bluuebunny/crossref_metadata_2025 huggingface dataset:
Semi-Automated Mining of Customer Reviews to Identify Design Problems of Cantilever-Style Bassinets
Authors: Singhose William , Adams Christopher , Martinez Rebecca , Rana Anjnee , Kim Dooroo , Li Wayne
Published: November 2024

Customer reviews posted online provide a massive dataset that can identify product defects. Locating informative reviews of a specific product is straightforward. However, it can be challenging to extract information that informs product designers of potential defects and drives useful testing to investigate the potential defect. Such data extraction can be manually performed in a reliable and effective manner by skilled engineers. However, such an approach can be time-consuming and expensive. On the other hand, completely automated data extraction would likely yield generic results that are not particularly useful. This paper describes a semi-automated process wherein researchers develop an informed search algorithm that is based on an initial engineering assessment of a product that identifies potential design problems and words sets that are associated with the potential defects. An automated search process then operates on the review database to greatly streamline the data analysis process. The product reviews that indicate product defects are grouped together and associated with design features. The design features that are linked to the largest classes of complaints are then rigorously tested to evaluate the credibility and accuracy of the online complaints. The process is illustrated via a case study and testing of a cantilever-style bassinet.
Design and Testing of a 1 MW 800V Truck Testbed for High Performance Applications
Authors: Bucknor Norman , Patil Chinmaya , Raghavan Madhu
Published: November 2024

We present an overview of a 1 Megawatt 800V truck testbed that we created to study extreme acceleration, fast-charging and adjacent applications such as eVTOL propulsion. The system architecture details are presented along with the sub-system specifications. In order to get practical hands-on learnings as well as to demonstrate the operation of such high-powered systems, we designed and fabricated an 800V battery pack comprised of high-power cells. This battery pack was integrated into a 2019 Chevrolet Silverado truck to study “extreme” high power operation and the associated thermal management challenges.
Digital Twin Simulation of the Automotive Body in White Dispensed Bead Quality Validation and Inspection Process
Authors: Lekarczyk Liz , Ahmad Malik Ali
Published: November 2024

Standard joining techniques such as adhesive, sealer, and mastic dispensing play a vital role in automotive Body in White manufacturing processes. Traditionally, validation involves verifying dispensed bead placement and presence, relying on human inspectors who utilize camera and closed circuit TV (CCTV) systems for quality confirmation. Equipment validation involves assessing camera quantity and placement, typically determined post-installation through the “camera field of view” placement method. Unfortunately, this approach has led to purchasing excessive or sometimes unnecessary equipment, contributing to elevated launch costs. This publication introduces a Digital Twin approach for predicting the quantity and field of view of passive vision cameras used in dispensed bead applications. The method employs simulation to validate the process before actual integration. Additionally, the placement of beads undergoes validation through Virtual Reality simulation, enhancing training for quality inspectors and thereby increasing the number of inspections achievable per shift. The use of Digital Twin and Virtual Reality modeling also results in improved development time for quality inspection documentation, a task typically handled by Quality Engineers. The proposed procedure has demonstrated noticeable reductions in costs, inspection documentation development time, and an enhancement in quality validation procedures. Ongoing research aims to explore the impact of increased inspection frequency within manufacturing facilities, with a specific focus on the implications for reprocessing time.
Towards Real-Time Validation of Rail-Guided Shuttles: A Multibody Modelling and Digital Twin Approach
Authors: Nezzi Chiara , De Marchi Matteo , Vidoni Renato , Rauch Erwin
Published: November 2024

In the world of multibody dynamics, models describing the behavior of rail-guided vehicle systems usually start from characterizing the body adhesion to a predetermined path, on which the body keeps a specified orientation associated with the spatial track curve. This is also the case of the rail-guided shuttles addressed by this work, which move along a planar path built by a small-size transfer line in a laboratory environment. Firstly, the multibody model of the shuttles to ensure their prescribed motion constraint along the path is described. Secondly, a physics-based 3D simulation software is exploited for the simulation of the shuttles’ kinematics and dynamics in the virtual world. Lastly, the validation and real-time synchronization with the physical system are done via REST API communication. Empirical results of shuttles’ travelling times between the workstations along the path are collected both in the real and simulated system. Results are then compared to test and validate the simulation accuracy. Insights and considerations regarding the role of Digital Twin in the system are also given. In conclusion, possible future improvements are provided.
Kinematic and Curvature Analysis of the McPherson Eight-Bar Suspension System
Authors: Lanni Chiara , Figliolini Giorgio , Tomassi Luciano
Published: November 2024

This paper deals with the kinematic and curvature analysis of the McPherson eight-bar suspension system that is developed by referring to a suitable planar car model, where both front wheels are supposed as rotating about their fixed revolute joints. In particular, the position analysis of the eight-bar mechanism, with an internal six-bar loop, is formulated to analyze the chassis roll motion by devoting particular attention to the roll center and its fixed and moving centrodes. Consequently, the curvature analysis is also carried out by using the Euler-Savary equation and the inflection circle, in order to determine the center of curvature of the mass center trajectory for the chassis roll motion. Finally, the proposed formulation has been implemented in a Matlab program and several examples allowed its validation in different mechanism configurations.
Tooth Contact Analysis of Spur and Helical Gears in Case of Shafts Deflections
Authors: Cazan Stelian , Creţu Spiridon
Published: November 2024

The paper presents semi-analytical methods (SAMs) for determination of pressures distributions on the flank surfaces of spur and helical gears with both pinion and gear shafts deformed. In case of spur gear pair, a nonstandard gear pair was chosen. It is well known that the curvature of the tooth profile decreases when a negative correction of the gears is made, this thing leading to higher contact pressures and a decrease of durability. The SAMs starts from Hartnett’s approach based on Boussinesq equation from half-space theory of linear elasticity, which means implicitly an infinite width of gear’s flank. To simulate the more realistic quarter-space conditions, a correction based on virtual mirror pressures is introduced in the computational algorithm. For spur gears, the elements of the matrix of separations are obtained using analytical equations derived from involute properties while a methodology dedicated to helical gears was developed. The pressures distributions obtained with worked up SAMs have been validated by comparisons with results obtained by neutral research by using FEM or a dedicated SAM. The present paper took into consideration both deflections of pinion and gear’s shaft, so that a more realistic pressures distribution is obtained. The contact surfaces are considered smooth surfaces and the contact between mating surfaces is considered as dry contact.
Comparative Analysis of Real-Time and Simulated Monitoring Techniques for MIG Welding
Authors: Salazar Carlos D. , Longoria Raul G.
Published: November 2024

This study introduces two methodologies designed to monitor welding procedures, furnish feedback to users, and thereby refine the welding technique of novice welders. The first method introduces a low-cost welding simulator utilizing augmented reality (AR) technology. This simulator provides real-time visual feedback on essential welding parameters such as the contact tip to work distance (CTWD), work angle, travel angle, displacement, and travel speed. The second approach involves a welding sensor unit (WSU) attached to the actual welding torch, enabling monitoring and analysis of welding parameters in a real welding environment. Experimental verification of both methods demonstrates their effectiveness and accuracy. The simulator accurately replicates human performance within the real welding environment, enabling real-time visual feedback to enhance welding techniques. The WSU, equipped with economical sensors, is feasible for monitoring real-world welding scenarios. These methodologies have diverse applications, including welder training, quality control, and the generation of synthetic data for research and industrial purposes. An investigation into the influence of welding parameters on weld bead formation underscores the critical role these parameters play in shaping both the geometry and quality of welds. Through careful analysis, it becomes evident that factors such as Contact Tip to Work Distance (CTWD), travel speed, and working angles significantly impact the appearance and integrity of weld beads.
Enhancing Efficiency of Human Pickers in Strawberry Harvesting With Quadruped Robots
Authors: Chen Yiyu , Chen Shengqiang , Zhang Xiaopan , Racano Dario , Vougioukas Stavros G. , Nguyen Quan , Gupta Satyandra K.
Published: November 2024

In this work, we present a step toward boosting human picker efficiency in strawberry harvesting using quadruped robots. Transitioning from manual to automated harvesting in the fruit industry, especially for fresh-market strawberries, remains a challenge due to the delicate nature of the fruit and unique field layouts. A notable inefficiency in manual harvesting is the time human pickers spend transporting filled trays to collection stations. Quadruped robots, renowned for their adaptability in challenging terrains, emerge as promising candidates for mobile crop transportation. Extending the foundational concepts from previous work, we introduce quadruped robots as auxiliary support units specifically tailored to boost human picker efficiency. Such legged platforms have demonstrated their mobility in agricultural scenarios. The robots can traverse various agricultural terrains, including rugged and muddy furrows in the field and main irrigation lines at the headland. Beyond advancements in transportation robot hardware, our system introduces a novel predictive scheduling algorithm, aiming to enhance harvesting efficiency. Specifically, this algorithm is engineered for real-time replanning, to account for unpredictable picker behaviors. Our empirical results show that the integration of quadruped robots leads to 15% decrease in total makespan of the task and a 72% decrease in non-picking time, compared to traditional manual methods.
Characterization of Tool-Specimen Interaction in Non-Repetitive Mechanical Micro-Drilling Tasks Performed by Collaborative Robot Manipulators
Authors: Wang Xiangyu , Dong Siqin , Kaipa Krishnanand
Published: November 2024

Mechanical micro-drilling finds widespread use in diverse applications ranging from advanced manufacturing to medical surgery. Our work is focused on developing methods enabling robots to perform non-repetitive micro-drilling tasks effectively. Achieving this goal encounters many challenges. Even small variations in drill process parameters cause the micro-drills to break frequently. In this paper, an experimental approach is proposed to conduct a systematic characterization of the interactions between the micro-drill tool and the specimen, which is crucial to understanding the underlying processes and developing effective methods of robotic micro-drilling. Specifically, we carry out an experimental investigation of the relationship between drill feed-rate and reaction forces experienced by the micro-drill as it contacts and penetrates the specimen. The Sawyer and KUKA collaborative robots were used as the test beds during experimental validation as they were found to be suitable candidates for implementing the micro-drilling strategies proposed in this work. The Photron Fastcam Nova S6 high-speed camera was used for video-based observation of microdrilling, revealing the temporal details of interaction between the microdrill tool and the specimen, with a high time-resolution of a few microseconds to milliseconds between successive frames. The experimental results consisted of characterization studies that involved the execution of several micro-drill plans and analyzing the drill tool-surface interactions. In particular, graphs of temporal and spatial variations of feedrate and reaction forces were obtained and used to identify and characterize different stages — approach, touchdown, penetration, drill-through, coming-to-a-halt, and retraction — of each executed micro-drill plan. A regression analysis revealed a relationship between feed rate and reaction forces involved in the micro-drilling process that matched the underlying mathematical model of the tool-specimen interactions. Experimental results show that the Sawyer and KUKA robots can use the developed strategies to drill micro-holes of diameters up to a minimum of 0.6 mm and 0.2 mm, respectively.
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

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

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

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

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

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

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

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

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

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

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

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

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.
Effect of Aspect Ratio and Flow Rate on the Flow Regimes and Thermal Performance of Two-Phase Micro-Channel Cold Plates for Direct to Chip Cooling
Authors: Shah Yousaf , Ortega Alfonso , Mehrabi-kermani Mehdi , Martinez Victor A. , Manaserh Yaman , Heydari Ali , Rodriguez Jeremy
Published: November 2024

Liquid cooling technology is experiencing rapid acceptance in high-speed computing and data centers, driven by the remarkable advancements in these sectors that have resulted in unprecedented increase in thermal loads that are anticipated to surpass the capabilities of single-phase liquid cooling. Two-phase direct to chip cooling is a promising alternative for managing these high loads. The majority of the literature has investigated thermal performance enhancement in two-phase cooling utilizing small scale microchannel structures suitable for fundamental studies. For actual deployment of the two-phase liquid cooling technology, it is of practical importance to understand flow and cooling in full-scale devices. This paper will present an experimental study on the thermal performance of full-scale microchannel cold plates, employing thermal test vehicles (TTV) as the heat source to simulate the heat generation rates in practical applications. The study focuses on characterizing a single cold plate, but the work is part of a larger study simulating data center thermal loads in rack level experiments employing multiple rack level thermal test vehicles in a rack with distribution manifolds feeding each server TTV. Simulated cold plates, suitable for fundamental laboratory studies, were used in this study. The cold plates had skived fins on a copper base, with suitable inlet and outlet manifolds. The lid was made of transparent acrylic to allow for flow visualization from the top of the fins. The cold plate had a base area of 50 by 50 mm with rectangular channels having a fixed height of 4 mm, identical channel width and fin-pitch of 0.1 mm, 0.2 mm, 0.3 mm, 0.4 mm, and channel length of 50 mm. Tests were conducted in steady state conditions using R-134a as the working fluid. Flow rate varied between 1.2 to 3.5 LPM and the fluid inlet temperature was maintained at 17° C. The effect of fin-pitch and the liquid flow rate on the thermal performance was evaluated by measuring base temperature for applied power up to 3200 W. Flow regimes and flow instability were investigated utilizing high-speed high-resolution video flow visualization showing the effects of mass flux and heat flux on the flow and heat transfer at different aspect ratios. The effect of fin aspect ratio, heat flux, and mass flux on the flow regimes, instabilities, and the average heat transfer coefficients were investigated. At all mass fluxes, the boiling curves showed no sign of criticality. For all aspect ratios, an optimum exit flow quality was found that maximized the average heat transfer. Furthermore, it was found that there is an optimum fin aspect ratio that maximizes the overall heat transfer; this aspect ratio is distinct from what is normally found for single phase flow.
A Thermal Reduced Order Model for Power Throttling Simulations of a 3D IC
Authors: Geb David , Deodhar Subodh , Netake Nitin , Jeurkar Tejas
Published: November 2024

Power throttling with dynamic voltage and frequency scaling (DVFS) is important to the dynamic thermal management (DTM) of processors. With increasing numbers of on-chip thermal sensors monitoring temperature conditions, optimizing the DVFS control logic with design tools becomes increasingly important due to the problem’s complicated nature. Furthermore, three-dimensional integrated circuits (3D ICs) with vertically stacked chips present additional challenges as cross-die thermal coupling, increased power densities, and localized hot spots, among other concerns, become more important. Effective thermal simulation methods of 3D ICs with DVFS power throttling and external cooling control can serve as an important tool in both chip and system co-design. However, overcoming the long simulation times inherent in transient thermal simulations is a key hurdle to overcome. In this paper, a dynamic thermal analysis method that includes handling of DVFS for a 3D IC example, with cooling control, is presented. A test case is prepared in which dynamically varying heat loads are imposed on the 3D IC over a 60 second operating time frame. The DVFS and cooling control react to the operating conditions measured at the on-chip temperature sensors in the 3D IC. A 3D field model is prepared from which a Linear Parameter Varying (LPV) Reduced Order Model (ROM), with variable cooling effects represented by a scheduling parameter, is obtained. The 3D field model provides the training data upon which the ROM is built. The power throttling algorithm is defined, specifically in a Python script, which is then applied within both the 3D field model and the ROM. The external cooling control is implemented via a PID controller in the model. The simulation results demonstrate both the on-chip and cross-chip hot spot thermal coupling (both strong and weak coupling is observed) along with variable cooling impacts on the 3D IC test case. Furthermore, temperature prediction verification of the ROM with the DVFS power throttling algorithm applied is obtained to within 1% over the 60 second time frame. Finally, the value of the ROM as a 3D IC thermal design tool is presented in terms of solution speed up, compared to the 3D field model representation, with a speedup factor of 61 being observed in this example. ROM speedup can be significantly higher as the corresponding 3D field model’s mesh size increases. The methodology presented in this paper can serve as the basis for improved 3D IC chip and system DTM, with simulation-based DVFS and cooling controller design. Furthermore, the method can be extended to other thermal ROM technologies.
Fabrication and Passive Assembly of Compact, Broadband Substrate-To-Die Evanescent Couplers for Sustainable Pbps Co-Packaged Optics
Authors: Weninger Drew M. , Serna Samuel , Ranno Luigi , Kimerling Lionel , Agarwal Anuradha
Published: November 2024

The co-packaging of optics and electronics provides a potential path forward to achieving Pbps per package data capacities. In a co-packaged design, the scaling of bandwidth, cost, and energy is governed by the number of optical tranceivers per package as opposed to transistor shrink. The scaling of optical transceivers is hindered by the need to actively assemble bulky single mode fiber arrays directly to optical transceivers. The development of efficient, dense optical couplers with wide alignment tolerances allowing for automated, passive assembly will be an enabler for continued scaling in co-packaged designs. To this end, a passively assembled substrate-to-die evanescent coupler between silicon nitride and silicon was experimentally demonstrated with a 0.39 ± 1.06 dB coupling loss at 1550 nm, a 160 nm 1-dB wavelength tolerance (1480–1640 nm), and a 1-dB lateral alignment tolerance of ± 1.56 μm. The thermal stability was evaluated from 23–60°C with average coupling loss and alignment tolerance varying by less than ± 0.35 dB and ± 30 nm, respectively. Finally, repeatability was evaluated across four packaged systems, demonstrating a coupling loss range of 1.5 dB. Together, these results show this coupler can help achieve Pbps co-packaged optics input/output (I/O) within a sustainable envelope.
Modelling Effect of Isothermal Aging of Anisotropic SaAgCu(SAC) Solder Joint Using Crystal Viscoplasticity
Authors: Bharamgonda Aniket , Dasgupta Abhijit , Chen Yaxiong , Hauck Torsten
Published: November 2024

SnAgCu (SAC) solder joints have oligocrystalline (few large grains) structure, with each grain showing strongly anisotropic behavior due to body-centered tetragonal (BCT) structure of Sn crystals. SAC solder joints go through microstructural evolution which directly affect their life during the life time o solder joint, Crystal viscoplasticity (CV) model has been used in the literature for demonstrating anisotropy of solder grains and is used in this paper, to demonstrate the effect of isothermal aging on anisotropic creep properties of solder grains. Changes in secondary creep strain rate of solder grains along principal crystal directions is demonstrated in this article.
Predictive Machine Learning Models for LiDAR Sensor Reliability in Autonomous Vehicles
Authors: Farahani Saba A. , Lee Jae Yun , Kim Hunjae , Won Yoonjin
Published: November 2024

The emergence of autonomous vehicles marks a transformative moment in the transportation sector, significantly propelled by the integration of Light Detection and Ranging (LiDAR) technology. LiDAR revolutionizes how vehicles perceive their environment, emitting laser beams to measure distances to objects and creating highly accurate three-dimensional maps. This innovation is pivotal for enhancing the operational efficiency and safety of autonomous vehicles by providing instantaneous and detailed environmental mappings. However, the efficacy of LiDAR sensors is compromised by environmental factors such as dust, dirt, snow, and rain, which can severely affect their accuracy and, consequently, the safety of the vehicles they guide. Despite the importance of this issue, the research field has shown a notable lack of investigation into predicting LiDAR sensor contamination and its reliability. This gap underscores a critical need for dedicated research efforts to ensure the reliability and safety of autonomous driving technologies, making it a pressing challenge for researchers and developers alike. Therefore, it is imperative to develop a novel way to detect LiDAR sensors’ reliability like contamination level.

In response to the urgent need to address LiDAR sensor contamination, our team have initiated a comprehensive data collection journey, spanning from California’s diverse climates to Michigan’s variable weather conditions. This dataset contains multi-level features such as contamination levels on multiple sensors, environmental factors, and sensor images across varying weather conditions and geographical locales. The dataset is a groundbreaking contribution to the field, playing a vital role in developing our machine learning models and offering novel insights that could dramatically advance research and practical applications.

In this work, our framework employs machine learning methods across two distinct but complementary strategies: sensor data analysis and image data analysis. The first strategy indirectly predicts the contamination levels by leveraging environmental variables, such as temperature, humidity, precipitation, and vehicle speed. This indirect method represents a novel approach to assessing sensor contamination, offering a significant advantage by allowing for the prediction of contamination risks before they impact sensor performance. The second strategy directly predicts the contamination levels by visually assessing LiDAR sensors under various weather conditions. The integrated strategy better performs in terms of the contamination level predictions, which is meaningful to ensure safer navigation in complex environments. Together, by introducing a novel dataset and predictive machine learning models, our framework shows a potential to address a critical gap in existing research.