bluuebunny/crossref_metadata_2025_split huggingface dataset:
In-Situ Observation Selection for Quality Management in Metal Additive Manufacturing
Authors: Roh Byeong-Min , Kumara Soundar R. T. , Yang Hui , Simpson Timothy W. , Witherell Paul , Lu Yan 
Published: November 2021

Abstract
Metal additive manufacturing (MAM) provides a larger design space with accompanying manufacturability than traditional manufacturing. Recently, much research has focused on simulating the MAM process with regards to part geometry, porosity, and microstructure properties. Despite continued advances, MAM processes have many variables that are not well understood with respect to their effect on the part quality. With the common use of in-situ sensors — such as CMOS cameras and infrared cameras — numerous, real-time datasets can be captured and analyzed for monitoring both the process and the part.
However, currently, real-time data predominantly focuses on the build failure and process anomalies by capturing the printing defects (cracks/peel-off). A large amount of data — such as melt pool geometries and temperature gradients — are just beginning to be explored, along with their connections to final part quality. Towards investigating these connections, in this paper we propose models that capture numerous sensor capabilities and associate them with the corresponding, real-time, physical phenomena. These sensor models lay the foundation for a comprehensive, knowledge framework that forms the basis for quality monitoring and management of MAM process outcomes.
Using our previously developed process ontology model [1–3], which describes the relationship between process variables and process outcomes, we can discover the relationship between the real-time, physical phenomena and the deviations in the targeted, build quality. For example, statistically significant sensor data that predicts deviations from targeted process qualities can be detected and used to control the process parameters. Case studies that scope the physical phenomena and sensor data are provided for verifying the effectiveness and efficiency of the proposed qualification and certification models.
A Novel Micro-Positioning Stage With Large-Stroke and Adjustable Stiffness
Authors: Yang Zhijun , Cai Bingyu , Li Ruiqi , Peng Hao , Bai Youdun 
Published: November 2021

Abstract
The existing micro-motion stage based on flexure hinge can hardly meet the requirements of a high-precision stage with large stroke and variable operating conditions (especially variable frequency operation). In this paper, a flexible hinge micro-motion stage with adjustable stiffness is presented. A wide range of stiffness and frequency adjustments are realized by changing the length of the flexure hinge through the movement of the support. However, the change on the stiffness of the flexure hinge is non-linear when is in large deformation. It is difficult to use the traditional PID algorithm to control such nonlinear system without the complete mathematical model, and much more complicated control strategies are required to deal with the condition of large deformation of the flexure hinge. In this paper, the active disturbance rejection control (ADRC) strategy is adopted to solve the above non-linear control problem without relying on the complete system model. A novel model-compensated ADRC based on the dynamic characteristics is proposed to further improve the performance of the micro-motion stage. Experiments show that the ADRC with model compensation (MADRC) can achieve high positioning and tracking precision faster than the conventional ADRC. The presented micro-motion stage based on stiffness-adjustable flexure hinges and MADRC design is capable to meet the industrial application requirements of large stroke or variable working conditions.
An Radio Frequency Impedance Matching Control Benchmark System for Advanced Control Strategies Evaluation
Authors: Rodríguez Carlos A. , Viola Jairo , Chen YangQuan 
Published: November 2021

Abstract
An impedance matching network is mandatory between the source and the load to obtain the maximum power transfer in a system. For example, in the plasma processing technology widely used in semiconductor wafer processing, guaranteeing the maximum transference of power is the main goal. This paper presents the problem definition and guidelines for a radio-frequency (RF) impedance matching benchmark. This benchmark system we developed in public domain is useful in studying and evaluating various impedance patching algorithms fairly. The goal is to match the impedance of the source with its complex conjugate of the load in a minimum time. The reflection coefficient is used as an evaluation metric to measure the reflected power, which has to be zero to guarantee the maximum power transfer. Some impedance matching controllers proposed in the literature are tested as sample applications on this benchmark in a fair manner to check and evaluate its robustness and stability performance against different initial conditions, load conditions and perturbations on the impedance matching network control system. We also suggested a novel Feedforward Control (FF) profile generation method with a primitive sample feedback control strategy (proportional-integral (PI) controller) as a baseline reference for the benchmark.
Analytical Study of Dry Whip Phenomena During Rotor-Stator Rub
Authors: Srivastava Aman K. , Kumar Anurag , Tiwari Mayank , Singh Akhilendra 
Published: November 2021

Abstract
Rotor-Stator Rub is a rare but catastrophic phenomenon and in most cases leads to failure of Gas Turbine Engines. Asynchronous rub namely Partial Rub and Dry whip are two of the most common observed rub related phenomena. Dry whip leads to pure backward whirl and instability. This paper studies the dry whirl analytically to determine the boundaries of instability and establish expressions for nonlinear vibrations in case of dry whip. First, Nonlinear Natural Frequency has been defined for a rotor-stator rub system and expression for natural motion of the system has been formulated. Next, Pure rolling of rotor on stator has been used as a condition to approximate the nonlinear system to find out the dependence of dry whip frequency on the parameters such as stiffness, damping ratio, coefficient of friction and spin speed of the rotor. Moreover, a validation of the obtained frequencies and amplitudes obtained analytically has been performed through the simulation of rotor stator rub system using RK-4 integration technique. Furthermore, this study offers insight into the frequencies that are present in the Radial motion of the rotor and its source of origin. The radial displacement of the rotor has harmonics which are the result of interaction between the rotor speed and the backward whirl frequency causing dry whip.
Application of Prognostics and Health Management (PHM) to Software System Fault and Remaining Useful Life (RUL) Prediction
Authors: Islam Mohammad Rubyet , Sandborn Peter 
Published: November 2021

Abstract
Prognostics and Health Management (PHM) is an engineering discipline focused on predicting the point at which systems or components will no longer perform as intended. The prediction is often articulated as a Remaining Useful Life (RUL). RUL is an important decision-making tool for contingency mitigation, i.e., the prediction of an RUL (and its associated confidence) enables decisions to be made about how and when to maintain the system. PHM is generally applied to hardware systems in the electronics and non-electronics application domains. The application of PHM (and RUL) concepts has not been explored for application to software.
Today, software (SW) health management is confined to diagnostic assessments that identify problems, whereas prognostic assessment potentially indicates when in the future a problem will become detrimental to the operation of the system. Relevant areas such as SW defect prediction, SW reliability prediction, predictive maintenance of SW, SW degradation, and SW performance prediction, exist, but all represent static models, built upon historical data — none of which can calculate an RUL.
This paper addresses the application of PHM concepts to software systems for fault predictions and RUL estimation. Specifically, we wish to address how PHM can be used to make decisions for SW systems such as version update, module changes, rejuvenation, maintenance scheduling and abandonment.
This paper presents a method to prognostically and continuously predict the RUL of a SW system based on usage parameters (e.g., numbers and categories of releases) and multiple performance parameters (e.g., response time). The model is validated based on actual data (on performance parameters), generated by the test beds versus predicted data, generated by a predictive model. Statistical validation (regression validation) has been carried out as well. The test beds replicate and validate faults, collected from a real application, in a controlled and standard test (staging) environment. A case study based on publicly available data on faults and enhancement requests for the open-source Bugzilla application is presented. This case study demonstrates that PHM concepts can be applied to SW systems and RUL can be calculated to make decisions on software version update or upgrade, module changes, rejuvenation, maintenance schedule and total abandonment.
Optimal Control of a 5-Link Biped Using Quadratic Polynomial Model of Two-Point Boundary Value Problem
Authors: Hernandez-Hinojosa Ernesto , Satici Aykut , Bhounsule Pranav A. 
Published: November 2021

Abstract
To walk over constrained environments, bipedal robots must meet concise control objectives of speed and foot placement. The decisions made at the current step need to factor in their effects over a time horizon. Such step-to-step control is formulated as a two-point boundary value problem (2-BVP). As the dimensionality of the biped increases, it becomes increasingly difficult to solve this 2-BVP in real-time. The common method to use a simple linearized model for real-time planning followed by mapping on the high dimensional model cannot capture the nonlinearities and leads to potentially poor performance for fast walking speeds. In this paper, we present a framework for real-time control based on using partial feedback linearization (PFL) for model reduction, followed by a data-driven approach to find a quadratic polynomial model for the 2-BVP. This simple step-to-step model along with constraints is then used to formulate and solve a quadratically constrained quadratic program to generate real-time control commands. We demonstrate the efficacy of the approach in simulation on a 5-link biped following a reference velocity profile and on a terrain with ditches. A video is here: https://youtu.be/-UL-wkv4XF8.
Developing a Supply Chain Modeling Approach to Facilitate Ecology-Inspired Design for Sustainability and Resilience
Authors: Wilson Tyler , Chatterjee Abheek , Layton Astrid 
Published: November 2021

Abstract
Supply chain policies and design efforts are traditionally focused on efficiency objectives such as reducing operational costs. With the occurrence of the most devastating pandemic in decades and the continually increasing prevalence of natural disasters, this focus has been challenged, and the need to focus on supply chain resilience has become apparent. Achieving long-lasting sustainable development in supply chains requires a balance of efficiency-focused measures that enhance economic and environmental sustainability and resiliency measures. Ecological Network Analysis has revealed a unique balance between pathway efficiency and redundancy in ecosystems’ network architecture. This enables both efficient operations under normal circumstances and resilience to perturbations. This same analysis can be used to evaluate the balance of sustainability and resilience in supply chain networks, providing insights into what kind of supply chain design and policy decisions lead to more ecosystem-like architectures. This study lays the groundwork for such efforts by studying four supply chain topologies (formed by prevalent supply chain strategies) using ENA. Inventory (storage) is not well understood in the typical flow analysis used in ENA but is an essential facet of supply chain design and must be included in a supply chain analysis. This study overcomes this limitation by proposing a method to include inventory in the ENA framework. The analysis conducted revealed two significant insights: (a) the agile supply chain strategy is the most ecologically similar and (b) it is possible that there are optimal inventory levels (given partnership strategies) to utilize bio-inspiration in supply chain design.
Robust Design of Coupled Engineered Systems
Authors: Sharma Gehendra , Allen Janet K. , Mistree Farrokh 
Published: November 2021

Abstract
Coupled Engineered Systems can be characterized by the inherent interactions among design decisions. These interactions define the influence that one design decision exerts over another and require appropriate method to model such interactions. Robust design enables designers to design a product or process that is relatively insensitive to uncertainties. Hence, robust design of coupled engineered systems enables designers to, (i) design engineered systems while accounting for interaction among design decisions and (ii) identifying design decisions that are relatively insensitive to uncertainties.
In this paper, an appropriate method to model interactions and identify robust solution is presented. The interacting decisions are categorized into concurrent and hierarchical decisions and are respectively modeled with horizontal and vertical coupling. Based on the strength of interaction between these decisions, two types of interactions are defined, weak and strong interactions. To enable robust decisions in a coupled engineered systems, robustness metrics are defined and included as goals/constraints. The metrics considered in this work are to explore the solution space and manage uncertainty by considering the design of robust systems. The method has been tested on three design examples, that are, (i) design of a fender, (ii) design of a gearbox and (iii) design of a composite structure.
Learning of a Basketball Free Throw With a Flexible Link Robot
Authors: Timke Jannik , Morlock Merlin , Duecker Daniel A. , Seifried Robert 
Published: November 2021

Abstract
Object throwing is an efficient approach for overcoming the kinematic workspace limitations of robots in placement scenarios. Throwing of objects with rigid link robots has been widely studied in literature. Although using robots with spring-like flexible links can significantly increase the throwing distance, existing contributions are very rare. Therefore, we propose an efficient iterative learning control throwing algorithm and apply it to a flexible link robot. A simple rigid link throwing model is used to generate the motor motion. Errors caused by this simplification are corrected by a flexible link throwing model based on the finite element method. As representative scenario a basketball free throw is selected which requires high throwing accuracy. Here, we demonstrate that the controller can be efficiently pre-learned in simulations to reduce real-world training time. Experiments then validate that our learning control method achieves the required free throw accuracy within very few real-world learning iterations.
Design Human-Robot Collaborative Lifting Task Using Optimization
Authors: Arefeen Asif , Xiang Yujiang 
Published: November 2021

Abstract
In this paper, an optimization-based dynamic modeling method is used for human-robot lifting motion prediction. The three-dimensional (3D) human arm model has 13 degrees of freedom (DOFs) and the 3D robotic arm (Sawyer robotic arm) has 10 DOFs. The human arm and robotic arm are built in Denavit-Hartenberg (DH) representation. In addition, the 3D box is modeled as a floating-base rigid body with 6 global DOFs. The interactions between human arm and box, and robot and box are modeled as a set of grasping forces which are treated as unknowns (design variables) in the optimization formulation. The inverse dynamic optimization is used to simulate the lifting motion where the summation of joint torque squares of human arm is minimized subjected to physical and task constraints. The design variables are control points of cubic B-splines of joint angle profiles of the human arm, robotic arm, and box, and the box grasping forces at each time point. A numerical example is simulated for huma-robot lifting with a 10 Kg box. The human and robotic arms’ joint angle, joint torque, and grasping force profiles are reported. These optimal outputs can be used as references to control the human-robot collaborative lifting task.
Multi-Modal Search for Inspirational Examples in Design
Authors: Kwon Elisa , Huang Forrest , Goucher-Lambert Kosa 
Published: November 2021

Abstract
Inspirational stimuli are known to be effective in supporting ideation during the design process. However, minimal prior work has allowed individuals to search using multiple modes of input simultaneously, which is more representative of real design behavior. In the current work, we developed a multi-modal search platform that retrieves 3D model parts based on text, appearance, and function-based search inputs. This work presents the results of an experimental study (n = 21) in which the search platform was used to find parts identified as potentially useful for inspiring solutions to a design challenge. Participants were asked to engage with three different search modalities: search by keywords, by curated 3D parts, and by user-assembled 3D parts in their workspace. When searching by parts that are curated or in their workspace, additional control over the similarity of appearance and function of results in reference to the input was available to participants. The results of this study demonstrate that the modality used affects search behavior, such as in the frequency of searches, how participants engage with retrieved search results, and how broadly the search space is covered. Specific results link interactions with the interface to search strategies participants may have used during the task. Findings suggest that multi-modal search should enable intentional search for desired goals through direct search inputs (e.g., by keyword) and incremental adjustments to features of visually represented search inputs. Moreover, enabling discovery of inexplicitly searched for examples through related information or more randomly encountered examples may assist exploratory search behavior.
Open Research Questions for Incorporating Multi-Stakeholder Interests in Engineering for Global Development
Authors: Stevenson Phillip D. , Wood Amy E. , Mattson Christopher A. , Salmon John L. 
Published: November 2021

Abstract
There is potential for multi-stakeholder social impact design approaches to help advance design and modeling processes in engineering for global development. Adding multiple stakeholders, however, raises questions about how to choose stakeholders, weight their preferences, and ultimately model the perceived or actual impacts of products on stakeholders. While methods for handling the interests of multiple stakeholders exist in the literature, they are not without problems that are often exacerbated when modeling social impact in an engineering for global development setting. The purpose of this paper is to articulate some of the barriers and open research questions for handling multiple stakeholder interests during the process of designing engineering for global development products. Six challenges and sixteen research questions are presented.
Two-Scale Topology Optimization With Parameterized Cellular Structures
Authors: Rastegarzadeh Sina , Wang Jun , Huang Jida 
Published: November 2021

Abstract
Advances in additive manufacturing enable the fabrication of complex structures with intricate geometric details. It also escalates the potential for high-resolution structure design. However, the increasingly finer design brings computational challenges for structural optimization approaches such as topology optimization (TO) since the number of variables to optimize increases with the resolutions. To address this issue, two-scale TO paves an avenue for high-resolution structural design. The design domain is first discretized to a coarse scale, and the material property distribution is optimized, then using micro-structures to fill each property field. In this paper, instead of finding optimal properties of two scales separately, we reformulate the two-scale TO problem and optimize the design variables concurrently in both scales. By introducing parameterized periodic cellular structures, the minimal surface level-parameter is defined as the material design parameter and is implemented directly in the optimization problem. A numerical homogenization method is employed to calculate the elasticity tensor of the cellular materials. The stiffness matrices of the cellular structures derived as a function of the level parameters, using the homogenization results. An additional constraint on the level parameter is introduced in the structural optimization framework to enhance adjacent cellulars interfaces’ compatibility. Based on the parameterized micro-structure, the optimization problem is solved concurrently with an iterative solver. The reliability of the proposed approach has been validated with different engineering design cases. Numerical results show a noticeable increase in structure stiffness using the level parameter directly in the optimization problem than the state-of-art mapping technique.
Deep Learning-Based Surrogate Modeling via Physics-Informed Artificial Image (PIAI) for Strongly Coupled Multidisciplinary Engineering Systems
Authors: Hwang Sungkun , Choi Seung-Kyum 
Published: November 2021

Abstract
The design of strongly coupled multidisciplinary engineering systems is challenging since it is characterized by the complex interaction of different disciplines. Such complexity cannot be easily captured by explicit analytical solutions, which motivates the development of surrogate modeling. It enables the prediction of the systems’ behavior without analytical formulations. Among existing surrogate modeling techniques, deep learning has gained significant interest because of the flexibility of non-linear formulation and applicability to data-driven analysis. Notably, the convolution neural networks-based deep surrogate model augments the precision of prediction and estimation of system behavior once image-based inputs representing physical experiments and simulation are employed.
Nevertheless, the feasibility of the deep surrogate model is often flawed due to the miserable correlation representation between design parameters and the corresponding responses. Massive training costs also degrade the performance of the predictive model. To address those issues, this research proposes a physics-informed artificial image (PiAI) that incubates geometry-informed CAD, location-clarified filter, and essential simulation conditions, which augments the prediction credibility. Moreover, in lieu of employing multimodalities or multiple image channels, the proposed method employs a unimodal-based single image input to increase computational efficiency. The proposed framework’s efficacy and applicability are addressed in practical engineering design applications: cantilever beam and stretchable strain sensor.