bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimal Structural Design of the Composite Chassis of an High Energy Efficient Vehicle
Authors: Stabile Pietro , Ballo Federico Maria , Gobbi Massimiliano , Mastinu Gianpiero 
Published: November 2023

Abstract
This paper presents the optimal structural design of the composite chassis of a high energy efficient quadricycle. The chassis consists of three main parts, realised by means of sandwich structures. The bottom frame, has an expanded polypropylene (EPP) foam core and carbon fiber-reinforced plastic (CFRP) external skins. Aramid honeycomb and CFRP skins are used for the sandwich structures of the body and the bulkhead that separates driver’s compartment and energy compartment. An optimization procedure, suitable for laminated composite structures, is employed to determine the optimal stacking sequence, i.e. shape, number and orientation of each ply. The sum of the weighted compliance evaluated for several load cases is set as objective function to minimize. Optimization constraints are related to the limit mass and to structural stiffness, strength and manufacturing aspects. The optimization procedure relies on three phases. Free-size phase aims to determine the optimum shape and continuous thickness distribution for each ply. The second phase involves a discrete optimization procedure, to determine the optimal number of plies. Finally, the optimal stacking sequence of the laminates is defined in order to meet manufacturability requirements. Compared with the non-optimized design of the chassis, the optimization approach adopted leads to a chassis featuring lower mass, with same compliance. Strength and stiffness related properties are also improved.
Augmented Reality for the Positioning of Fasteners and Adhesives in Sheet Metal Joining Processes in the Automotive Industry
Authors: Schwenk Rebecca , Smith Shana 
Published: November 2023

Abstract
The manual sheet metal joining process in the automotive industry relies on visual guides to locate standardized joint positions. Traditionally, operators place physical templates on the workpiece to mark the exact positions of each joint prior to operating the machinery. In attempts to eliminate this step, applications of augmented reality (AR) that provide positional information through digital means have been introduced. AR offers additional functionalities such as displaying animations and tolerances; however, existing works on the topic often rely on markers in the workspace and face limitations due to ergonomics and workpiece complexity. This paper introduces an AR solution that uses model targets and digital templates to overlay the positional information of joints on the workpiece, enhancing manufacturing visualization. In addition, the positional and diametrical relative accuracies of joints are measured and provided to the user in real-time. Functionalities such as animations, a user interface (UI), and a progress tracking mechanism additionally enhance the process for the user. Unity and Vuforia Engine together with a sample workpiece were used to demonstrate the concept. The resulting solution is free of physical markers in the workspace, resistant to accidental occultation, and suitable for a variety of sheet metal joining processes and workpiece complexities.
Use of Data-Mined Customer Reviews to Inform Design Evaluation and Testing of Bassinets
Authors: Singhose William , Adams Christopher , Rana Anjnee , Kim Dooroo , Li Wayne 
Published: November 2023

Abstract
This paper describes a method to use data-mined customer reviews to identify potential product defects. The process involves locating negative reviews of a specific product and then extracting comments that have potential connections with the product design. The extracted comments are then categorized and correlated with features of the product. Given that the customer comments are generally not specifically tied to engineering requirements, or even posed in engineering terms, this correlation requires some degree of engineering analysis to establish a correlation. After these complaint-feature correlations are established, then engineering tests directed at understanding the possible defects are performed. This data-mining process effectively harnesses the massive amount of in-situ testing and evaluation that is performed by customers of the product. The process is illustrated via a case study of an infant bassinet. Customer reviews of the product were studied to identify and categorize key complaints about the bassinet. These complaints were correlated with features and potential defects in the product, including assembly difficulties, as well as a sleeping surface that tilts and causes infants to roll and press into the mesh side wall while sleeping. Then, assembly and sleeping surface deflection tests were conducted. The sleeping surface deflection tests include measurements of how bassinet leg separation affects the deflection, as well as long-duration progressive deflection that occurs from repeated use of the product. The results of the engineering testing confirm the presence of defects in the bassinet and its assembly instructions, as suggested by the reviews. This case study illustrates how data-mined customer reviews provide a valuable source of engineering data and indications of product defects.
Automated Evaluation of Written Assignments in Engineering Using Task-Independent Features and Rubric-Guided Features
Authors: Xu Wanyu , McAdams Daniel A. , Polycarpou Andreas A. 
Published: November 2023

Abstract
The increasing student enrollment in engineering and the growing importance of written communication motivate us to implement automated evaluation of written technical documents. This study aims to enhance our understanding of the automatic grading of written assignments in engineering and provides insights into effectively evaluating technical communication. We collected a graded dataset from a mechanical engineering course and manually selected features based on its grading rubric. Manually selected features include both task-independent features and task-dependent features. We evaluated the performance of the selected features on a classification model using grades provided by human graders. The combination of task-independent features and rubric-guided topic features outperformed any other single features or their combinations and reached a moderate agreement with human graders on 3 out of 5 datasets. The results suggest a new way to evaluate students’ assignments based on pre-defined rubrics and provide a significant first step toward building an automated evaluation framework that can provide detailed feedback on students’ complex assignments.
The Synthesis of P-Drivable Single Degree of Freedom Spatial Mechanisms
Authors: Liu Jian , Khan Mohammad Z. , Myszka David H. , Murray Andrew P. 
Published: November 2023

Abstract
For a single degree of freedom spatial mechanism, a reference frame attached to any of its links produces a continuous motion of this frame. Given the progression of this frame from the start through the end of the mechanism’s motion, this paper seeks to identify specific points relative to this moving reference frame. The points of interest are those that can be coupled with a second point determined in the fixed frame to act as the end joint locations for a spherical-prismatic-spherical (SPS) driving chain. If the selection of the point pair is made such that the change in distance between them as the mechanism moves is strictly monotonic, then the SPS chain they define is potentially capable of driving the mechanism over the desired range of motion. This motion is referred to as P-drivable. This avoids singularities encountered by actuating the mechanism at one of its original joints. The proposed approach enables the dimensional synthesis of a single degree-of-freedom mechanism to focus on creating circuit-defect-free solutions. The actuating chain can then be determined as a separate step in the synthesis process. This paper also considers motions that are not P-drivable and the specialization to planar systems with the synthesis of a P-drivable RPR chain.
Sensor Network Design for Permanent Magnet Synchronous Motor Fault Diagnosis
Authors: Kohtz Sara , Zhao Junhan , Renteria Anabel , Lalwani Anand Vikas , Zhang Xiaolong , Haran Kiruba Sivasubramaniam , Senesky Debbie , Wang Pingfeng 
Published: November 2023

Abstract
Optimal sensor placement is a challenge in many engineering design applications, especially within the field of prognostics and health management. Recently, data-driven approaches have become a staple for solving and addressing these challenges. Machine learning techniques have been applied to solve complex optimization problems in the field of signals processing. However, these methods require a substantial amount of data, which can be difficult to obtain. In addition, the design space may be extremely large, so a deterministic approach may not be possible. Therefore, there is a need for probabilistic frameworks that can simultaneously train a classifier for detection of faults as well as selecting new designs for optimal placement. In this paper, the proposed methodology contains a genetic algorithm embedded with a clustering algorithm to simultaneously train the classifier and determine a sensor network. This novel structure is implemented for detecting short-winding faults of a permanent magnet synchronous motor using magnetic field sensors. The training data is simulated using a finite element model, and the design space is extremely large. Nonetheless, the results of the proposed methodology show accuracy for detection of faults.
Understanding the Relation Between Designer Search Strategies and Designer Learning During Design Space Exploration
Authors: Song Hyeonik , Selva Daniel 
Published: November 2023

Abstract
Design Space Exploration (DSE) is a knowledge acquisition technique used from early-stage to detailed engineering design. During DSE, designers systematically generate a range of design alternatives and compare them by their design criteria. DSE allows designers to learn about the design problem, e.g., about design decisions or features that are more common among good designs than among other designs (“driving features”). To help designers learn the driving features, AI-assisted design tools with advanced analysis techniques have been developed. Yet, there has been scant research attention on understanding designers’ exploratory and learning behaviors during DSE, which has the potential to contribute to the development of more effective AI-assisted design tools. To address the research gap, we examine designer learning behaviors in real-time using data from a human subject study (N = 24) that studies human-AI collaboration in DSE. We examine design search strategies (i.e., number of generated designs, and scale of design moves made during design generation) and their relations to designer learning (i.e., knowledge about driving features, and ability to use driving features to generate designs). In this work, we show that the designer’s ability to use driving features to create new designs is associated with their knowledge about the driving features. Also, we observe that designers who generate many designs (mindless creation) achieved lower designer learning than those who generated few designs. We discuss the findings’ potential implications on the development of future AI-assisted design tools.
Exploring How the Design Hierarchy of Needs Explains Correlations Between Design Decisions and Online Customer Review Ratings
Authors: Retzlaff Lisa , Ferguson Scott 
Published: November 2023

Abstract
Understanding and meeting customer needs plays a significant role in the success of released products. Consumer shopping behavior has also moved toward an online experience, where customers learn of, read about, and make selections based on what they see online. Customer reviews offer a wide range of commentary about the shopping, delivery, and product use experience. The comments specific to product use can provide significant insights into where a product is failing to meet (or exceeds) expectations. In this paper, we establish a frame that the impact of design decisions is often reflected in these comments, and that the star rating a product receives can be correlated to how well a product fulfills the Design Hierarchy of Needs. The Design Hierarchy of Needs is an adaptation of Maslow’s Hierarchy of Needs, where needs are associated with the levels of functionality, reliability, usability, proficiency, and creativity. The tenet of the Design Hierarchy of Needs is that a design must first serve lower-level needs (functionality) before addressing higher-level needs (creativity). In this paper, we explore five hypotheses that are posed with the goal of exploring how the Design Hierarchy of Needs can be used for explaining online review star ratings by identifying where products fail to address (or exceed) needs. Reviews from five products across multiple star ratings are coded, and these codes are analyzed to determine the frequency of categories where negative comments about needs are being addressed. We show that the number of negative comments addressing functionality and reliability decrease as the star ratings increase, and that 1- and 2-star reviews are dominated by negative comments about these need levels. We also find that as the star ratings increase, negative comments are focused on the hierarchical levels of proficiency and usability, reflecting the tradeoffs that customers perceive themselves making while using the product. Finally, we also show that positive comments about proficiency and creativity primarily exists only in 4- and 5-star reviews. We believe that these findings can help drive conversations around design decisions when considering the extent to which needs should be addressed, particularly when tied to product cost, and the tradeoffs that are made when addressing mid-tier levels of the hierarchy. This work further establishes that design decisions must be made in the context of a market system, where need fulfilment, cost and performance, and the online shopping experience must be simultaneously considered.
Capturing Local Temperature Evolution During Additive Manufacturing Through Fourier Neural Operators
Authors: Chen Jiangce , Xu Wenzhuo , Baldwin Martha , Nijhuis Björn , Boogaard Ton van den , Gutiérrez Noelia Grande , Narra Sneha Prabha , McComb Christopher 
Published: November 2023

Abstract
High-fidelity, data-driven models that can quickly simulate thermal behavior during additive manufacturing (AM) are crucial for improving the performance of AM technologies in multiple areas, such as part design, process planning, monitoring, and control. However, the complexities of part geometries make it challenging for current models to maintain high accuracy across a wide range of geometries. Additionally, many models report a low mean square error (MSE) across the entire domain (part). However, in each time step, most areas of the domain do not experience significant changes in temperature, except for the heat-affected zones near recent depositions. Therefore, the MSE-based fidelity measurement of the models may be overestimated.
This paper presents a data-driven model that uses Fourier Neural Operator to capture the local temperature evolution during the additive manufacturing process. In addition, the authors propose to evaluate the model using the R2 metric, which provides a relative measure of the model’s performance compared to using mean temperature as a prediction. The model was tested on numerical simulations based on the Discontinuous Galerkin Finite Element Method for the Direct Energy Deposition process, and the results demonstrate that the model achieves high fidelity as measured by R2 and maintains generalizability to geometries that were not included in the training process.
Ocean Wave Energy Conversion With a Spar-Floater System Using a Nonlinear Inerter Pendulum Vibration Absorber
Authors: Gupta Aakash , Tai Wei-Che 
Published: November 2023

Abstract
The inerter pendulum vibration absorber is connected with a power take-off mechanism (called IPVA-PTO) to study its wave energy conversion potential. The resulting IPVA-PTO system is integrated between a spar and a floater (torus) using a ballscrew mechanism. The hydrodynamic stiffness, added mass and radiation damping effects on the spar-floater system are characterized using boundary element method via Ansys Aqwa. It has been observed that a 1:2 internal resonance between the spar-floater system and the pendulum is responsible for nonlinear energy transfer between the two systems. This nonlinear energy transfer occurs when the primary harmonic solution of the system becomes unstable, and a secondary solution emerges in the system characterized by harmonics of frequency half the excitation frequency. As a result of this energy transfer, the vibration of the spar-floater system is suppressed, and the energy is transferred to the pendulum. The focus of this paper is to analyze this 1:2 internal resonance phenomenon near the resonant frequency of the spar. The IPVA-PTO system, when integrated with the spar-floater system, is compared to a linear coupling between the spar and the floater in terms of the response amplitude operator (RAO) of the spar and the energy conversion capability defined by the capture width of the energy converter.
An Experimental Investigation of the Effects of Lubrication on Rattling Motions and Noise of a Gear Pair
Authors: Donmez Ata , Kahraman Ahmet , Handschuh Michael , O’Toole Michael 
Published: November 2023

Abstract
Vibro-impacts are common in various automotive power transmission applications. They lead to noise issues known as rattling. Input or output toque fluctuations cause gear teeth to separate and commit to complex sequences of drive and coast-side impacts spanning the entire backlash zone. Several recent experimental and theoretical studies characterized the underlying nonlinear structure of the rattling behavior of gear systems, ignoring any influences of lubrication conditions. Those rattle models with no lubricant provisions were shown to compare well with the dry contact measurements. This experimental study aims at complementing these earlier studies providing acoustics and torsional vibration measurements from a single gear pair excited by input torque fluctuations as it operates dry, jet-lubricated and dip-lubricated conditions. A rattle test machine equipped with a lubrication system is presented. Corresponding vibro-impact and sound pressure measurements are provided for a wide range of operating and lubrication conditions. Noise and torsional vibration data under different lubrication conditions are compared directly to quantify the influence of lubrication on the resultant rattle behavior. Presented results with dry, dip and jet lubrication show that lubricant provides some additional damping in the process reducing the noise level to a certain extent while overall vibro-impact behavior remains intact.
Non-Smooth Reduced Interface Models and Their Use in Co-Simulation of Mechanical Systems
Authors: Raoofian Ali , Dai Xu , Kövecses József 
Published: November 2023

Abstract
In a co-simulation setup, the entire system is decomposed into a collection of individual subsystems that are interfaced together, with each subsystem being modelled and integrated separately according to its own requirements. To maintain the interconnectivity and consolidation of the primary system, these subsystems must communicate with each other through the interface and transfer certain information at the end points of a defined time interval termed macro time step. Inside the macro time step, the evolution of the interface variables has to be approximated as information about them will only be available again at the end of the step. In real-time simulations, the size of the macro time step and the accuracy of the approximated interface variables are critical factors; if the interface variables are approximated accurately, the size of the macro time step can be kept large enough to provide interactive rates without loss of accuracy and stability. This work focuses on systems where unilateral contact interactions are important and proposes reduced interface model concepts for such non-smooth systems. The use of the proposed reduced interface model (RIM) is demonstrated in co-simulation to provide model-based approximation of the interface variables. The advantages of the proposed method are demonstrated through two representative case studies.
Modeling Semisubmersible Wind Turbine With Inerter-Based Structural Control in the Platform
Authors: Okuda Ryan , Zuo Lei 
Published: November 2023

Abstract
With growing awareness of climate change and an increased interest in renewable energy, resources like offshore wind are projected to grow in the near future. One key issue within offshore wind is how to stabilize the floating system when it experiences large wind and wave forces that impact its performance and shorten its operating life. Researchers have been exploring structural control methods and creating modeling tools to evaluate the performance of the control methods. One such tool is Open-FAST, which is the industry standard for modeling wind turbine dynamics. The goal of this paper is to build the Inerter-based structural control upon the existing capabilities of OpenFAST. Inerter-based structural control methods offer arguably better performance than traditional vibration absorbers, and the configuration proposed in this paper also offers the ability to use a generator as an element in the structural controller. This allows extra energy to be generated along with the improvement in vibration absorption. Through this study, this inerter-based control method is explored through the lens of an established modeling tool to provide the validation for the model to explore which load cases the inerter performs best in and what design considerations must be made. In addition, the energy harvesting potential of the inerter system is evaluated and shown to increase the system’s capabilities especially under stormy ocean conditions.
A Combined Simple/Complex Terramechanics Representation Part 1: Using Machine Learning to Identify DEM Soil Properties From Bevameter Test Data
Authors: Thornton Ben , Pesheck Eric , Jayakumar Paramsothy 
Published: November 2023

Abstract
This study seeks to improve upon existing methods of characterizing soft-soils for mobility simulations which use the Discrete Element Method. The study presents a novel approach for characterizing DEM properties based on Bevameter test data. The proposed method involves training a Reduced Order Model (ROM) to predict Bevameter results from DEM particle properties and using the ROM within a multi-objective optimization framework to determine the DEM properties which best fit a target Bevameter dataset.
To demonstrate the efficacy of the approach, the trained ROM was used to generate DEM models meant to mimic the Bevameter response for a variety of field-tested soils. The accuracy of the resulting DEM models was evaluated using two validation methods. One in which the error in the resulting DEM properties is quantified, and one in which the error in the resulting DEM Bevameter response is quantified.
The findings of this study provide compelling evidence that the proposed approach is a promising method for characterizing the properties of soil for DEM simulations using Bevameter data. The results indicate that certain enhancements are necessary for further refinement of the approach. However, once refined, this method is expected to offer a dependable and efficient means of soil model characterization.