bluuebunny/crossref_metadata_2025_split huggingface dataset:
A New Frequency-Modulation Input-Shaping Strategy Applied on a Double-Pendulum
Authors: Alhazza Khaled A. 
Published: November 2024

Abstract
In this work, a new frequency modulation technique is used to effectively control oscillations in multi-mode systems using single-mode command-shaping. It is well known that single-mode input shaper can effectively eliminate residual vibration of multi-mode system given that all frequencies within the system are odd-multiples of the shaped command frequency. Using this criterion, a new command shaping strategy specifically designed for multi-mode systems is developed. Our proposed strategy involves convolving a well defined formula with the original double-step command shaper. The resulting command modulates the modal frequencies such that the higher mode frequencies are odd-multiples of the first mode frequency. The equation of motion of the system is solved and the natural frequencies are determined analytically as a function of a controller gain. This gain is used to modify the trolley acceleration. To show the effectiveness of our strategy, two numerical simulation are conducted and validated experimentally on double pendulums.
Comparison of Robot Morphologies and Base Positioning for Welding Applications
Authors: Gautier Nicolas , Guillermit Yves , Sebsadji Yazid , Porez Mathieu , Chablat Damien 
Published: November 2024

Abstract
This article undertakes a comprehensive examination of two distinct robot morphologies: the PUMA-type arm (Programmable Universal Machine for Assembly) and the UR-type robot (Universal Robots). The primary aim of this comparative analysis is to assess their respective performances within the specialized domain of welding, focusing on predefined industrial application scenarios. These scenarios encompass a range of geometrical components earmarked for welding, along with specified welding paths, spatial constraints, and welding methodologies reflective of real-world scenarios encountered by manual welders. The case studies presented in this research serve as illustrative examples of Weez-U Welding practices, providing insights into the practical implications of employing different robot morphologies. Moreover, this study distinguishes between various base positions for the robot, thereby aiding welders in selecting the optimal base placement aligned with their specific welding objectives. By offering such insights, this research facilitates the selection of the most suitable architecture for this particular range of trajectories, thus optimizing welding efficiency and effectiveness. A departure from conventional methodologies, this study goes beyond merely considering singularities and also delves into the analysis of collisions between the robot and its environment, contingent upon the robot’s posture. This holistic approach offers a more nuanced understanding of the challenges and considerations inherent in deploying robotic welding systems, providing valuable insights for practitioners and researchers alike in the field of robotic welding technology.
Facilitating Success: Exploring the Influence of Design Facilitators’ Behaviors on Team Members’ Responses and Perceptions of Team Climate and Trust
Authors: Witherell C. J. , Maar Aliaa , Dougherty Presley , Letting Cynthia , Menold Jessica 
Published: November 2024

Abstract
Teams are vital in engineering design as they harness collective problem-solving abilities and enhance creative outputs. However, complex social dynamics, cognitive biases, and individual traits hinder their overall effectiveness, rendering them less useful than the combined capabilities of their members. While previous research has recognized the importance of facilitation and favorable climate for team success, limited research has explored the specific behaviors employed by facilitators to achieve desired outcomes. Leveraging data from a Design Thinking workshop, our mixed-methods study investigates five facilitators’ behaviors and their impact on team dynamics. We develop Markov models to represent the team’s conversational dynamics. Similarities across models indicate recognizable interaction patterns despite group differences. High transition probabilities between facilitator and both participant engagement and support suggest that facilitator involvement fosters a collaborative atmosphere. The significant transition probability from facilitator clarification to participant question underscores the facilitator’s role in promoting comprehension. To assess the impact of facilitators’ behaviors on participants’ perceptions of trust and psychological safety, we examine the correlations between these factors. Significant correlations suggest facilitators positively influence psychological safety through engagement in the design task and promotion of reflective behaviors. Contrasting prior research, rapport building does not correlate with psychological safety. Participants express higher levels of trust in facilitators who provide more direction. It is crucial for facilitators to recognize their influence and employ tactics to foster trust and positive team climate. These findings hold significant implications for the effective facilitation of design teams, a necessary step to ensuring the success of teamwork.
Data-Defined Machine Intelligence for Mechanical Properties Identification
Authors: Iliopoulos Athanasios P. , Michopoulos John G. , Steuben John C. , Apetre Nicoleta 
Published: November 2024

Abstract
In this work we are introducing a Machine Intelligence (MI) concept that belongs to the set of approaches that emphasize data over artificial intelligence model. The development of the concept was motivated by the need to eliminate training as the means to enable fast or perpetual system adaptation and update, and also vastly reduce pre-processing times. This characteristic can be extremely valuable in situations when new data are regularly becoming available and utilized to increase the MI accuracy with minimal delay, or when the represented system changes with time. A characteristic of the proposed data-defined MI approach is that of introspection; it can be queried to locally estimate the system error at any data point that is part of its data set and potentially act accordingly. To provide evidence of the validity and performance of the approach, we first demonstrate on classical hand-writing classification of numerical digits. We then apply it to a challenging problem of identifying the properties of bi-linear elastoplastic material using a single full-field strain tensor image of a synthetic experiment. The validation test is performed for many untrained instances, and the histogram, mean and maximum errors are discussed. We also discuss several interesting capabilities that a data-defined concept enables.
Evaluation of Select Condition Indicators for Detecting Damage in Geared Transmissions Using a Finite Element/Contact Mechanics Model
Authors: Thunuguntla Suhas Gupta , Hood Adrian A. , Cooley Christopher G. 
Published: November 2024

Abstract
This study analyzes the ability of several practical rotorcraft condition indicators for detecting damage in a two-stage rotor-craft transmission utilizing a finite element/contact mechanics (FE/CM) model. The transmission includes an input spiral-bevel stage and an output planetary stage. The vibrations of the transmission are calculated at typical operating torque and speed using a full dynamic solution of the finite element transmission model. In this solution, the contact conditions at all meshes are calculated at each time instant accounting for changes due to the kinematic motion of the individual gears; the elastic deformations of all teeth, blanks, shafts, and other components; and vibration. The elastic vibrations produced from the housing contain frequency content that appears in clusters near the mesh frequency harmonics of each gear stage. The planetary gear mesh frequency clusters have prominent upper and lower sidebands that occur at the planet-pass frequency. When damage (i.e., tooth root cracks or tooth surface pits) occurs on the individual gears of the planetary stage, additional transient vibrations occur that meaningfully alter the resulting vibrations and their spectrum. The frequency components near planetary gear mesh frequency clusters are not sensitive to damage, even when the damage is large. The use of condition indicators (CIs) for detecting damage is explored. The ability of the CIs to detect damage is shown to be sensitive to the input vibration signal used in the CI calculation. Time synchronous averaging is shown to improve the performance of CIs, provided the averaging does not artificially modify the underlying vibratory signal. We demonstrate that averaging over the planet-pass period effectively eliminates non-planetary gear vibration components and improves CI sensitivity. Multiple constructions of the difference signal are analyzed, and their impact on CI performance is quantified. Tooth root cracks with small lengths less than 50 percent through the tooth thickness generally do not lead to substantial increases in the individual CIs. It was observed that most CIs increased when more sidebands were removed for crack lengths above 50 percent. The results from this work will enable reliable damage detection techniques for geared transmissions, thus improving safety and reliability in rotorcraft propulsion.
Modeling and Control Co-Design of a Floating Offshore Vertical-Axis Wind Turbine System
Authors: Lee Yong Hoon , Bayat Saeid , Allison James T. , Hossain Md Sanower , Griffith D. Todd 
Published: November 2024

Abstract
This study investigates the modeling and design of a floating vertical-axis wind turbine (FloatVAWT) system with multidisciplinary design optimization (MDO) and control co-design (CCD) approaches. By integrating various associated disciplinary models, the study aims to holistically optimize the physical and control designs of the FloatVAWT system. Through the identification of impactful design elements and capitalizing on synergistic interactions, the study aims to provide insights to subsystem designers and aid their detailed decisions. The model developed for this CCD framework utilizes automated geometric manipulation and mesh generation to explore various FloatVAWT configurations during the early design stages. Surrogate models facilitate efficient design studies within limited computing resources by exchanging model information between disciplinary models and subsystems without running exhaustive simulations during the optimization loop. The model incorporates an aero-hydro-servo dynamic representation of the FloatVAWT system, considering physical and control constraints. Additionally, the study investigates the potential benefits of varying the average rotational speed of the vertical-axis wind turbine (VAWT) rotor to enhance energy production and minimize adverse platform motions, thus reducing the levelized cost of energy (LCOE). System-level design solutions are analyzed to identify design trade-offs and propose mitigation strategies for potential mechanical failures of the rotor. In conclusion, this study provides modeling strategies for the FloatVAWT system and analyzes the system design solutions through MDO and CCD approaches. The outcomes of the study offer insights into system-optimal solutions for subsystem-level decisions considering multidisciplinary couplings.
A Disassembly Score for Human-Robot Collaboration Considering Robots’ Capabilities
Authors: Liao Hao-Yu , Pulikottil Terrin , Peeters Jef R. , Behdad Sara 
Published: November 2024

Abstract
Product disassembly is essential for remanufacturing operations and recovery of end-of-use devices. However, disassembly has often been performed manually with significant safety issues for human workers. Recently, human-robot collaboration has become popular to reduce the human workload and handle hazardous materials. However, due to the current limitations of robots, they are not fully capable of performing every disassembly task. It is critical to determine whether a robot can accomplish a specific disassembly task. This study develops a disassembly score which represents how easy is to disassemble a component by robots, considering the attributes of the component along with the robotic capability. Five factors, including component weight, shape, size, accessibility, and positioning, are considered when developing the disassembly score. Further, the relationship between the five factors and robotic capabilities, such as grabbing and placing, is discussed. The MaxViT (Multi-Axis Vision Transformer) model is used to determine component sizes through image processing of the XPS 8700 desktop, demonstrating the potential for automating disassembly score generation. Moreover, the proposed disassembly score is discussed in terms of determining the appropriate work setting for disassembly operations, under three main categories: human-robot collaboration (HRC), semi-HRC, and worker-only settings. A framework for calculating disassembly time, considering human-robot collaboration, is also proposed.
Application of Machine Learning in Design and Optimization of MEMS Tunable Capacitors
Authors: Naderian Pooria , Shavezipur Mohammad 
Published: November 2024

Abstract
Electrostatically actuated MEMS tunable capacitors have a nonlinear capacitance-voltage (C-V) response and limited tunability. Increasing the tunability and the linearity of the device’s response is of great desire. In this study, a design method, based on machine learning and optimization, is introduced that improves the tuning ratio and linearity of the device response. A capacitor with two deformation modes (rigid-body displacement of the moving electrode before pull-in and its deformations after pull-in) is used to demonstrate the technique. A set of simulation data for different design geometries is generated by ANSYS coupled-field multiphysics solver to create a three-level, three-factor central composite rotatable design (CCRD). To predict the linearity of the MEMS capacitor, a mathematical model using response surface methodology (RSM) is developed. The initial and final gap between the electrodes and the curvature of the moving electrode are considered as the input parameters and linearity as the outcome for the regression model development. The created model was evaluated for adequacy and significance. The model can predict linearity with more than 97% accuracy, according to confirmation tests, and the effect of the input parameters on the linearity of the response is studied. The optimization was conducted by Design-Expert 13 software and results show 0.96 for the maximum linearity of the C-V plot.
Defining a Modelling Language to Support Functional Hazard Assessment
Authors: Hulse Daniel , Mbaye Seydou , Irshad Lukman 
Published: November 2024

Abstract
Functional Hazard Assessment (FHA) is a key early-stage engineering process that supports the incorporation of safety in design by identifying the high-level functional hazards the system may encounter. While many FHA-like methodologies have been proposed in the design engineering literature, many of these methodologies have had difficulty becoming accepted industry practice. Industry standards, on the other hand, either provide too little recommendation on how to represent the function of the system to perform FHA, or rely on existing design artefacts which insufficiently support the goals of the process. This paper presents some of the problems with current modelling languages (both proposed and used) for FHA which limit the scope, expressiveness, flexibility, and precision of the analysis. It then outlines desirable principles an FHA-supporting analysis language should embody, and introduces the Functional Reasoning Design Language (FRDL), a formal modelling language for describing the functional elements of a system and their interactions, which aims to satisfy these principles. To demonstrate the use of this language, the modelling and hazard analysis of a disaster response drone is presented. While this case study is limited in scope, it highlights how FRDL can represent system function while reducing the ambiguity present in typical FHA-supporting functional modelling languages.
Generative Design for Power System Networks Using WGAN and Graph Performance Measures for Guided Generation
Authors: Chung In-Bum , Wang Pingfeng 
Published: November 2024

Abstract
Interdependent critical infrastructures are prone to be a target of disruptions from either natural disasters or deliberate attacks that can cause a great magnitude of negative consequences on the system. To withstand and reduce the impacts of disruptive events, it is beneficial to consider them in the early stage of design to create a configuration that can maintain its performance as much as possible. However, due to the complexity of modern systems, it is difficult to directly optimize the system design of large-scale with complicated constraints. Hence, in this research, a generative approach based on Wasserstein GAN is implemented to create intelligent designs. The power system networks are represented as graphs with features holding information on the nodes and edges that relate to electrical components. The trained generator can create a power system network consisting of generators and loads, where the buses are connected to transmission lines of a certain capacity. Afterwards, performance measures are employed to induce the generation of a population with more appealing characteristics. The framework was applied to a case study of a 57-bus system of synthesized power grid and the results have shown that the model was able to generate feasible designs of higher resilience.
Enhancing Long-Term Decision Making in Trajectory Planning: Deep Reinforcement Learning With Knowledge Transfer and Online Demonstrations
Authors: Hu Chuanhui , Jin Yan 
Published: November 2024

Abstract
Deep reinforcement learning is now widely applied in motion planning problems for autonomous systems due to its model-free nature and its ability to solve complex control problems through trial-and-error. However, the success of deep reinforcement learning depends heavily on the exploration policy and the design of the reward function. This dependence makes it challenging to solve long-range planning problems and requires careful reward function design to avoid the sparse reward problem. In this paper, we propose a dual-level off-policy deep reinforcement learning framework that combines high-level policy learning and low-level integration of a controller. The high-level policy enables the agent to make long-term decisions with a flexible horizon. Using a high-level policy also mitigates the sparse reward problem in long-range planning tasks. By storing only high-level actions and transitions in the experience buffer, the agent can efficiently learn in long-range trajectory planning tasks. The proposed parallel architecture of the neural network enables the utilization of high-level domain knowledge transfer. Online demonstrations can be integrated during training using global planning algorithms to enhance the quality of experiences. Experimental results show that transferring high-level geometry knowledge and applying online error correction can significantly enhance the agent’s performance in long-range trajectory planning tasks.
Recycling Automotive Shredder Residue: A Techno-Economic Analysis
Authors: Chapman Luke , Velez Amber , English Ashley , Gavilanes Melanie , Huynh Hung , Welsh Emily , Winter Amos G. 
Published: November 2024

Abstract
This study presents a novel “sorter-at-shredder” business model for the pyrolysis of Automotive Shredder Residue (ASR) plastics, addressing the challenge of low plastic recycling rates in end-of-life vehicles (ELVs). ASR comprises 25% of ELV material, and ASR consists of about 43% polypropylene (PP) and polyethylene (PE) plastics, which can be separated using sink-float sorting technology and pyrolyzed into olefin-rich product. The “sorter-at-shredder” business model strategically places small-scale pyrolysis plants that process up to 280 tons per day at existing shredders, integrating with ferrous and non-ferrous processing chains and enabling daily processing of ASR plastics. The model focuses on PP and PE plastics, creating olefin-rich product that can be re-entered into the supply cycle. Economic analysis reveals a yearly revenue of $46.1M for olefins-rich product, and a promising payback period of 6.3 years for the shredder, falling within acceptable ranges. The model uses Dallas, Texas, to capitalize on regulatory, economic, and geographical advantages, including proximity to a cluster of 5 shredders which process 165 tons of PP and 119 tons of PE each day, and proximity to oil refineries which can purchase olefin-rich product from the shredder. Future work involves optimizing sorting and pyrolysis technologies, ensuring the model’s success and contributing to a sustainable automotive industry.
Identifying Human Errors and Error Mechanisms From Accident Reports Using Large Language Models
Authors: Irshad Lukman , Walsh Hannah 
Published: November 2024

Abstract
Emerging operational concepts for aviation hinge on novel paradigms for human machine interaction. Critical to their safe operation is early consideration of human error into the design process. Existing methods for consideration of human error require significant expert input, which is challenging both in early design and in novel systems for which there is little existing safety expertise. In this research, we propose a methodology for identifying human error, error producing factors, and mechanisms in early design from historical incident reports. Additionally, we hypothesize that cross-domain sharing of lessons learned can aid with early design human considerations in circumstances where data is not relevant or incomplete. This is addressed by identifying causes of human error in aviation and railway domains through applying state-of-the art natural language processing techniques to historical incident reports. Using this method, it is possible to extract extensive reports on human error from past incidents. Using the proposed approach, we identify nine human errors from railway reports and fourteen from aviation reports, with three errors common to both domains. There is at least one error producing conditions for each human error while a majority of the errors have more than one error mechanism. We also found that a majority of the human errors, error producing factors, and error mechanisms (even if they are not common between the domains) can be used to inform safe operations across domains as long as the errors are not domain specific and are interpreted and contextualized using engineering judgement.