Computer-aided design and computer-aided engineering embody the workflow process and tools to bring forth products from conception to design, where detailed changes can be made to fabricate the product efficiently in the manufacturing phase. This paper examines three CAD/CAE patterning features used in Siemens NX on the design of gas turbine turbulators — internal turbine blade surface discontinuities that enhance the heat transfer between the cooling air and blades on whose outside surface are subject to hot combustion gases. Each patterning feature is used to produce a pattern with a high number of instances on a variety of surfaces. The simulations’ execution speed, memory usage, versatility, and integrity of the produced model is evaluated to quantify the performance of the three features. The global pandemic and supply chain challenges has shown the need for virtual modeling and engineering design platforms to efficiently complete complex projects and patterning methods applicable when replicating elements.
Effects of Gas Flow Rates and Sample Position on Carbon-Infiltrated Carbon Nanotube Growth
Authors: Ogilvie Grant , Harriman Isaac , Bowden Anton , Bowden Jared , Jensen Brian D.
Published: November 2023

Carbon nanotubes have been investigated for their antimicrobial properties. The goal of this experiment was to better characterize the effects of gas flow rate and sample position on carbon-infiltrated carbon nanotubes (CICNTs) grown via chemical vapor deposition (CVD) on a silicon substrate to allow for improved sample preparation for antimicrobial experiments. 18 batches of 3 samples each (52 samples) were prepared by CVD coating silicon wafer substrates with alumina (400 nm) and iron (6 nm). Standard processes were generated for creating CICNT samples, with carbon nanotube (CNT) growth at 750 C and subsequent carbon infiltration at 900 C. In each batch, samples were positioned linearly with samples located centrally, downstream, and upstream in the furnace. Three batches had −20% gas flows (both hydrogen and ethylene), three batches had standard gas flows, and three batches had +20% gas flows. The experiment was then repeated with hydrogen flow held constant (only the ethylene was varied). Results showed that gas flow rates and sample position had negligible effects on nanotube height and diameter. This means that minor changes in gas flow rates and sample position do not have outsize effects on CICNT synthesis, which indicates that appropriate nanotube coatings can be prepared over a wide range of growth conditions.
A Design Knowledge Guided Position Encoding Methodology for Implicit Need Identification From User Reviews
Authors: Han Yi , Moghaddam Mohsen
Published: November 2023

Aspect-based sentiment analysis (ABSA) enables a systematic identification of user opinions on particular aspects, thus enhancing the idea creation process in the initial stages of product/service design. Attention-based large language models (LLMs) like BERT and T5 have proven powerful in ABSA tasks. Yet, several key limitations remain, both regarding the ABSA task and the capabilities of attention-based models. First, existing research mainly focuses on relatively simpler ABSA tasks such as aspect-based sentiment analysis, while the task of extracting aspect, opinion, and sentiment in a unified model remains largely unaddressed. Second, current ABSA tasks overlook implicit opinions and sentiments. Third, most attention-based LLMs like BERT use position encoding in a linear projected manner or through split-position relations in word distance schemes, which could lead to relation biases during the training process. This article addresses these gaps by (1) creating a new annotated dataset with five types of labels, including aspect, category, opinion, sentiment, and implicit indicator (ACOSI), (2) developing a unified model capable of extracting all five types of labels simultaneously in a generative manner, and (3) designing a new position encoding method in the attention-based model. The numerical experiments conducted on a manually labeled dataset scraped from three major e-Commerce retail stores for apparel and footwear products demonstrate the performance, scalability, and potential of the framework developed. The article concludes with recommendations for future research on automated need finding and sentiment analysis for user-centered design.
An Advanced Method to Investigate and Manage the Stability of Autonomous Utility Trucks With the Morphing Boom Equipment Under Crosswind
Authors: Patel Parth Y. , Koomullil Roy , Vantsevich Vladimir
Published: November 2023

During natural disasters, such as hurricanes, tornadoes, earthquakes, and floods, utility trucks with boom equipment, also known as bucket trucks, are often the first responders in assisting in the rebuilding and recovery process in affected areas. Crew safety is critical while driving utility trucks in hazardous weather conditions to ensure efficient and effective service delivery to affected communities. Therefore, the utility truck’s stability while driving in extreme weather conditions is a crucial aspect of the safety of the rescue crew. This paper proposes an advanced novel method to investigate and manage the stability of autonomous utility trucks equipped with morphing booms under crosswind. The proposed method involves developing mathematical models to analyze the truck statically, simulating various crosswind scenarios to identify the stability issues, and implementing a solution to mitigate those issues by morphing strategies. The mathematical model considers crosswind effects on the truck, including the aerodynamic forces and moments, the lateral displacement, and the roll angle. The model then simulates different crosswind scenarios (varying wind speed and direction) and evaluates the truck’s stability and performance under different weather conditions. This study offers valuable insights for improving the stability and safety of an autonomous utility truck under diverse and challenging crosswind conditions.
Lock-In Control and Mitigation of Vortex-Induced Vibrations in Aircraft Wings Using a Conserved-Mass Nonlinear Vibration Absorber
Authors: Basta Ehab , Gupta Sunit K. , Barry Oumar R.
Published: November 2023

The impact of the vibration absorber on the synchronization region during vortex-induced vibration of turbine blades is investigated. This work is based on a 3DOF model, including a coupled plunge-pitch airfoil motion and a van der Pol oscillator to the fluid-structure interaction caused by the vortex shedding of the incoming flow. The aeroelastic system is increased by a degree of freedom, namely, the vibration absorber. Linear and nonlinear vibration absorbers are used in this investigation to analyze the effectiveness of the vibration absorber. To demonstrate the effect of the resonator on the lock-in, the coupled natural frequencies, numerical frequency responses, and time histories are plotted. The study reveals the promising capability of the absorber to reduce the lock-in region and mitigate the VIV amplitudes within these regions. For the current application, however, the nonlinear absorber response was indifferent compared to its linear counterpart for the given values of coupling coefficients. This observation indicates that a linear absorber efficiently shrinks the lock-in regions and mitigates the VIV in turbomachinery.
Recent Developments in Ontology Standards and Their Applicability to Biomanufacturing
Authors: Drobnjakovic Milos , Kulvatunyou Boonserm , Frechette Simon , Srinivasan Vijay
Published: November 2023

ISO and IEC have jointly initiated, and recently issued, a series of standards (the ISO/IEC 21838 series) for top-level ontologies. These standards have been used by industrial consortia to develop and disseminate standards for mid-level ontologies to ease the development of ontologies in specific domains and applications. This paper traces the motivation for these concerted efforts to the successful application of ontologies in biological sciences. It then analyzes these recently released international standards for top-level and mid-level ontologies, and shows how they can be used for domain-level and application-level ontologies. Finally, it provides a rationale for applying these standards to the important and emerging field of biomanufacturing.
A Functional Perspective on the Emergence of Dominant Designs
Authors: Robinson Myles , Taube-Adams Bailey , Kang Samantha , Dong Andy
Published: November 2023

Models of long-term product innovation depict the trajectory of products through an evolutionary selection metaphor in which product designs converge toward a dominant design. The product innovation literature favors trajectory descriptions based on the physical manifestations of products while neglecting to account for solution principles. This paper offers a new way to explain the life-cycle of product innovation through the identification of motifs that describe the functions of a product. Functional motifs are recurrent function blocks across multiple generations of designs for a product. A collection of functional motifs defines the functional architecture of the product. Using some key examples from innovations in sewing machines, the paper illustrates the occurrence of motifs as the basis for detecting the emergence of a dominant design. Patents related to the sewing machine over 177 years are analyzed to identify functional motifs characterizing the evolution and convergence toward a dominant design. Results show that motifs do not change over long periods once a dominant design emerges even though components continue to change. This observation confirms a view of dominant designs as a technological frame but refutes the notion that design no longer matters in the era of incremental change. These motifs refine our understanding of how designs evolve along a particular path over the course of product innovation.