bluuebunny/crossref_metadata_2025 huggingface dataset:
Forms of fiscal governance in the Euro Area – An update
Authors: Catania Moira , Baimbridge Mark , Litsios Ioannis
Published: November 2023

Despite the common Euro Area (EA) requirements, we find notable differences in the quality of fiscal governance among the 19 member states. Moreover, characteristics of the delegation approach, which have been largely ignored in the EA fiscal governance framework, remain important in various member states. Using a two‐way fixed effects panel data model for the EA countries during 2006–2018, we find that the delegation approach can be effective to improve the fiscal position. On the other hand, the imposition of centrally mandated common rules‐based reforms has not taken into account the national political, social and institutional setting, and this may have also affected their effectiveness to achieve fiscal discipline. Our findings thus suggest a reconsideration of the one‐size‐fits‐all, rules‐based approach to fiscal governance in the EA.
Rapid screening for suicide risk: An algorithmic approach
Authors: Dodge Matthew C. , Hicks Adam D. , McCord David M.
Published: November 2023

Introduction

In the United States, primary medical care settings are the first accessed resource for both medical and behavioral health care. Thus, there is a clear need for accurate and efficient behavioral health screening in this setting, including routine surveillance screening for suicide risk. The Multidimensional Behavioral Health Screen (MBHS), a broadband but very brief screening tool developed specifically for primary care, has been updated to include an algorithm that classifies suicide risk based on the interpersonal‐psychological theory of suicide, and associated interview and decision framework. This study aims to evaluate the predictive accuracy of the new MBHS 2.0 suicide risk algorithm, with actual risk determined by clinical suicide risk interview.

Method

Data were collected as part of a larger study that, overall, included 551 college student participants. Of these, 309 completed the MBHS 2.0 and the clinical suicide risk interview, the two measures reported here. The final participant count was 299 following the removal of incomplete or invalid cases. Predicted suicide risk as determined by the MBHS 2.0 (Low, Mild, At least Moderate) was compared to actual risk as determined by clinical interview (Low, Moderate, Severe, Extreme).

Results

Utilizing chi‐square analyses, data show a significant association between the predicted suicide risk category based on the MBHS 2.0 algorithm and the actual risk category based on the semi‐structured clinical interview. Furthermore, classification analyses suggest that primary care providers will be able to confidently assess the suicide risk level for the majority of their patients when using the MBHS.

Conclusion

Findings suggest that the MBHS 2.0 can be an accurate and efficient tool for use by primary care providers in classifying suicide risk. Future research will be useful to evaluate the utility of the suicide risk algorithm among primary care populations.
Observation on cadavers and through ultrasonography using a 2 mm needle length for intradermal injections
Authors: Yi Kyu‐Ho , Lee Brian , Kim Michael James , Lee Seo‐Hyun , Hidajat Inneke Jane , Lim Ting Song , Kim Hyoung Moon , Kim Jin‐Hyun
Published: November 2023

Background

An intradermal injection is a medical procedure that involves administering a small amount of medication or substance into the dermal layer of the skin. This research focused on identifying the most suitable injection needle for precise intradermal administration of skin boosters.

Methods

The study involved conducting intradermal injections on four cadavers and participants using a 2 mm length, 34‐gauge needle (N‐Finders, Inc., South Korea). During the cadaveric study, the polynucleotide prefilled syringe was dyed green, and an anatomist performed dissections, removing only the skin layer. Ultrasonographic observations were carried out to ensure accurate intradermal injection placement.

Results

In all four cadavers, the facial injections at the anterior cheek region were precisely administered intradermally at a 30‐degree injection angle. However, the 90‐degree injection was found just below the dermal layer upon skin layer removal.

Discussion

The findings suggest that using a 2 mm needle length allows for easy and convenient intradermal injections.
Sequential Ahmed valve implantation after primary implant surgery in two dogs
Authors: Lee Songhui , Kim Hyun‐ah , Seo Kangmoon , Kang Seonmi
Published: November 2023

Objective

To report the outcomes of sequential Ahmed valve implantation in two dogs with uncontrolled intraocular pressure (IOP) after primary Ahmed valve implantation.

Animals Studied

A 6‐year‐old castrated male American Cocker Spaniel (case 1) and a 12‐year‐old spayed female mixed‐breed dog (case 2) presented with glaucoma management in the left eyes of both dogs.

Procedures

Primary Ahmed valve implantation was performed because of uncontrolled IOP despite antiglaucoma medications used in both dogs. The IOP was maintained within the normal range for 14 and 2 months after surgery in cases 1 and 2, respectively. In both cases, the IOP gradually increased, which was assumed to be caused by bleb fibrosis. In particular, in case 2, one bleb revision surgery was performed during that period, but it was unsuccessful. Therefore, we decided to perform sequential implantation surgery.

Results

In case 1, hyphema occurred the day after sequential surgery and resolved with anti‐inflammatory treatment. In case 2, it occurred immediately after the surgery and resolved after surgical blood clot removal. IOP remained below 15 mmHg, and menace response was positive until 40 and 23 months after the sequential surgery in cases 1 and 2, respectively. As a long‐term complication, cataracts occurred at 17 months in case 1 and 10 months in case 2.

Conclusions and Clinical Relevance

Sequential implantation surgery in the same eye could be an effective alternative for long‐term IOP management and vision maintenance. A better understanding of complications and appropriate postoperative management is essential for better outcomes.
Three‐dimensional versus two‐dimensional laparoscopy for cholecystectomy in a canine cadaveric study
Authors: Azuma Kazushi , Monnet Eric
Published: November 2023

Objective

To compare the effect of three‐dimensional (3D) and two‐dimensional (2D) laparoscopy on surgical time and intraoperative complications during cholecystectomies in canine cadavers.

Study design

Experimental prospective study.

Animals

Twelve canine cadavers.

Methods

The laparoscopic cholecystectomies were performed in canine cadavers with four cannulas and with either 2D or 3D cameras by a single surgeon. The following surgical times were recorded: time from insertion of laparoscopic instruments to first endoclip placement, time from first endoclip placement to second endoclip placement, time from second endoclip placement to complete gall bladder dissection, and total surgical time. The cystic duct length distal to the first endoclip, intraoperative complications, and the amount of liver attached to the gall bladder were also recorded.

Results

Time from the insertion of the instruments to the application of the first endoclip was shorter for the 3D group than for the 2D group (p = .016). Other surgical times were not different between groups. There was no difference in the cystic duct length distal to the first endoclip, intraoperative complications, or the amount of liver attachment.

Conclusion

Three‐dimensional laparoscopy shortened the time from insertion of the laparoscopic instruments to placement of the first endoclip. However, total surgical time was not affected by the type of camera in laparoscopic cholecystectomy.

Clinical significance

Three‐dimensional laparoscopy may be beneficial when performing procedures requiring enhanced spatial orientation. However, this technique did not shorten the total surgical time in this cadaveric study. Further studies in a clinical setting are necessary for the optimization of the future use of 3D laparoscopy.
A Product Life Cycle Approach to Medical Device Development for Low-Resource Settings - A Systematic Literature Review
Authors: Overhoff Milena , Lumpe Thomas S. , Shea Kristina
Published: November 2023

Rural areas in most of the world lack functional medical devices for essential care. Even though devices exist on the global market, they are often not accessible in low-resource settings (LRS). One evident reason is the high price that many health facilities in rural areas cannot afford. However, even after the initial purchase, an estimated 70–90% of the devices are not operational. The core of this problem is often that the existing technologies were not designed for these settings, but for high-income countries with efficient healthcare systems and abundant resources. As such, the challenges for medical devices in LRS can vary significantly from high-income countries and span the whole product life cycle. This paper aims to synthesize the existing knowledge from literature about these challenges and to identify the product life cycle stages that provide the greatest potential for further research. Through a systematic keyword search with multiple exclusion rounds using the PRISMA method, 26 relevant papers are identified and analyzed according to the product life cycle stages they cover. The results reveal a lack of knowledge especially about the final life cycle stages, i.e. recycling and disposal, and a wide range of challenges for the use, consumption and maintenance stages as well as the marketing and sales of devices. The identified gaps in generalized knowledge provide a call to action for researchers and medical device developers to investigate and determine critical areas to make high quality medical devices more accessible and sustainable in LRS.
Model Consistency for Mechanical Design: Bridging Lumped and Distributed Parameter Models With A Priori Guarantees
Authors: Wang Randi , Behandish Morad
Published: November 2023

Engineering design often involves representation in at least two levels of abstraction: the system-level, represented by lumped parameter models (LPMs), and the geometric-level, represented by distributed parameter models (DPMs). Functional design innovation commonly occurs at the system-level, followed by a geometric-level realization of functional LPM components. However, comparing these two levels in terms of behavioral outcomes can be challenging and time-consuming, leading to delays in design translations between system and mechanical engineers. In this paper, we propose a simulation-free scheme that compares LPMs and spatially-discretized DPMs based on their model specifications and behaviors of interest, regardless of modeling languages and numerical methods. We adopt a model order reduction (MOR) technique that a priori guarantees accuracy, stability, and convergence to improve the computational efficiency of large-scale models. Our approach is demonstrated through the model consistency analysis of several mechanical designs, showing its validity, efficiency, and generality. Our method provides a systematic way to compare system-level and geometric-level designs, improving reliability and facilitating design translation.
Constrained Bayesian Optimization Methods Using Regression and Classification Gaussian Processes As Constraints
Authors: Jetton Cole , Li Chengda , Hoyle Christopher
Published: November 2023

In Bayesian optimization, which is used for optimizing computationally expensive black-box problems, constraints need to be considered to arrive at solutions that are both optimal and feasible. Techniques to deal with black-box constraints have been studied extensively, often exploiting the constraint model to sample points that have a high probability of being feasible. These constraints are usually modeled using regression models if the output is a known value on a continuous real scale. However, if the constraint or set of constraints is not modeled explicitly, but rather it is only known whether a design vector is either feasible or infeasible, then we treat the constraints as a categorical constraint. Because of this, these constraints should be modeled using classification models rather than regression methods, which have also been studied. Because of the variety of approaches to handling constraints, there is a need to compare methods for handling both classification constraints as well as continuous constraints modeled with individual regression models. This paper explores and compares four main methods, with two additional ones specifically for classification constraints; these methods handle black-box constraints in Bayesian optimization by modeling the constraints with both regression and classification Gaussian processes. We found that the problem type and number of constraints influence the effectiveness of different approaches with statistical differences in convergence. Regression models can be advantageous in terms of model fit time; however, this is also a function of the total number of constraints. Generally, regression constraints outperformed classification surrogates in terms of minimizing computational time, but the latter was still effective. Overall, this study provides valuable insights into the performance and implementation of different constrained Bayesian optimization techniques. This can help inform engineers on which method is most suitable for their problem, what issues they may encounter during implementation, and give a general understanding of the differences between using regression and classification Gaussian processes as constraints.
Derivation of Sensor-Actuator Layout for Active Control of Gearbox Housing Vibration
Authors: Nampally Sneha Rupa , Fontana Mauro , Nordmann Rainer , Rinderknecht Stephan
Published: November 2023

To effectively control the vibration of a complex structure like the housing of an automotive gearbox, a distributed network of point sensors and actuators is required. This work focuses on demonstrating optimal sensor and actuator placement concepts for industrial applicability for high frequency vibrations caused due to gear whine phenomenon. The goal is to minimize the transfer of vibration from gearbox housing to the car body through the structural connection points. The frequency domain of interest is 1000–5000 Hz. A network of 10 spatially distributed sensors is derived using the constrained Frequency Effective Independence method (FEfI) that maximizes the system observability of the housing vibration. In addition, an optimized two-actuator network is derived based on the concept of spatial and modal controllability in the frequency domain of interest. The study uses Frequency Response Functions (FRFs) of the gearbox housing obtained from an experimentally correlated finite element analysis. The sensor-actuator layout is tested in closed-loop simulation with the excitation using a multi-channel Filtered-X Least Means Squares (FxLMS) adaptive algorithm. The goal is to mitigate structure-borne vibration transfer from the gearbox housing at the mounting points. The derived optimal actuator layout is compared against a fully collocated actuator-sensor system. The design requirements for optimally positioned actuators can also be derived for the desired frequency domain.
A Quasi-Static Load Distribution Model for Deep Groove Ball Bearings
Authors: Dharap Gaurang , Talbot David
Published: November 2023

A computationally efficient load distribution model for deep groove ball bearings that accounts for local geometrical imperfections is developed. The generalized 5-DOF model is capable of accounting for different race supporting conditions. The model formulated consists of rigid bodies that are actual ball bearing components, and, hypothesized contact elements which mathematically represent contact. The resulting governing equation is composed of the compatibility conditions and equilibrium equations both of which relate the properties of rigid bodies to the contact elements. The initial separations, rigid body approach and elastic approach are components of the compatibility condition. The race and ball equilibrium are components of the equilibrium equations. A modified simplex style algorithm is implemented to capture contact non-linearity along with an iterative scheme that captures geometric non-linearity in the form of instantaneous contact elements. Common errors and manufacturing tolerances are then discussed with a parametric equation. An example analysis compares the base load distribution to an existing load distribution model. The load distribution with modifications to race diameter, race groove curvature, and, ± 5 μm random ball diameter variation is carried out. The load distribution and contact pressures developed for the three cases are compared with the base solution. This comparison highlights the use of and need for such a model.
Design and Modelling of Compliant Mechanisms With Invertible Poisson’s Ratio Effect for Growing Biological Cells
Authors: Sebastian Manu , Balakrishnan Sreenath , Palathingal Safvan
Published: November 2023

The behaviour of biological cells depends on the mechanical properties, such as Elastic Modulus and Poisson’s ratio, of the substrate they adhere to. Tunable materials such as polyacrylamide gels and hydrogels were previously used as substrates to understand this dependence. However, these substrates do not facilitate changing their elastic properties in situ while cells are growing on them. This work presents an alternate approach that enables this — substrates based on tunable compliant micro mechanisms. In particular, the mechanism proposed here has an invertible Poisson’s ratio effect. In the first configuration, the effect is positive, and in the second, it is negative, with any desired magnitude. We achieve this by changing the stiffness between two internal points of a mechanism with the shape of a re-entrant structure. An increase in stiffness causes the direction of deformation along the lateral axis to reverse for a given reference load along the horizontal axis. We derive analytical expressions that relate the geometric parameters to the ratio of input and output displacements for both mechanism configurations. The analytical modelling is verified with finite element analysis and experiments on mesoscale design prototypes of both configurations.
Hyperdimensional Cognitive Computing for Lightweight Cyberattack Detection in Industrial Internet of Things
Authors: Jalil Piran Fardin , Barkam Hamza Errahmouni , Imani Mohsen , Imani Farhad
Published: November 2023

Although the connectivity offered by industrial internet of things (IIoT) enables enhanced operational capabilities, the exposure of systems to significant cybersecurity risks poses critical challenges. Recently, machine learning (ML) algorithms such as feature-based support vector machines and logistic regression, together with end-to-end deep neural networks, have been implemented to detect intrusions, including command injection, denial of service, reconnaissance, and backdoor attacks, by capturing anomalous patterns. However, ML algorithms not only fall short in agile identification of intrusion with few samples, but also fail in adapting to new data or environments. This paper introduces hyperdimensional computing (HDC) as a new cognitive computing paradigm that mimics brain functionality to detect intrusions in IIoT systems. HDC encodes real-time data into a high-dimensional representation, allowing for ultra-efficient learning and analysis with limited samples and a few passes. Additionally, we incorporate the concept of regenerating brain cells into hyperdimensional computing to further improve learning capability and reduce the required memory. Experimental results on the WUSTL-IIOT-2021 dataset show that HDC detects intrusion with the accuracy of 92.6%, which is superior to multi-layer perceptron (40.2%), support vector machine (72.9%), logistic regression (84.2%), and Gaussian process classification (89.1%) while requires only 300 data and 5 iterations for training.
Scalable Probabilistic Modeling and Machine Learning With Dimensionality Reduction for Expensive High-Dimensional Problems
Authors: Luan Lele , Ramachandra Nesar , Ravi Sandipp Krishnan , Bhaduri Anindya , Pandita Piyush , Balaprakash Prasanna , Anitescu Mihai , Sun Changjie , Wang Liping
Published: November 2023

Modern computational methods involving highly sophisticated mathematical formulations enable several tasks like modeling complex physical phenomena, predicting key properties, and optimizing design. The higher fidelity in these computer models makes it computationally intensive to query them hundreds of times for optimization. One usually relies on a simplified model, albeit at the cost of losing predictive accuracy and precision. Towards this, data-driven surrogate modeling methods have shown much promise in emulating the behavior of expensive computer models. However, a major bottleneck in such methods is the inability to deal with high input dimensionality and the need for relatively large datasets. In certain cases, the high dimensionality of the input space can be attributed to its image-like characteristics, for example, the stress and displacement fields of continuums. With such problems, the input and output quantity of interest are tensors of high dimensionality. Commonly used surrogate modeling methods for such problems suffer from requirements like many computational evaluations that precludes one from performing other numerical tasks like uncertainty quantification and statistical analysis. This work proposes an end-to-end approach that maps a high-dimensional image-like input to an output of high dimensionality or its key statistics. Our approach uses two main frameworks that perform three steps: a) reduce the input and output from a high-dimensional space to a reduced or low-dimensional space, b) model the input-output relationship in the low-dimensional space, and c) enable the incorporation of domain-specific physical constraints as masks. To reduce input dimensionality, we leverage principal component analysis, coupled with two surrogate modeling methods: a) Bayesian hybrid modeling and b) DeepHyper’s deep neural networks. We demonstrate the approach’s applicability to a linear elastic stress field data problem. We perform numerical studies to study the effect of the two end-to-end workflows and the effect of data size. Key insights and conclusions are provided, which can aid such efforts in surrogate modeling and engineering optimization.
Mixed-Variable Global Sensitivity Analysis With Applications to Data-Driven Combinatorial Materials Design
Authors: Comlek Yigitcan , Wang Liwei , Chen Wei
Published: November 2023

Global Sensitivity Analysis (GSA) is the study of the influence of any given inputs on the outputs of a model. In the context of engineering design, GSA has been widely used to understand both individual and collective contributions of design variables on the design objectives. So far, global sensitivity studies have often been limited to design spaces with only quantitative (numerical) design variables. However, many engineering systems also contain, if not only, qualitative (categorical) design variables in addition to quantitative design variables. In this paper, we integrate the novel Latent Variable Gaussian Process (LVGP) with Sobol’ analysis to develop the first metamodel-based mixed-variable GSA method. Through two analytical case studies, we first validate and demonstrate the effectiveness of our proposed method for mixed-variable problems. Furthermore, while the new metamodel-based mixed-variable GSA method can benefit various engineering design applications, we employ our method with multi-objective Bayesian optimization (BO) to accelerate the Pareto front design exploration in many-level combinatorial design spaces. Specifically, we implement a sensitivity-aware design framework for metal-organic framework (MOF) materials that are constructed only from qualitative design variables and show the benefits of our method for expediting the exploration of novel MOF candidates from a many-level large combinatorial design space.
Simulating the Adoption and Social Impact of Improved Cookstoves in Uganda Using Agent-Based Modeling and Neural Networks
Authors: Mabey Christopher S. , Peiffer Erin E. , MacCarty Nordica , Mattson Christopher A.
Published: November 2023

This paper presents a methodology for predicting the adoption and social impact of a product using agent-based modeling (ABM) and neural networks to aid in decision-making related to the design and implementation of the product in a sociotechnical system. The collection of primary data on the social impact of a product is also outlined. Although this paper illustrates the method for improved cookstoves in Uganda, the method can be applied to a wide range of contexts. A field study was carried out in Uganda, consisting of two phases of data collection. The data from the fieldwork was used to train a neural network to predict if an individual would adopt an improved cookstove. Data collected from surveys and the trained adoption model were used to create an ABM to estimate adoption rates and social impacts experienced by households that had adopted technology and to assess social impact indicators. The contributions of this article are a method for collecting primary social impact data on a product and how to integrate those data into a predictive agent-based social impact model. This methodology also enables the examination of leverage points in the sociotechnical system to improve the social impact of a product as it is implemented in society.
Application of Munich Agile Concept for MBSE Based Development of Automated Guided Robot Based on Digital Twin-Data
Authors: Salehi Vahid
Published: November 2023

Simulation and Virtualization of robotic systems and Autonomous Guides Vehicle (AGV) is one of the most important aspects in the future development of such systems. This paper will contribute a framework and an entire toolchain to present a holistic and systematic approach for development of future robotic AGV systems. In the first section this paper will present the first descriptive study which is the literature survey and the definition of the scientific gaps and research questions related to systematic approaches based on Systems Engineering and Model Based System Engineering. In section number two this paper will present a new approach and framework called “Munich Agile Concept for Modell based Systems Engineering (MAGIC)” to develop entire virtual tool chains for virtualization and simulation of different functions in a virtual environment. This aspect is one of the important contributions of this paper. MBSE is based on three important core pillar which is 1) Methods/Processes, 2) Language and 3) Systems. The purpose of the new developed Munich Agile Concept Approach is to handle the complexity over the entire holistic information framework for simulation and virtualization of AGV function development from the system requirement definition process up to the test and validation of the system. Furthermore, with MAGIC there is a holistic approach and method to carry out the entire virtual development process by means of capable simulation and virtualization tools based on MAGICLOOP. The Munich Agile Concept contains six different levels which are System Requirement-, System Functional-, System Architecture-, System Validation-, System Test and the System-Usage-Level. For defining the first three-level, a graphical language called System Modelling Language (SysML) has been applied. The third chapter will apply the developed approach in an industrial Use Case which is based on simulation and virtualization of AGVs in a manufacturing environment. The last chapter will summaries the results of the applied Method. A lot of verification and testing are required robot and AGV development. ore the autonomous driving system can be deployed. However, testing in a real world will consume a lot of resources and may also bring security risks. Therefore, simulation is particularly important for autonomous driving systems.
Investigation of Isotropic Superfinishing Effects on Ground Hypoid Gears
Authors: Xu Hai , Heaton Jeff
Published: November 2023

Isotropic superfinishing has been demonstrated by many studies that it can have positive influence on friction/heat reduction and surface fatigue life improvement for sliding/rolling contact elements such as bearings and gears. However, superfinished gears are still not widely used in automotive transmissions and axles. The tradeoff between cost and benefit is certainly an important reason, but more importantly it is perhaps due to the lack of evidence in demonstrating the effect of isotropic superfinishing on several gear parameters and performances, such as process inherited micro-geometry, contact pattern and noise characteristics alteration. Published data are particularly limited on superfinishing effects on axle hypoid gears. On the other hand, over the past several years, superfinishing processes for gears have been evolved since decades ago. It could take up to a couple hours to process a typical hypoid gear set to a desired surface finish level in the past. With today’s latest technology, it can be done within a few minutes. In this paper, a study has been performed to investigate the effects of super finishing process on hypoid gears by using a vendor’s latest isotropic superfinishing technology. Experimental hypoid gears hard finished by grinding were used such that the tooth geometry was precisely controlled prior to superfinishing. The final surface finish processed with different cycle times were compared to its original ones. The amount of material removal and micro-geometry change were quantified through CMM measurements. Contact pattern and motion transmission error (MTE) before and after superfinishing were taken by using a Single Flank Tester (SFT). Finally, two samples were built into axles and tested on a noise and vibration (NV) dynamometer to evaluate and compare the NV performances with standard non-superfinished gears.
Power Loss and Damage Behavior of Gears Operating Under Loss of Lubrication
Authors: Morhard Bernd , Paschold Constantin , Lohner Thomas , Stahl Karsten
Published: November 2023

Geared turbofans need to withstand loss of lubrication due to inevitable off-design conditions. Additionally, loss of lubrication specifications that have to be fulfilled are getting stricter. For a loss of lubrication event, today’s geared turbofans are equipped with secondary oil supply systems. Their omission would reduce space and weight, and consequently, carbon emissions. However, this requires geared transmissions to withstand loss of lubrication events. In order to enable this, knowledge of the power loss and damage behavior of gears under loss of lubrication is required first. In this study, power loss and bulk temperature measurements of test gears under loss of lubrication on a FZG gear efficiency test rig are presented. Hertzian pressures in the pitch point up to 1723 N/mm2, and circumferential speeds up to 20 m/s were considered. The experimental results show the characteristics of increasing power loss and bulk temperatures under loss of lubrication depending on load and speed. At moderate operating conditions, no damage occurs within 20 seconds under loss of lubrication, whereas a load increase results in slight scuffing, and a speed increase results in severe scuffing. Oil centrifugation has a strong effect on the loss of lubrication performance. Additional experiments under minimum quantity lubrication demonstrate the impact of the remaining oil on the survivability of gears facing loss of lubrication.
Tackling an Exact Maximum Stress Minimization Problem With Gradient-Free Topology Optimization Incorporating a Deep Generative Model
Authors: Kato Misato , Kii Taisei , Yaji Kentaro , Fujita Kikuo
Published: November 2023

Maximum stress minimization is an active research topic in topology optimization. In general, gradient-based topology optimization has been used to solve such a problem, but it fundamentally faces problematic aspects. For example, singularity problems require relaxation treatments. They transform an exact minimax problem, such as the maximum stress minimization problem, into any differentiable pseudo-problem. Therefore, gradient-based topology optimization can be regarded as a low-fidelity optimization that deals with pseudo-models. Our interest is that there is room for improvement regarding the exact maximum stress in the optimized designs of the conventional method. To clarify, we focus on data-driven multifidelity topology design (MFTD) that optimizes topology without gradient information, even under a high degree of design freedom. Its basic idea is based on evolutionary algorithms (EAs). In the optimization process, the initial design candidates are generated by solving low-fidelity optimization problems. Then, they are iteratively updated a deep generative model by conducting high-fidelity forward analyses. This paper tackles a bi-objective problem of the exact maximum stress and volume minimization by data-driven MFTD incorporating initial solutions composed of the optimized designs derived by solving the gradient-based topology optimization using the p-norm stress measure. The numerical results indicate that the optimized designs by data-driven MFTD completely dominate the initial solutions and achieve a volume reduction of up to 10% under the same maximum stress value.
CFD Modelling of an Internal Gear Pump for High Viscosity Fluids
Authors: Fresia Paola , Rundo Massimo , Brignolo Gianpaolo , Ferrarese Giulio
Published: November 2023

The paper presents the CFD modelling of an internal gear positive displacement pump used for pumping high viscosity fluids. The aim of the study is to show the proper methodology for the evaluation of the delivered flow rate in conditions of incomplete filling. The model was developed in SimericsMP+ with the general gear template. It features axial and radial feeding of the variable chambers and all leakage passageways. The model was calibrated in terms of leakage flow rate in conditions of complete filling, then validated on the steady-state flow rate as a function of the suction pressure. Good agreement was found in the evaluation of the critical operating condition for which the incomplete filling begins. Based on the obtained results, it has been demonstrated that the simulation methodology can be used with a good degree of confidence for predicting the pump behavior. Hence, the model seems suitable for designing and optimizing such kinds of machines.
An Integrated Holistic Approach Toward Sustainable Product Design Using Life Cycle Assessment
Authors: Basereh Taramsari Hossein , Mudhar Simerjeet , Hoffenson Steven
Published: November 2023

The number of sustainable design methods emerging in recent years has increased due to global environmental concerns. Several proposed methods focus exclusively on the environmental impacts of products rather than taking a holistic perspective that includes social and economic sustainability. Despite the advancements in tools for incorporating sustainability in the product design phase, designers are not universally adopting these tools mainly due to their fragmented or underdeveloped characteristics. This research aims to integrate change propagation and sustainable design tools within a holistic framework that can enable simultaneous consideration of design parameters’ impacts on the three dimensions of sustainability. A reusable water bottle is used to demonstrate the application of the proposed holistic, sustainable design framework. The interdependencies among these design parameters are captured using a multi-domain matrix (MDM), and then equations are defined to quantify these dependencies. Life cycle assessment (LCA) is then performed in an automated way to evaluate the product’s environmental impacts, and the investigation of its results provides details to identify critical unit processes contributing to the environmental categories. Using this framework, sensitivity analyses are able to reveal how changes to individual design parameters propagate through the model to influence the three dimensions of sustainability.
Facilitating Cost-Effective Circular Economy With an Example on Plastics
Authors: Thomas Douglas
Published: November 2023

In the global economy, plastics have become a ubiquitous material, as have the environmental impact and waste that accompany them. This article examines causes of unsustainable production activities and potential cost-effective solutions in the U.S. plastics industry. This paper estimates that approximately 3.5 % of the total U.S. environmental impact is due to plastics, measured as a weighted composite of twelve types of environmental impact. It further discusses five potential solutions to reduce these impacts: increasing product life expectancy, improving the repairability of products, increasing the use of recycling and bioplastics, and improving manufacturing efficiency. Several potential needs were identified for increasing the sustainability of plastics: the ability to differentiate product brands and models by life-expectancy, ability to differentiate product brands and models by repairability, the aggregation of plastic streams to increase recycling volume, standards for separating plastics, and the ability to differentiate products by recyclability. Bioplastics are associated with different types of environmental impact than traditional plastic and are generally considered to be less severe (environmental) impacts; however, their cost is higher, leaving a need for cost reduction efforts. For traditional plastic resin production, efficiency improvements in energy management were identified as having an average per establishment net present value of $0.92 million for a 10-year study period and for the use of plastics the net present value was $0.67 million, suggesting that there are opportunities for cost-effectively reducing their environmental impact by improving energy management.
Process-Aware Prediction of Geometric Accuracy for Additive Manufacturing via Transfer Learning
Authors: Lin Daphne , Seepersad Carolyn
Published: November 2023

While additive manufacturing offers unprecedented design freedom to engineers, challenges associated with part accuracy and quality prevent additive manufacturing from becoming as widespread as traditional manufacturing techniques. Statistical quality control is typically applied to mass-produced parts in which large numbers of identical (or similar) parts are fabricated in a production line context, but for customized, additively manufactured parts, part qualification must be performed with a much more limited number of samples — ideally, just one customized part — or the benefit of customized production is lost to the expense of repeated production and refinement. Building upon previous work that established a statistical database of geometric design allowables for a laser sintering additive manufacturing process via extensive geometric metrology, this research establishes transfer learning techniques for transferring those databases to new materials, machines, or parts. The goal of this research is to utilize transfer learning to maximize the accuracy of design allowables for new materials, machines, or parts, while limiting the required number of expensive new metrology studies. Transfer learning is implemented via a two-stage Gaussian process regression method. In the first stage, a trend model is built using pre-existing metrology data to capture general geometric characteristics of the process. In the second stage, a deviation model is trained on the difference between the pre-existing data and a small amount of data gathered from the new material/machine/process. The trend and deviation models are then aggregated to create a surrogate model referred to as the transfer learning model. The proposed method is evaluated using a multi-material polymer selective laser sintering metrology dataset. The performance of the transfer learning model is compared to that of simpler alternative models. Results show that transfer learning models are generally more accurate in predicting geometric dimensions and less sensitive to changes in training data points.
Overview of Design Considerations for Data-Driven Time Stepping Schemes Applied to Non-Linear Mechanical Systems
Authors: Slimak Tomas , Zwölfer Andreas , Todorov Bojidar , Rixen Daniel J.
Published: November 2023

Artificial neural networks (NNs) are a type of machine learning (ML) algorithm that mimics the functioning of the human brain to learn and generalize patterns from large amounts of data without the need for explicit knowledge of the system’s physics. Employing NNs to predict time responses in the field of mechanical system dynamics is still in its infancy. The aim of this contribution is to give an overview of design considerations for NN-based time stepping schemes for non-linear mechanical systems. To this end numerous design parameters and choices available when creating a NN are presented and their effects on the accuracy of predicting the dynamics of non-linear mechanical systems are discussed. The findings are presented with the support of three test cases: a double pendulum, a duffing oscillator, and a gyroscope. Factors such as initial conditions, external forcing as well as system parameters were varied to demonstrate the robustness of the proposed approaches. Furthermore, practical design considerations such as noise-sensitivity as well as the ability to extrapolate are examined. Ultimately, we are able to show that NNs are capable of functioning as a time-stepping schemes for non-linear mechanical system dynamics applications.
A Framework of Real-Time Knowledge Capture and Formalization for Model-Based Design With Spoken Annotation and Design Operations
Authors: Nomaguchi Yutaka , Morikawa Hiroyuki , Nishida Rempei , Fujita Kikuo
Published: November 2023

Knowledge capture without an extra burden on engineers is one of the most challenging issues in the engineering domain. This study proposes a framework of real-time knowledge capture and systematic formalization for the model-based design (MBD), the systems design approach where a simulation is a key component. The framework records the screen of the modeling tool as video data and spoken annotation in which an engineer verbally explains the rationale of the modeling conducted with Modelica-based software commonly used for MBD. The captured modeling tool log and spoken annotation are divided into chunks, each corresponding to a design operation, e.g., defining functions, defining system components, setting model elements, setting model parameters, and running simulations. gIBIS, the argumentation model for representing design rationale, represents the logical structure of the design process. We define design operation templates at the system and subsystem levels, which act as rules to convert the modeling tool log into gIBIS format systematically. We conduct a design case of a spinach harvesting machine with MBD software to verify the proposed framework. The results show that through non-intrusive recording of the design process, the proposed framework is effective in capturing knowledge that can be used to help understand how a model was built.
Design of Tooth Flank Modifications in Transmission Systems Considering Dynamic Misalignments
Authors: Westphal Christian , Brimmers Jens , Brecher Christian
Published: November 2023

Current challenges in gear design, such as higher speeds and increasing power density, are leading to the increasing use of power-split and high-ratio transmission systems. These requirements can be achieved with planetary and stepped planetary gear systems. However, the power split of these gearbox types results in kinematic overdetermination and a pronounced misalignment behavior. In this report, a method is developed for the design of tooth flank modifications in transmission systems, taking into account dynamic misalignments caused by manufacturing or assembly deviations. For this purpose, the simulation of the dynamic operational behavior of transmission systems with assembly or manufacturing deviations in the dynamic multi-body simulation (MBS) is described. The focus is on the modeling of the tooth contact with the force element GearForce6D developed for the MBS software Dassault Systèmes Simpack. In the following, a novel combination of the MBS with the quasi-static tooth contact analysis (TCA) is presented. Here, the misalignment and load time histories simulated in the MBS are used for the detailed calculation of the gear meshes. With the combination of the MBS and the TCA, a significant reduction of the calculation time for the variation of tooth flank modifications is possible.

The developed method is used for the design of tooth flank modifications of a stepped planetary gearbox considering a misalignment of the planet carrier. The optimized tooth flank modifications show that a significant improvement in operational behavior can be achieved by considering the dynamic misalignment in the design. The load-related misalignment of the gear components has a high influence on the dynamic operational behavior.
A Tale of Two Turrets: Transitioning Hands-On, Project-Based Learning to a Remote Environment
Authors: Kutzer Michael D. M. , DeVries Levi D. , Severson Tracie A.
Published: November 2023

This paper presents an open-source MATLAB tool developed as a partial “digital twin” for a hands-on, project-based learning (PBL) course. The aforementioned course is a semester-long, mechatronic design exercise that guides students through the interface, modeling, and control of a one-dimensional turret system integrated with an electronic NERF RIVAL-series blaster. The digital twin presented is a MATLAB-based simulation that replicates key elements of the existing in-person course for use in a remote setting. Results show a highly visual, open-source tool that accomplishes 55% of the key tasks associated with the in-person PBL course; allowing students to explore experimental modeling, control tuning, and camera-based feedback techniques in a remote setting. Source code, documentation, and installation functions for the Turret Simulation Toolbox are available at https://github.com/USNA-WRCE/TurretSimToolbox.
A GPU-Based Parallel Bound-and-Classify Method for Continuous Constraint Satisfaction Problems
Authors: Feng Wangchuan , Zhang Guanglu , Cagan Jonathan
Published: November 2023

Continuous constraint satisfaction is prevalent in many science and engineering fields. When solving continuous constraint satisfaction problems, it is more advantageous for practitioners to derive all feasible regions (i.e., the solution space) rather than a limited number of solution points, since these feasible regions facilitate innovative design concept generation and design tradeoff evaluation. Several CPU-based branch-and-prune methods and geometric approximation methods have been proposed in prior research to derive feasible regions for continuous constraint satisfaction problems. However, these methods have not been extensively adopted in practice, mainly because of their high computational expense. To overcome the computational bottleneck of extant CPU-based methods, this paper introduces a GPU-based parallel bound-and-classify method to derive feasible regions for continuous constraint satisfaction problems in a reasonable computational time. Using interval arithmetic, coupled with the computational power of GPU, this method iteratively partitions the design space into many subregions and classifies these subregions as feasible, infeasible, and indeterminate regions. To visualize these classified regions in the design space, a planar visualization approach that projects all classified regions into one figure is also proposed in this paper. The GPU-based parallel bound-and-classify method and the planar visualization approach are validated through a case study of welded beam design. The case study shows that the method and the approach can solve the welded beam design problem and visualize the result effectively and efficiently. A four-step procedure for implementing the GPU-based parallel bound-and-classify method and the planar visualization approach in practice is also outlined.