bluuebunny/crossref_metadata_2025_split huggingface dataset:
Model Consistency for Mechanical Design: Bridging Lumped and Distributed Parameter Models With A Priori Guarantees
Authors: Wang Randi , Behandish Morad 
Published: November 2023

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.