bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multi-Objective Optimization of Multi-Stage Gear Transmission Considering Scuffing Risks
Authors: Shi Zhenghong , Liu Yuchuan , Zavala Favian , Xu Hai , Reinhart Timothy 
Published: November 2024

Abstract
Multi-objective and multi-step optimization of gear mesh is presented in this study which covers both macro and micro-geometry design variables. Two objective functions are considered including minimization of gear mesh power loss and transmission error (TE). Design constraints such as bending/contact stress limits, geometrical requirements and scuffing risk are introduced in optimization process. Scuffing is one of the most common surface failures in EV gear transmission due to high motor speed and output torque. Major causes include high contact pressure, high sliding velocity and surface temperature etc. The mainframe of its evaluation method applied in the analysis is based on AGMA standard which incorporates most tribological evaluation data needed. Some related key properties such as surface roughness and lubrication conditions are generated from more refined analysis or test results. For optimization method, a modified multi-objective particle swarm optimization (MOPSO) is applied which account for different aspects of gear performances and constraints. Results demonstrated significant reduction in both power loss and peak to peak TE at the end of iteration. Finally, the relationship among scuffing probability and other gear performance data are compared. It is indicated that optimized design with minimum power loss and TE does not have the lowest maximum surface temperature.
Applying Affordance Theory to Improve Design Practices for Physical, Mental, and Social Health Outcomes
Authors: Myers Hannah E. W. , Mabey Christopher S. , Burleson Grace 
Published: November 2024

Abstract
As people interact with physical artifacts, the interaction can produce a wide range of social impacts on the user. Of all possible social impact outcomes, design processes have traditionally focused on health and safety. Within health and safety, most design methods and standards aim to mitigate the probability of physical harm while neglecting other aspects of health, such as mental and social well-being. We argue that expanding the focus of design to consider a more holistic view of health will require looking beyond performance-based standards. Specifically, we suggest that affordance theory and technological mediation could be used as strategies when designing for health, enabling designers to focus on the various conditions and mechanisms that solutions may or may not enable. Although the idea of affordances is not new, their application to design remains limited. Adoption of affordance theory in design processes may be limited by criticisms that affordances are too abstract and ambiguous. Further, most health-related standards reflect generalized best practices that do not account for differences in health needs across different individuals and populations. We argue that affordance theory may enable new approaches for designers to directly consider the co-production of physical, mental, and social health in design processes.
Multilevel Decision Support Framework for the Simultaneous Design Exploration of Material Structures and Processes
Authors: Digonta H. M. Dilshad Alam , Nellippallil Anand Balu , Baby Mathew 
Published: November 2024

Abstract
Traditional manufacturing, such as steel manufacturing, involves a series of processes to realize the final product. The properties and performance of the final product depend on the material processing history and the microstructure generated at each of the processes. Realizing target product performance requires the simultaneous design exploration of the material microstructure and processing, taking into account the multilevel interactions between the material, product, and manufacturing processes. This demands the capability to co-design, which involves sharing a ranged set of solutions through design exploration across multilevel and providing design decision support.
In this paper, we present a co-design exploration framework for multilevel decision support. Using the framework, we model the interactions and couplings between the levels and facilitate simultaneous decision-based design exploration. The framework integrates the coupled compromise Decision Support Problem (c-cDSP) construct with interpretable Self-Organizing Maps (iSOM) to facilitate (i) the formulation of the multilevel decision support problems taking into account the interactions and couplings between levels, (ii) the simultaneous visualization and exploration of the multilevel design spaces, and (iii) decision-making across levels for multilevel designers. The efficacy of the framework is tested using a hot rod rolling problem focusing on the interactions between the dynamic and metadynamic phases of material recrystallization and the thermo-mechanical processing during the hot rolling process. The framework is generic and supports the co-design exploration of systems characterized by multilevel interactions, couplings, and multidisciplinary designers.
Predicting Future States of Evolving Systems
Authors: Mandegari Siavash , Bhalerao Mayank J. , Allen Janet K. , Mistree Farrokh 
Published: November 2024

Abstract
Systems are characterized by continuous changes that are driven by both the evolving nature of the system itself and external environmental factors. To design systems for the future, it is critical to understand the behavior of these systems, the interactions between variables, and the emergent phenomena that arise in evolving systems. Designers must possess the ability to incorporate this understanding into their designs, to ensure robust solutions for the problems at hand.
In response to the capabilities required to design future system states, we propose a method that includes System Dynamics to simulate the system and the compromise Decision Support Problem Construct (cDSP) to obtain robust satisficing solutions. Through this method we envision enabling designers design a system for required system goals by initially simulating the system for the present and past states and using this information of the change in behavior of the system to attain robust satisficing solutions for systems goals of the future state.
In our method, we use System Dynamics to simulate the system and obtain the values of the variables for different states. Based on these values, we formulate and solve a cDSP to find robust satisficing solutions. We account for the trade-off between different variables. Based on the information obtained from present and past states we establish a satisficing solution space for the future state.
In this paper, we demonstrate the efficacy of our method using a simple problem to gain clarity on the process and the outcome. Our focus in this paper is on describing the method rather than the outcomes associated with the test problem.
Reducing Nonlinear Vortex-Induced Vibrations in Power Lines With Moving Nonlinear Absorbers
Authors: Basta Ehab , Gupta Sunit K. , Barry Oumar R. 
Published: November 2024

Abstract
Aeolian vibration is a significant factor contributing to the fatigue failure of power transmission lines. The mitigation of such vibrations in power lines has traditionally been achieved using Stockbridge dampers along the line spans, which are modeled as fixed vibration absorbers. They largely depend on their resonant frequencies and placement on the cable. Therefore, given the stochastic nature of the wind, recent studies have explored the concept of dynamic/moving absorbers. Although the effectiveness of the moving absorber has been demonstrated in the literature to be superior to that of the fixed absorber, analyses have primarily been limited to linear cases and have not accounted for nonlinearity introduced by the moving absorber or the wind inflow on the powerline. Aiming to fill this gap, this work combines the nonlinearities from the fluctuating lift force modeled as a van der Pol oscillator, with a nonlinear moving absorber into a single model to investigate the effect of a nonlinear mobile damper relative to its linear counterpart. We observe that the system with a nonlinear moving absorber exhibits smaller amplitude oscillations when compared to its linear counterpart. This finding underscores the superior mitigation characteristics of nonlinear vibration absorbers and suggests the potential for designing an optimal nonlinear moving vibration absorber.
A Model and an Experimental Methodology to Validate Deep Groove Ball Bearing Load Distribution
Authors: Dharap Gaurang , Talbot David 
Published: November 2024

Abstract
Experimental ball bearing load distribution measurement is uncommon compared to its roller counterpart due to a variety of reasons. This paper presents a novel methodology to conduct experimental ball bearing load distribution measurement under radial loads. A brief literature review discusses the different types of experimental studies that directly or indirectly attempt to validate bearing load distribution models. First, a feasibility study highlights the possibility of measuring contact pressure, local to individual rolling elements, at the outer race-housing interface. A tactile pressure film that shows color spread and density proportional to the magnitude of applied pressure is used. The contact density is converted to contact pressure using a series of 1-D maps. Subsequently a conformal contact model is developed to model the pressure between the outer race and housing. The elastic displacements at the outer diameter of the outer race are computed to form a part of the initial separation, and the contact compliance is computed using Boussinesq contact theory. The contact problem is subsequently solved from the governing equation consisting of condition of compatibility and equilibrium equations. The contact pressure thus obtained can be directly compared with the measured contact pressure to validate any ball bearing load distribution model. A representative example simulation is presented before concluding the research.
Knowledge Extraction in Additive Manufacturing: a Formal Concept Analysis Approach
Authors: Ndiaye Yande , Lezoche Mario , Panetto Herve , Lu Yan , Yang Zhuo 
Published: November 2024

Abstract
In Additive Manufacturing (AM), it is still a major challenge to manage part quality, which is heavily influenced by feedstock materials, process settings, and in-process control. Deviations in these factors can lead to defects in the final product, particularly in Laser-Powder Bed Fusion (L-PBF), an AM process that builds parts with high precision but is too complex to control. On that account, in-situ monitoring becomes crucial for evaluating and ensuring process stability during L-PBF builds. By analyzing in-situ data, AM operators can detect process deviations and pause or stop a build as needed. Continuously monitoring in-situ data allows for immediate adjustments of process settings and avoids potential defects in the final part. However, the extensive amount of high dimensional data generated from advanced sensing technologies holds their benefit for anomaly detection and process control. The abundance and complexity of AM process monitoring data require a systematic approach to extract valuable insights and translate them into actionable knowledge. While machine learning-based techniques are nowadays essential for establishing AM process-structure-property relationships, they lack interpretability for quick actions from AM operators. In this context, we introduce a new methodology based on Formal Concept Analysis (FCA) to extract knowledge from AM multi-modal data as a set of association rules that could be used as a knowledge base to guide decisions and provide guidelines for AM operators. Specifically, for L-PBF, melt pool features are first extracted from in-situ melt pool images, and clustered into discrete categories through Machine Learning (ML) techniques. After that, FCA is allied and demonstrated to discover association rules revealing the intricate relationships between ranges of values of these features and process deviations. The final set of rules is analyzed and presented for users to adopt in monitoring their L-PBF AM processes.
The Impact of Undergraduate Experience on Mental Model Component and Function Identification
Authors: Furiato Mikala Y. , Kado Corey J. , Murphy Alexander R. 
Published: November 2024

Abstract
The examination of a student’s comprehension of concepts and ability to apply these concepts to the real-world is challenging. Mental model elicitation has been used as a methodology for decades by educators and researchers to examine conceptual knowledge of various systems. This study examines the impact that student expertise has on mental model completeness through a study involving everyday products. Mental models were gathered using a direct elicitation method from 1st- and 4th-year students in undergraduate engineering courses. Students were instructed to identify components and their functions within the product. Results showed that the student’s overall mental model completeness and identification of system functionality did not significantly change as they progressed through their degree program. However, there was a direct positive correlation between the year of study and the students’ ability to identify components across all products. The findings emphasize the importance of developing mental models as students gain engineering content knowledge and conceptual understanding over the course of a degree program. Equally important is the ability to recognize the similarities between engineered systems and transfer knowledge between those products. The results of this research continue to build on prior examinations using mental models, which provides a broadened view of the capabilities of students regarding component and system functionality of common products. Other peculiarities are discussed as potential avenues for future work through other mental model elicitation methods such as semi-structured interviews. Overall, this study contributes to our understanding of how expertise relates to system thinking.
Experimental Investigation of Bouncing Dynamics of a MEM Switch Under Electrostatic Actuation
Authors: Wang Tianfu , Bognash Mohamed , Ma Jingning , Asokanthan Samuel 
Published: November 2024

Abstract
Advantages of MEMS technology such as low-cost, low power consumption, high functionality, and small size and light weight of the devices. The aim of the present research is to understand the bouncing dynamic behavior of MEM switches in order to improve the switch performance and reliability. It is well known that the bouncing can dramatically degrade the switch performance and life; hence, in the present study, bouncing dynamics of a cantilever-based MEM switch has been studied in detail. To this end, a model of MEM switch that incorporates electrostatic force, squeeze film air damping force as well as asperity-based contact force has been proposed for an electrostatically actuated switch. Euler-Bernoulli beam theory and an approximate approach based on Galerkin’s method have been employed for predicting transient dynamic responses. In the present study, performance parameters such as initial contact time, permanent contact time, major bounce height, and the number of bounces have been quantified in the presence of interactive system nonlinearities. Prototype design and fabrication through UW-MEMS process and an experimental investigation via a micro-scanning laser Doppler vibrometer system are also performed to verify the analytical predictions. The present investigation on the bouncing dynamic behavior of a class of MEM switches is envisaged to yield greater insight into the design, reliability and performance predictions for this class of switches.
Synthesizing Spatial RSCR Mechanisms for Path Generation Using a Deep Neural Network
Authors: Deng Xueting , Nurizada Anar , Purwar Anurag 
Published: November 2024

Abstract
In the recent past, various machine learning approaches have been broadly studied and successfully applied in the planar mechanism synthesis problem. This paves the way for its adoption in the spatial mechanism synthesis that is typically more complex to solve due to dimensionality of the input, large number of mechanism parameters, and highly non-linear relationship between the input and output. This paper introduces a neural network approach for the path synthesis of spatial RSCR mechanisms. We detail the method of database generation with the development of a simulator, data space selection strategies, and a complete data normalization method. A novel way of calculating the coupler curve is utilized, addressing the challenge of describing the absolute position of a point on the rigid body in ℝ3. The database generation uses a spatial mesh grid-base method with specific criteria for preventing circuit defects and duplicate mechanisms. All data in the database is translated, rotated, and reflected to a normalized position. In the end, the classical neural network structure, multi-layer perceptron (MLP), is applied for finding the mapping from the spatial path to mechanism parameters. The MLP with different parameters and techniques, such as batch normalization and dropout, are implemented for finding the best model. The result shows the capability of MLP for complex spatial path synthesis. Moreover, despite the limitation of a single MLP’s structure, this study demonstrates the potential of improvement with more advanced neural networks.
Case Study in Agile for Hardware: Aerospace Systems
Authors: Peterson Matthew , Mocko Gregory 
Published: November 2024

Abstract
This paper provides a case study in Agile for Hardware examining the SpaceX Falcon 9 launch vehicle program. It provides an overview of the program and graphical analysis of the launch and recovery successes of Falcon Rockets. Further comparison is made to the similar rockets in the same vehicle class. SpaceX provides an interesting case study. In less than twenty years they went from a start up to dominating the U.S. and global launch industry. They use non-standard development methods in iterative design and provide an informative case study on success in this market. The evolution of the Falcon rocket is covered, including all major variants from 2002 to 2018. Important technical advances are addressed. Graphical analysis shows how SpaceX applies failures to subsequent launches and generations of launch vehicles to develop revolutionary new capability in the recovery, and eventual reuse of boosters. A “knowledge curve” is proposed and applied to the Falcon rocket development. Similar knowledge curves are used to compare Falcon and Falcon Heavy rockets to Vulcan Centaur and NASA SLS rocket respectively. The time to first flight and iterations are considered. This analysis is informative for those in design research and application. The case is made that iterative development, which traditionally considered expensive and long, compared to virtual iteration, can be faster and cheaper.
Microvia Geometry Assessment at High-Stress Substrate Locations in Printed Circuit Boards Under Thermomechanical Stresses
Authors: Syed Mujahid Abbas , Yu Qiang 
Published: November 2024

Abstract
The increasing dependency of the automobile industry on densely packed electronic packages needs high-density interconnect (HDI) printed circuit boards (PCBs) with strong microvia interconnections. HDI substrates and PCBs utilize microvias as an interconnect among different conducting layers. The electronics industry has evolved from 1-level to multi-stacked microvias for interconnections across multiple sequential layers. Automotive manufacturers. Aiming to reduce failure rates and processing costs, demand that microvias achieve a reliability standard of zero failures. To fulfill this requirement, it is vital to identify and understand the uncontrollable factors that influence microvia structure during reflow. It highlights the need for a thorough analysis of the composite substrate layer with microvia geometry that was overlooked in conventional studies. The conventional studies have centered around the design modifications of microvia for reliability improvement and have not discussed the uncontrollable factor i.e., the interaction between the immediate surrounding of the substrate material and microvia interconnects during the reflow process.
This paper presents a newly developed detailed finite element model of the composite substrate to 1) distinguish the high-stress locations of the substrate composite layer, 2) analyze the role of microvia structures at high-stress locations in the substrate layers and, 3) identify the microvia geometry configurations that are susceptible to early failures at high-stress locations across the substrate. Design of experiments (DOE) involving the Taguchi method is employed to distinguish the role of microvia geometry at resin-rich substrate sites towards reliability assessments.
The simulation results have revealed that uneven fiber-resin distribution in the substrate layer impacts the integrity of microvias. Sites within the layer that have higher concentrations of resin significantly affect the microvia structures, leading to early failures. Specifically, microvias present in regions with more resin concentration are more prone to failures with a 41% increase in stress than those located in high fiber concentration regions. Moreover, variation in the target pad side of microvia diameter turned out to be a significant parameter. Larger diameters withstand high stresses as compared to smaller microvia diameters in resin-rich areas of the substrate. For example, the average overall impact of stresses on microvia with a diameter of 150 μm is 15% smaller than the microvia with a diameter of 80 μm at the high microvia failure site on the substrate layer. Finally, the developed model was also correlated with the literature models to verify the accuracy. The results provide great microvia design considerations at resin-rich sites to avoid early failures.