bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dynamically Weighted Ensemble of Diverse Learners for Remaining Useful Life Prediction
Authors: Nemani Venkat , Thelen Adam , Hu Chao , Daining Steve 
Published: November 2022

Abstract
A traditional ensemble approach to predicting the remaining useful life (RUL) of equipment and other assets has been constructing data-driven and model-based ensembles using identical estimators. This ensemble approach may perform well on quality data collected from a design-of-experiments test but may ultimately fail when deployed in the field because of higher-than-expected noise, missing measurements, and different degradation trends. In such work environments, the high similarity of the learners can lead to large under/overestimates of RUL, where the ensemble is only as accurate as the learner which under/overestimates RUL the least. In response to this, we investigate whether an ensemble of diverse learners might be able to predict RUL consistently and accurately by aggregating the predictions of various algorithms which are found to perform differently under the same conditions. We propose improving ensemble model performance by 1) using a combination of diverse learning algorithms which are found to perform differently under the same conditions and 2) training a data-driven model to adaptively estimate the prediction weight each learner receives. The proposed methods are evaluated using open-source run-to-failure datasets from two popular systems of prognostics research: lithium-ion batteries and rolling element bearings. Results for the lithium-ion battery case study indicate that the proposed method achieves a 12% improvement in RUL prediction accuracy and a 27% decrease in the average uncertainty calibration error when compared to ensembles without dynamic weighting, and an even larger improvement when compared to individual models.
Analyzing Expert Design Cost Estimation for Additive Manufacturing
Authors: Williams Glen , Meisel Nicholas A. , Simpson Timothy W. , McComb Christopher 
Published: November 2022

Abstract
Compared to conventional fabrication, additive manufacturing enables production of far more complex geometries with less tooling and increased automation. However, despite the common perception of AM’s “free” geometric complexity, this freedom comes with a literal cost: more complex geometries may be challenging to design, potentially manifesting as increased engineering labor cost. Being able to accurately predict design cost is essential to reliably forecasting large-scale design for additive manufacturing projects, especially for those using expensive processes like laser powder bed fusion of metals. However, no studies have quantitatively explored designers’ ability to complete this forecasting. In this study, we address this gap by analyzing the uncertainty of expert design cost estimation. First, we establish a methodology to translate computer-aided design data into descriptive vectors capturing design for additive manufacturing activity parameters. We then present a series of case study designs, with varied functionality and geometric complexity, to experts and measure their estimations of design labor for each case. Summary statistics of the cost estimates and a linear mixed effects model predicting labor responses from participant and design attributes was used to estimate the significance of factors on the responses. A task-based, CAD model complexity calculation is then used to infer an estimate of the magnitude and variability of normalized labor cost to understand more generalizable attributes of the observed labor estimates. These two analyses are discussed in the context of advantages and disadvantages of relying on human cost estimation for additive manufacturing forecasts as well as future work that can prioritize and mitigate such challenges.
Transforming Hand-Drawn Sketches of Linkage Mechanisms Into Their Digital Representation
Authors: Purwar Anurag , Nurizada Anar 
Published: November 2022

Abstract
This paper presents a deep neural network based approach for interactive digital transformation and simulation of n-bar planar linkages consisting of both revolute and prismatic joints from hand-drawn sketches. Instead of taking a pure computer vision approach, we combine the output of a convolutional deep neural network with the topological knowledge of linkage mechanisms to create a framework for recognition of hand-drawn sketches. To accomplish this, we first synthetically generate a dataset of images of linkage mechanism sketches similar to hand-drawn ones and then fine-tune a state of the art deep neural network capable of detecting discrete objects. While the network had previously been exposed to only a general class of images of every-day objects, it was for the first time trained with a set of building blocks of linkage mechanisms, viz. joints and links. Thereafter, we present a novel algorithm, which performs topological analysis on the set of detected objects to create a kinematic model of the sketched mechanisms. The results show that this algorithm performs well on hand-drawn sketches and could help with conversions of such sketches to their digital representation for effective communication, analysis, cataloging, and classification.
Towards Creating Robust Walking Multi-Legged Robots for Traversing Different Terrains
Authors: Robson Nina , Audrey Vanessa 
Published: November 2022

Abstract
This paper explores the kinematic synthesis, design and pilot experimental testing of a six-legged walking robotic platform able to traverse through different terrains. The design approach builds up incrementally starting with studying the basic human leg walking trajectory and then defining a “relaxed” kinematic task. The “relaxed” kinematic task consists only of two contact locations (toe-off and heel-strike) with higher order motion task specifications compatible with foot-terrain(s) contact and curvature constraints in the vicinity of the two contacts. As a next step, an eight-bar leg image is created based on the “relaxed” kinematic task and incorporated within a six-legged walking robot. Pilot experimental tests explore if the proposed approach results in an adaptable behavior which allows the platform to incorporate different walking foot trajectories and gait styles coupled to each environment. The results suggest that the proposed “relaxed” higher order motion task combined with the leg morphological properties and feet material allowed the platform to walk stably on the different terrains.
Adaptive Slicing Based on Accurately Assessing the Variations of the Model’s Geometry for Staircase Effect and Dimensional Deviation Mitigation
Authors: Yang Qinkai , Myant Connor 
Published: November 2022

Abstract
Current additive manufacturing (AM) technologies commonly cause geometric inaccuracies (e.g. staircase effect and dimensional deviation) in the printed parts. These geometric inaccuracies significantly limit the industry potential of AM as they are the primary cause of reduced energy efficiency (e.g. turbine blade and wind tunnel testing models) and failed implants (e.g. hip and cranial implants and dental prostheses).
To improve the geometric accuracy while balancing the build time in AM, previous studies have proposed adaptive slicing. It works by varying decreasing layer thickness in sections of high curvature. However, current adaptive slicing methodologies have all faced difficulties adjusting layer thickness precisely according to the variations in the model’s geometry, limiting the geometric accuracy improvement.
This paper tackles this difficulty by assessing the geometric variations of the model by directly evaluating the ratio of the volume of each sliced layer’s geometric deviation to the volume of its corresponding region in the digital (triangulated surface) model. This assessment allows all the topological information of the corresponding region to be considered in measuring the geometric deviation (volume) between each sliced layer and its corresponding region, and therefore, it can accurately indicate the geometric variations of the model. Through this precise indication to modify each layer thickness, this paper has developed an adaptive slicing methodology that can better optimise the trade-off between build-time reduction and geometric accuracy improvement than other slicing methodologies. For example, it can reduce the build time by nearly half compared to other existing slicing methodologies by assuming a similar degree of the geometric accuracy of printed parts. This slicing was evaluated using six different test models and compared with three current slicing methodologies (voxelisation-based, cusp height-based, and uniform slicing).
A Material Removal State Prediction Method Based on Multi-Scale Attention Mechanism
Authors: Li Zhihang , Tang Qian , Liu Ying , Li Xiao 
Published: November 2022

Abstract
The exact removal of material in abrasive belt grinding determines the final machining quality of the workpiece. However, it is difficult to determine the removal state of materials in actual processing, which is affected by factors such as abrasive belt wear and processing errors. Therefore, a multi-scale attention convolutional neural network for material removal state prediction method is proposed based on the analysis of displacement data. First, the first-order difference and sliding-window expansion methods for displacement data are adopted, making it possible to use displacement data for deep learning, which is the premise of material removal state prediction. Then, the multi-scale convolutional neural network is Employed to extract important features of the displacement data. Due to the different importance of different features, Squeeze-and-Excitation Networks are used to independently assign the importance of features based on the loss function, so that the model pays more attention to those main features and ignores the secondary features, which can improve the convergence speed and prediction accuracy of the model. The K6 cross-validation of experiment results shows that this method can accurately predict the material removal state with an average prediction accuracy of 87.9%, which can be practically applied to the online prediction of the material removal state in industrial processing to further control the processing quality.
Designing Privacy Risk Frameworks for Evolving Cyber-Physical Social Systems: Knowledge Gaps Illuminated by the Case of Autonomous Vehicles and Bystander Privacy
Authors: Rao Vivek , Joshi Ankita , Kang Soo Min , Lin Susan , Song (Erin) Junghyun , Miller Drew , Goucher-Lambert Kosa , Agogino Alice 
Published: November 2022

Abstract
Designers and engineers increasingly engage with and must design for sociotechnical systems, also described as cyber-physical-social systems (CPSS). Leading frameworks like System-Theoretic Process Analysis and Value-Sensitive Design intend to help designers consider the consequences and impacts of their work with CPSS. However, such frameworks may not sufficiently account for human-centered scenarios. This complicates designers’ efforts to balance user needs with traditional forms of risk assessment. In this work, we explore foundations for the design of human-centered risk frameworks and examine a case study of autonomous vehicles and bystanders’ privacy as an example CPSS to address this gap. We develop an exploratory scenario-based risk framework and conduct expert interviews with experienced professionals (N = 7) working in the fields of autonomous vehicle design, development, policy and security to understand their perspectives on risk assessment and gather feedback on our framework. Reconciling interview findings with existing knowledge of evolving CPSS, we identify three broad knowledge gaps that could motivate future research in this space. First, we argue that there is a knowledge gap in developing human-centered frameworks and best practices to consider all stakeholders during the design of evolving CPSS. Second, we argue that a knowledge gap exists in acknowledging, reconciling, and proactively managing disciplinary discontinuities in vocabularies and mental models in evolving CPSS. Lastly, we argue that a critical knowledge gap exists around how to adapt scenario-based frameworks to accommodate the shifting challenges of designing evolving CPSS. We conclude with a discussion of preliminary implications for designing human-centered frameworks for autonomous vehicles and CPSS more generally.
Elasto-Plasticity, Damage and Multiphysics Effects on the Behavior of Adhesive Step Lap Joints
Authors: Michopoulos John G. , Apetre Nicole A. , Iliopoulos Athanasios P. , Steuben John C. 
Published: November 2022

Abstract
The presence of damage in the adhesive material as well as combined environmental excitation in multi-material adhesive step-lap joints (ASLJ) often encountered in aircraft industries are frequently neglected. Historically, their design is based only within the scope of elasto-plastic failure. The present work describes the implementation and application of a computational framework enabling the performance evaluation of such joints under quasi-static loading conditions under the simultaneous presence of plasticity, damage and environmental stimulus. In particular, a ASLJ involving Ti-6Al-4V alloy adherents with a FM-300K adhesive is modeled under the proposed framework for various material responses and mutliphysics excitations. It is shown that the assumption of assuming elasto-plastic failure as being the only behavior defining the failure of the adhesive, may not be an adequate assumption for designing and qualifying ASLJs. Specifically, considering presence of plasticity, damage and environmental effects indicates that there are reasons to re-examine the design practices of such joints.
Bi-Objective Surrogate Feasibility Robust Design Optimization Utilizing Expected Non-Dominated Improvement With Relaxation
Authors: Kania Randall J. , Azarm Shapour 
Published: November 2022

Abstract
Engineering design optimization problems often have two competing objectives as well as uncertainty. For these problems, quite often there is interest in obtaining feasibly robust optimum solutions. Feasibly robust here refers to solutions that are feasible under all uncertain conditions. In general, obtaining bi-objective feasibly robust solutions can be computationally expensive, even more so when the functions to evaluate are themselves computationally intensive. Although surrogates have begun to be utilized to decrease the computational costs of such problems, there is limited usage of Bayesian frameworks on problems of multiobjective optimization under interval uncertainty. This article seeks to formulate an approach for the solution of these problems via the expected improvement Bayesian acquisition function. The acquisition function is solved alternatingly with worst-case searches to find robust solutions. In this paper, a method is developed for iteratively relaxing the solutions to facilitate convergence to a set of non-dominated, robust optimal solutions. Additionally, a variation of the bi-objective expected improvement criterion is proposed to encourage variety and density of the robust bi-objective Pareto optimal (or non-dominated) solutions. Several examples are tested and compared against another bi-objective robust optimization approach with surrogate utilization. It is shown that the proposed method performs well at finding robustly optimized feasible solutions with limited function evaluations. Future directions for improving the proposed methodology are suggested.
Localized Dielectric Sintering With Magnetron for Microwave Material Processing
Authors: Graber Benjamin D. , Iliopoulos Athanasios P. , Michopoulos John G. , Steuben John C. , Birnbaum Andrew J. , Gorzkowski Edward P. , Patterson Eric A. , Fischer Richard P. , Petrov George M. , Johnson Luke A. 
Published: November 2022

Abstract
The process of sintering occurs when enough heating energy is applied on the particles of precursor powders to coalesce together and form a solid material without melting. Solidification takes place through cross mass diffusion along common interfaces and this technique has been used extensively by the materials processing community for ceramic part manufacturing. However, in most cases, furnaces are being used to elevate the temperature of material powder precursors globally throughout the entire volume of the intended parts. Instead of this approach, the present work explores the feasibility of using localized heating induced by coherent microwave radiation. Microwave-based material processing involves coupling between thermal and electromagnetic physics where the microwave radiation heats the sample locally via volumetrically tailored heat fluxes. However, changes in temperature change the dielectric properties of the sample, which then in turn affect microwave propagation. The nonlinearity introduced by the temperature dependence of the material properties into the relevant partial differential equations of this coupled system is further complicated by poorly defined dielectric, thermal, and thermo-electric properties of the dielectric precursor powders at temperatures required for sintering. This work focuses on analyzing a TE106 2.45 GHz microwave cavity used for processing BaTiO3, or BTO, precursor powder. Both a physical and a virtual experiment were carried out in tandem to understand the microwave propagation and dielectric property evolution with respect to temperature. It was demonstrated that appropriate tuning of the material properties (i.e., density, specific heat, heat conductivity, dielectric permittivity and loss tangent) relative to temperature enabled localized heating predicted by our model to match that of the physical experiment.
Design of Low-Cost Mini Controller Area Network
Authors: Makhlouf Abdalla , Ragab Elsayed , Abdelwahab Bassam , Qotb Ahmed , Alaaeldin Mohamed , Mousa Aiman , Yacoub Mostafa 
Published: November 2022

Abstract
Recently, the development of automotive embedded networks with different communication protocols has drawn the attention of many researchers. Especially, the development of separate onboard add-on systems that communicate with the vehicle network for the aim of enhancing comfort, safety or performance. The current article presents a simplified method for the design of a mini Controller Area Network (CAN) that accomplishes a sample task. The simple network uses the commonly used standard CAN protocol although it’s easily reconfigurable to deal with the extended CAN protocol. The network functionality was chosen to be as simple as transferring data between three CAN nodes, each holding a signal containing a switch status. After stating the network system requirements, the network topology and components list were identified. The circuit design of each node as well as the network design are presented and simulated. The network is built and tested experimentally. The proposed design method enables the use of low cost components that are available in the market. The nodes control programs are reconfigurable to match the target research vehicle network configurations. The proposed design method provides a handy tool for researchers that require utilizing a CAN protocol communication circuit that gathers information from any vehicle platform.
Hey, AI! Can You See What I See? Multimodal Transfer Learning-Based Design Metrics Prediction for Sketches With Text Descriptions
Authors: Song Binyang , Miller Scarlett , Ahmed Faez 
Published: November 2022

Abstract
Measuring design creativity is an indispensable component of innovation in engineering design. Properly assessing the creativity of a design requires a rigorous evaluation of the outputs. Traditional methods to evaluate designs are slow, expensive, and difficult to scale because they rely on human expert input. An alternative approach is to use computational methods to evaluate designs. However, most existing methods have limited utility because they are constrained to unimodal design representations (e.g., texts or sketches) and small datasets. To overcome these limitations, we propose a multimodal transfer learning-based machine learning model to predict five design metrics: drawing quality, uniqueness, elegance, usefulness, and creativity. The proposed model utilizes knowledge from large external datasets through transfer learning and simultaneously processes text and sketch data from early-phase concepts through multi-modal learning. Through six unimodal models using only texts or sketches, we show that transfer learning improves the predictive validity of text learning and sketch learning by 2%–18% and 9%–24%, respectively, for design metric evaluation. By comparing our multimodal model with the best unimodal models, we demonstrate that joining unimodal text and sketch learning models further increases the predictive validity of the approach by 4%–10%. The proposed models are generalizable to many application contexts beyond design concepts. Our findings highlight the importance of analyzing designs from multiple perspectives for design assessment. Finally, we discuss the challenges and opportunities in developing AI models for design metric evaluation.
Design, Kinematics, and Control of a Modular Robotic Suit as an Exercise System to Prevent Muscle Atrophy of Astronauts During Long-Duration Space Missions
Authors: Chae Jay S. , Nelson Carl A. , Baca José , Mukherjee Mukul 
Published: November 2022

Abstract
Muscle atrophy is a significant health issue for astronauts during long-duration space missions. Existing exercise equipment designed for use on spacecraft are an important part of mitigation strategies, but have some limitations. This paper presents a simple and compact modular robotic suit to overcome some of these limitations. Design rationale, kinematic modeling of the robot, control strategies for exercise, and prototyping of the modular robotic suit (MORS) are described.
Control Design and Simulation Framework for an Autonomous Paramotor UAV
Authors: Redkar Sangram , Fiedler Brett 
Published: November 2022

Abstract
This research develops a dynamics and control framework for an unmanned powered parachute system. The 6 DOF dynamics are derived from the first principle/fundamentals. The dynamic equations are simulated and validated within the MATLAB Simulink simulation environment. A Model Reference Adaptive Controller algorithm is utilized and simulated to update coefficients to control the plant more accurately. A Computational Fluid Dynamics (CFD) simulation using SimScale is used to estimate aerodynamic coefficients. This paper is presented as a building block/work in progress for future work on robust and efficient adaptive control of powered parachute aerial vehicles. Derivation of the dynamic equations is included in section two, while section three covers methods used to computationally define key aerodynamic coefficients and describe PPC simulation within the MATLAB Simulink environment. Finally, section four presents the results. Further research into this framework will include validating results through experimentation of the PPC aircraft and comparing dynamic model accuracy. Finally, more advanced control techniques will be applied to the PPC model in simulations and experiments.