bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Predictive Approach to Geometry Preparation for AR/VR Applications
Authors: Dammann Maximilian Peter , Steger Wolfgang , Paetzold Kristin 
Published: November 2022

Abstract
AR/VR applications are a valuable tool in product development and the overall product lifecycle in engineering. However, data transformation of the models from CAD systems to the AR/VR applications is labor-intensive and requires expertise. The main task in the data transformation is the tessellation of the product geometry. Depending on the product complexity and the performance of the target platform extensive optimization is needed to ensure the usability and visual quality of the AR/VR application.
Current approaches to this problem use iterative and inflexible processes mostly based on tessellation and on mesh decimation that ignore the varying importance of different geometric aspects for an AR/VR application. An alternative respectively more targeted approach is proposed, that aims at predicting tessellation results and moving the optimization process before the actual tessellation. As a result, the need for iterative operations on the polygon meshes can be reduced or ideally avoided altogether.
The paper presents some results of an investigation of the hypothesis that geometric complexity metrics can be used to control and enhance the choice of tessellation parameters. Several characteristics and metrics are identified and gathered from literature and subsequently evaluated with regard to the polygon count and visual quality in the geometry preparation process. Based on the evaluation, prediction models are created and implemented in a geometry preparation tool. The performance is evaluated and discussed.
The Impact of a Strategy of Deception About the Identity of an Artificial Intelligence Teammate on Human Designers
Authors: Zhang Guanglu , Raina Ayush , Brownell Ethan , Cagan Jonathan 
Published: November 2022

Abstract
Advances in artificial intelligence (AI) offer new opportunities for human-AI collaboration in engineering design. Human trust in AI is a crucial factor in ensuring an effective human-AI collaboration, and several approaches to enhance human trust in AI have been suggested in prior studies. However, it remains an open question in engineering design whether a strategy of deception about the identity of an AI teammate can effectively calibrate human trust in AI and improve human-AI joint performance. This research assesses the impact of the strategy of deception on human designers through a human subjects study where half of participants are told that they work with an AI teammate (i.e., without deception), and the other half of participants are told that they work with another human participant but in fact they work with an AI teammate (i.e., with deception). The results demonstrate that, for this study, the strategy of deception improves high proficiency human designers’ perceived competency of their teammate. However, the strategy of deception does not raise the average number of team collaborations and does not improve the average performance of high proficiency human designers. For low proficiency human designers, the strategy of deception does not change their perceived competency and helpfulness of their teammate, and further reduces the average number of team collaborations while hurting their average performance at the beginning of the study. The potential reasons behind these results are discussed with an argument against using the strategy of deception in engineering design.
Improving Designer Learning in Design Space Exploration by Adapting to the Designer’s Learning Goals
Authors: Virós-i-Martin Antoni , Selva Daniel 
Published: November 2022

Abstract
Design space exploration is a design method by which the designer tries to learn important information about a design problem (e.g., main design trade-offs, sensitivities, common features among good designs) to help them make better design decisions. This paper presents preliminary results of a study characterizing the effects on a designer’s learning of an AI assistant that adapts to the designer’s goals during design space exploration. Specifically, we compare the designer’s learning when the AI assistant adapts to explicit learning goals shared by the designer versus when it does not adapt. The AI assistant used for the study is Daphne, which helps engineers design Earth observation satellite systems. The designer’s learning process is modeled as an iterative hypothesis generation and testing process. First, the designer shares with Daphne a certain learning goal in the form of a hypothesis (e.g., designs with feature F are more likely to be on the Pareto front). Then, Daphne adapts to this goal by searching for more designs that have the feature being tested and showing the user the extent to which the data supports their hypothesis. The participants in the preliminary study are N = 10 students from Texas A&M University. We ask each participant to design earth observation satellite constellations to meet a set of requirements while trying to learn about the design problem. The results show that participants with the adaptive AI assistant consistently score higher on their learning about the design task compared to the baseline design assistant as measured by a post-task test. A negative effect is observed on task performance with the adaptive AI assistant condition due to a smaller number of design creation actions, which is consistent with findings from previous studies. Recommendations are provided for the design of similar future AI assistants based on the results of this study. Finally, a power study is done to set a goal for statistical validity of the study.
Integrated Computational Materials Engineering With Monotonic Gaussian Processes
Authors: Tran Anh , Maupin Kathryn , Rodgers Theron 
Published: November 2022

Abstract
Physics-constrained machine learning is emerging as an important topic in the field of machine learning for physics. One of the most significant advantages of incorporating physics constraints into machine learning methods is that the resulting machine learning model requires significantly fewer data to train. By incorporating physical rules into the machine learning formulation itself, the predictions are expected to be physically plausible. Gaussian process (GP) is perhaps one of the most common methods in machine learning for small datasets. In this paper, we investigate the possibility of constraining a GP formulation with monotonicity on two different material datasets, where one experimental and one computational dataset is used. The monotonic GP is compared against the regular GP, where a significant reduction in the posterior variance is observed. The monotonic GP is strictly monotonic in the interpolation regime, but in the extrapolation regime, the monotonic effect starts fading away as one goes beyond the training dataset. Imposing monotonicity on the GP comes at a small accuracy cost, compared to the regular GP. The monotonic GP is perhaps most useful in applications where data is scarce and noisy or when the dimensionality is high, and monotonicity is where supported by strong physical reasoning.
Review of Transfer Learning in Additive Manufacturing Modeling
Authors: Tang Yifan , Dehaghani M. Rahmani , Wang G. Gary 
Published: November 2022

Abstract
The process-structure-property modeling of additive manufacturing (AM) products plays an important role in process and quality control. In practice however, only limited data are available for each product due to its expensive material and time-consuming fabricating process, which becomes an obstacle to achieve high quality models. Transfer learning (TL) is a new and promising approach that the model of one product (source) may be reused for another product (target) with limited new data on the target. This paper focuses on reviewing applications of TL in AM modeling in order to help further research in this area. To clarify the specific topic, the problem definition is presented, as well as the differences between TL, multi-fidelity modeling, and multi-task learning. Then current applications of TL in AM modeling are summarized according to different TL approaches. To better understand the performances of different TL approaches, several representative TL-assisted AM modeling methods are reproduced and tested on an open-source dataset. Based on the test results, their effectiveness and limitations are discussed in detail. Finally, future research directions about TL in AM modeling are discussed in hope to explore more potential of TL in boosting the AM model performance.
Optimization of Self-Heated Vacuum Membrane Distillation Using Response Surface Methodology
Authors: Rahmadhanty Shaneza Fatma , T. M. Subrahmanya , Hung Wei-Song , Lin Po Ting 
Published: November 2022

Abstract
Currently, technology is increasingly advanced and has penetrated into several fields such as Membrane Distillation (MD) in water treatment, especially to purify saline water, in the face of the increasing scarcity of clean water needed for consumption, both in the domestic, agriculture, and industrial sectors. One of the configurations in MD systems, self-heated Vacuum Membrane Distillation (VMD) system, which employs a Graphene-PVDF membrane heated by power supply, in this case using DC power with low voltage, to eliminate feed pre-feed heating and temperature polarization, is interesting to learn. In addition, to enhance the performances, such as optimizing permeate flux (JW), Temperature Polarization Factor (TPF), Specific Heating Energy (QSH), and Gain Output Ratio (GOR), several different designs are made and compared. In this study, four parameters are selected: DC power supply’s voltage, feed flow rate, the length, and the width of the cell-body’s slot. Ansys FLUENT software is utilized to simulate the system, followed by Minitab software to analyze the results using Response Surface Method (RSM) which aims to achieve the optimal design parameters. The simulation data was validated by experimental data and determination of the optimum conditions of the self-heated VMD system led to the best performances such as maximizing JW, TPF, and GOR, as well as minimizing QSH.
Decision Fusion Method for the Failure Form of Retired Parts Based on Cloud Model Optimization D-S Evidence Theory
Authors: Wang Lei , Xu Chengying , Zhang Zelin , Xia Xuhui 
Published: November 2022

Abstract
The decision-making fusion of multi-source failure information for retired parts is vital to classify the failure forms of retired parts. There are various failure information detection methods for retired parts, however the results obtained by different detection methods are biased, one-sided, and uncertain, resulting in difficulty in decision making. Aiming at improving the classification accuracy and reliability of the failure forms for retired parts, we propose a fusion method for retired parts failure form decision-making based on the cloud-model optimization D-S evidence theory (CM-D-S). The cloud model solves the problem of strong subjectivity in the traditional D-S evidence theory and overcomes the negative effects caused by random factors. Through image processing, magnetic memory, and ultrasonic testing, the multi-source failure information of retired parts can be obtained, and the failure forms of retired parts are divided into fracture, corrosion, wear and deformation. The cloud model parameters are used to characterize different failure feature quantities sequentially, obtain the basic probability assignment matrix in the D-S evidence theory, and substituted into the D-S evidence theory model to combine and optimize multi-source failure information, and output the result of decision fusion, which is the multi-source failure form of the retired parts. The case shows that the CM-D-S decision-making method has high accuracy for failure form recognition, which proves the applicability and feasibility of the method.
Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty
Authors: Chen Wei (Wayne) , Lee Doksoo , Balogun Oluwaseyi , Chen Wei 
Published: November 2022

Abstract
Deep generative models have demonstrated effectiveness in learning compact and expressive design representations that significantly improve geometric design optimization. However, these models do not consider the uncertainty introduced by manufacturing or fabrication. Past work that quantifies such uncertainty often makes simplifying assumptions on geometric variations, while the “real-world”, “free-form” uncertainty and its impact on design performance are difficult to quantify due to the high dimensionality. To address this issue, we propose a Generative Adversarial Network-based Design under Uncertainty Framework (GAN-DUF), which contains a deep generative model that simultaneously learns a compact representation of nominal (ideal) designs and the conditional distribution of fabricated designs given any nominal design. This opens up new possibilities of 1) building a universal uncertainty quantification model compatible with both shape and topological designs, 2) modeling free-form geometric uncertainties without the need to make any assumptions on the distribution of geometric variability, and 3) allowing fast prediction of uncertainties for new nominal designs. We can combine the proposed deep generative model with robust design optimization or reliability-based design optimization for design under uncertainty. We demonstrated the framework on two real-world engineering design examples and showed its capability of finding the solution that possesses better performances after fabrication.
The Need for Desalination in Humanitarian Crises
Authors: Bessette Jonathan T. , Winter Amos G. 
Published: November 2022

Abstract
Humanitarian crises ranging from political unrest to natural disasters are becoming increasingly prevalent with global climate change. Correspondingly, there are an increasing number of regions that consist both of high crises risk and saline water contamination. Such regions include the Middle East, Subsaharan Africa (particularly along the Great Rift Valley), Southeast Asia (including the Mekong Delta and Pacific Islands), and coastal regions. However, there is a lack of robust, deployable desalination technologies for humanitarian crises. This is mainly attributed to the highly-constrained environment which necessitate: minimization of consumables, rapid speed of deployment and simplification of operation and maintenance. Such constraints are often secondary thoughts, are difficult to traditionally quantify, and differ from stable commercial situations where operations are supported by an accessible supply chain and network of technicians. These barriers have particularly hindered the adoption of membrane technology and thus, high volume desalination and chemical contaminant removal. This work justifies the need for desalination technology in humanitarian crises via geospatial analysis of saline water databases and exploration of regional case studies, formulates design requirements for an emergency-use desalination system based on needs extracted from open-interviews of stakeholders and literature review, evaluates some of the gaps within currently employed deployable desalination systems and explores the potential opportunities of other desalination technology.
Human Grasping Force Prediction, Measurement, and Validation for Human-Robot Lifting
Authors: Arefeen Asif , Quarnstrom Joel , Syed Shahbaz P Qadri , Bai He , Xiang Yujiang 
Published: November 2022

Abstract
In this study, a 13 degrees of freedom (DOFs) three-dimensional (3D) human arm model and a 10 DOFs 3D robotic arm model are used to validate the grasping force for human-robot lifting motion prediction. The human arm and robotic arm are modeled in Denavit-Hartenberg (DH) representation. In addition, the 3D box is modeled as a floating-base rigid body with 6 global DOFs. The human-box and robot-box interactions are characterized as a collection of grasping forces. The joint torque squares of human arm and robot arm are minimized subjected to physics and task constraints. The design variables include (1) control points of cubic B-splines of joint angle profiles of the human arm, robotic arm, and box; and (2) the discretized grasping forces during lifting. Both numerical and experimental human-robot liftings were performed with a 2 kg box. The simulation reports the human arm’s joint angle profiles, joint torque profiles, and grasping force profiles. The comparisons of the joint angle profiles and grasping force profiles between experiment and simulation are presented. The simulated joint angle profiles have similar trends to the experimental data. It is concluded that human and robot share the load during lifting process, and the predicted human grasping force matches the measured experimental grasping force reasonably well.
Mobility Prediction of Off-Road Ground Vehicles Using a Dynamic Ensemble of NARX Models
Authors: Liu Yixuan , Barthlow Dakota , Mourelatos Zissimos P. , Gorsich David , Singh Amandeep , Zeng Jice , Hu Zhen 
Published: November 2022

Abstract
Mobility prediction of off-road Autonomous Ground Vehicles (AGV) in uncertain environments is essential for their model-based mission planning especially in the early design stage. While surrogate-modeling methods have been developed to overcome the computational challenge in simulation-based mobility prediction, it is very challenging for a single surrogate model to accurately capture the complicated vehicle dynamics. With a focus on vertical acceleration of an AGV under off-road conditions, this paper proposes a surrogate modeling approach for AGV mobility prediction using a dynamic ensemble of Nonlinear Autoregressive Network with Exogenous inputs (NARX) models over time. Synthetic vehicle mobility data of an AGV are first collected using a limited number of high-fidelity simulations. The data are then partitioned into different segments using a variational Gaussian mixture model in order to represent different vehicle dynamic behaviors. Based on the partitioned data, multiple surrogate models are constructed under the NARX framework with different numbers of lags. The NARX models are then assembled together dynamically over time to predict the mobility of the AGV under new conditions. A case study demonstrates the advantages of the proposed method over the classical NARX models for AGV mobility prediction.
Using Coaxial Melt Pool Monitoring Images to Estimate Cooling Rate for Powder Bed Fusion Additive Manufacturing
Authors: Yang Zhuo , Lane Brandon , Lu Yan , Yeung Ho , Kim Jaehyuk , Ndiaye Yande , Krishnamurty Sundar 
Published: November 2022

Abstract
Cooling rate is a decisive index to characterize melt pool solidification and determine local microstructure formation in metal powder bed fusion processes. Traditional methods to estimate the cooling rate include in-situ temperature measurement and thermal simulation. However, these methods may not be accurate or efficient enough under complex conditions in real-time. This paper proposes a method to approximate the melt pool cooling rate using temperature profile acquired via thermally-calibrated melt pool camera, and based on continuous pixel tracking result. The proposed method can estimate the temperature and associated cooling rate for every pixel immediately, which is potentially applicable for real-time process monitoring. This paper focuses on investigating image data processing, method development, and cooling condition analysis. This work presents the preliminary result of the cooling rate estimation under different conditions such as position, layer number, and overhanging.
Toward Formal Qualitative Reasoning to Support Functional Decomposition
Authors: Mao Xiaoyang , Sen Chiradeep 
Published: November 2022

Abstract
Functional decomposition is an important task in early systems engineering and design, where the overall function of the system is resolved into the functions of its components or subassemblies. Conventionally, this task is performed manually, because of multiple possible solution paths and the need for understanding the physics phenomena that could realize the desired effects. This paper presents an approach of developing a formal method for functional decomposition using physics-based qualitative reasoning. The representation includes three parts: (1) a natural language reasoner that detects the changes of physical states of material and energy flows, (2) a set of causation tables that abstract the knowledge of qualitative physics by capturing the causal relations between the various quantities involved in a physical phenomenon or process, and (3) a process-to-subgraph mapping that translate the physical processes into function structure constructs. The algorithm uses the above three representations and some topological reasoning to assemble function models that represent the decomposition of a given black box model. The paper illustrates the potential of this method for functional decomposition using an example of an air-heating device. The paper also discusses the limitations and challenges in maturing this approach into an end-usable design tool.
Concept Maps in Augmented Reality to Improve the Learning Process and the Retrieval of Information
Authors: Carulli Marina , Bordegoni Monica , Spadoni Elena , Rossoni Marco 
Published: November 2022

Abstract
Memorization techniques are of primary importance in education. Two relevant and extensively used techniques are the Concept maps and the method of loci. Both methods are based on the visualization of information, which helps memorization and retrieval. For these reasons, they are also considered inclusive learning tools for people with Specific Learning Disability. Augmented Reality is a technology that has gained popularity in many sectors, from industry to the medical one, for its effectiveness in visualizing graphical items on top of real contexts. The paper demonstrates that Augmented Reality can also be beneficial for representing and interacting with Concept maps in a 3D virtual space that is linked to the real world. Specifically, the authors developed an Augmented Reality application in which the key features of both the Concept maps (such as visual-spatial logic and concepts organization) and of the method of loci are integrated with those of Augmented Reality technologies to improve the learning process and the retrieval of information.
An Innovative Method for Lightweight Design Based on FBS and Conflict Resolution
Authors: Liu Shuang , Guo Xin , Yang Xiwen , Hu Huicong 
Published: November 2022

Abstract
With the rapid development of today’s economy, people’s requirements for products are more diverse, and lightweight requirements are typical of these. Lightweight products play an important role in today’s engineering practice, and many researchers are participating in lightweight research. Schemes generated by conceptual design are used to realize the function, but it is difficult to consider simultaneously the lightweight and the functions’ realization. Especially the structures of the products may conflict with the way of working when the lightweight is considered. Sometimes, choosing which structure to start lightweight design is an important issue for a product; so an innovative design model for lightweight products based on FBS and conflict resolution is offered by the authors which can also take into account the product function while reducing the weight from the structure. The model includes three parts: Firstly, multi requirement analysis and FBS is constructed for the initial scheme. Secondly, if the requirements are not met, the structure in the initial scheme is scored by AHP, and the appropriate structure is selected for preliminary lightweight. Thirdly, the lightweight structure is obtained through conflict resolution, and then the conflict between it and other structures in the initial scheme is solved, to generate a new general structure. This paper takes the vertical frame and brace of a gantry crane as an example to verify the effectiveness of the model.