bluuebunny/crossref_metadata_2025_split huggingface dataset:
Drive the Cobots Aright: Guidelines for Industrial Application of Cobots
Authors: Malik Ali Ahmad , Pandey Vijitashwa 
Published: November 2022

Abstract
Collaborative robots (cobots) are a new class of industrial robots with a safe, lightweight structure and easy to program interface. They have received attention from manufacturers seeking to automate some simple and repetitive tasks, while still retaining humans for the remaining ones, thereby taking advantage of their complementary abilities. From an implementation perspective, cobots need to accommodate and potentially benefit from direct human-robot interaction. They have demonstrated a high potential in manual operations where a set of interdependent tasks need to be performed, however, due to the complexity of some of the tasks, a subset of them are kept manual — for example, assembly related tasks. Since they share the same physical space, humans and cobots operate in close proximity, and this directly impacts the cobots’ design and the speed at which they can operate. Therefore, it can be argued that possible advantages of cobots tend to be less dependent on the design of the cobot manipulator itself, and more on its application design. In this work, we investigate the impact of these limitations along with the benefits, and contrast them with conventional high-speed industrial robots. This study explores a manufacturing company that has invested in cobot automation for assembly tasks. Three use-cases of cobots are investigated for their productivity and flexibility against conventional industrial robots. The conclusion is made as a set of guidelines for practitioners for the implementation of cobots both from a product perspective, as well as the perspective of the specific tasks and implementations.
Towards the Understanding of Nudging Strategies in Cyber-Physical-Social System In Manufacturing Environments
Authors: Yang Xiaoou , Lim Ahreum , Nicolaides Aliki , Morkos Beshoy 
Published: November 2022

Abstract
The involvement of artificial intelligence in manufacturing settings has revolutionized the relationship between humans, computers, and workforce environments. The prior cyber-physical systems are developing rapidly due to advances in technology. However, human consideration has not advanced at the same rate. Only highlighting technology advancement is not sufficient, given the high interconnectivity between human-technology in Industry 4.0. Integrating the human factor in design and implementation of cyber-physical technology leads to the holistic development of cyber-physical-social systems (CPSS). Nonetheless, little is known regarding workers’ behavior and ethics that mainly pertain to the human factors of CPSSs. Also, little effort is given to seek a methodologically rigorous way to investigate human factors in CPSS. To fill the gap, this paper proposes a research framework that aims to explore the interaction between humans and AI agents in manufacturing setting. Incorporating human considerations properly can enhanced the development of an integrated cyber-physical-social system. The goal of the research methodology presented here is to fundamentally understand a common dimension in CPSS. The objective of the proposed study is to determine how nudging impacts user response on a manufacturing line, both in terms of manufacturing performance and human response. To do so, we introduced the concept of nudging, which can come in the form of audio, visual, and haptic, refers to signals sent by a cyber-physical device to humans to illicit a performance related response. The design of an experiment is discussed in this paper. Quantitative data (assembly time, quality, and assembly errors) and qualitative data (recorded video, pre-experiment questionnaire, and post-experiment interviews) will be used for analysis to determine if and how the types of nudging are observed in cyber-physical-social environments that we see today can impact human response. The results of this research methodology will help inform engineers how cyber physical systems should be implemented in manufacturing environments while considering the impact it has on the human.
On the Combination of Geometrically Nonlinear Models and Substructuring for Multibody Simulation of Wind Turbine Blades
Authors: Jensen Christian Sigurd L. , Pedersen Rasmus B. E. , Blanco Blas , Escalona José L. , Balling Ole 
Published: November 2022

Abstract
This study develops a geometrically nonlinear model of a wind turbine blade utilizing finite strain theory for the calculation of elastic forces. The model is based on the floating frame of reference (FFR) formulation, which is a common choice in the modeling of long and flexible wind turbine blades. To model the nonlinear deformation of blades, the FFR formulation divides the structure into several substructures, which involves a significant increase of the system degrees of freedom. In the presented model, a nonlinear description of the elastic forces is introduced to achieve the convergence of the dynamic blade model at a lower number of substructures. The nonlinear elastic forces are formulated according to the Euler-Bernoulli beam theory, and they account for third-order terms of the potential elastic energy, the so-called geometric stiffness. The developed blade model is formulated in two dimensions and tested in a blade of 44.8 m length, which corresponds to a 2.75 MW wind turbine. Firstly, the results show that linear models do not accurately represent tip blade transverse displacement, and the substructuring technique becomes necessary to account for geometric nonlinearity. Secondly, using nonlinear elastic models significantly reduces the number of substructures needed to achieve convergence of the solution.
Deriving Recommendations for the Use of Agent-Based Models in Engineering Design
Authors: Agyemang Malena , Radwan Noriana , Hicks Sierra , Azhar Fariha , Valencia-Romero Ambrosio , McComb Christopher 
Published: November 2022

Abstract
Agent-based modeling (ABM) is a widely used simulation methodology. The use of ABM in engineering design continues to grow due to increasing interest in modeling and simulating human systems. However, there are few resources specific to engineering design that guide how to develop an ABM, execute an ABM experiment, and assess the subsequent results. This inhibits the effective use of ABM by design researchers who are new to the methodology. The goal of this paper is to provide a resource for both novice and experienced design researchers that details a systematic approach to ABM. First, this paper reviews current ABM research in engineering design, with a specific focus on models used to simulate human systems. Specifically, we review this work with respect to a step-by-step approach to modeling developed in the systems simulation literature. Second, each of the steps is contextualized with respect to the literature review, identifying heuristics and best practices. Through this contextualization, gaps in current practice are also identified.
Mechanical Face Seal Dynamics Subjected to Machine Vibration and Noise
Authors: Green Itzhak 
Published: November 2022

Abstract
Mechanical face seals are wide-spread in many applications, such as centrifugal compressors, submersible pumps, drill-bits for oil and water, hydrocarbon processing equipment, and turbomachinery. Often vibration and noise are unavoidable because of changing environments which can be persistent and forceful. In critical applications when seals fail, they may have significant or even catastrophic consequences. To ensure the safety of such machinery and its associated mechanical components, the vibration and noise must be diagnosed and kept within certain limits. This work focuses on the dynamics of a flexibly mounted stator mechanical face seal that is subjected to combinations of axial broad-band noisy vibrations of the shaft and the housing. The current analysis builds upon the dynamic models that have been developed over the last four decades. In all that body of work, the positions of the housing and the shaft have been considered fixed. The current work relaxes that condition, augmenting the equations of motion to incorporate the said noisy vibrations. While the exact root-causes and sources of machinery vibration are very difficult to ascertain, the current analysis uses some general common causes of noisy seal operation, and provides markers to be observed for diagnosis. While the forcing noises are assumed to originate along the main axis of the seal, because of the strong coupling between the axial and angular modes, the seal is affected (even strongly) in all modes (axial and angular). A numerical simulation ensues, and the results are subject to spectral analyses. Results show that under some design conditions, the seal is largely insensitive to machine vibrations. However, under other design parameters, the seal response exhibits a rich spectral content that stems from various transient phenomena that include (among others) half frequency whirl, synchronous steady-state response, and the natural (eigen) response. Under aggravated noisy conditions the investigated seal exhibits large oscillations and leakage, along with face contact and rubbing. Prolonged rubbing causes faces to wear and leads ultimately to seal failure.
Data Augmentation of Engineering Drawings for Data-Driven Component Segmentation
Authors: Zhang Wentai , Chen Quan , Koz Can , Xie Liuyue , Regmi Amit , Yamakawa Soji , Furuhata Tomotake , Shimada Kenji , Kara Levent Burak 
Published: November 2022

Abstract
We present a new data generation method to facilitate an automatic machine interpretation of 2D engineering part drawings. While such drawings are a common medium for clients to encode design and manufacturing requirements, a lack of computer support to automatically interpret these drawings necessitates part manufacturers to resort to laborious manual approaches for interpretation which, in turn, severely limits processing capacity. Although recent advances in trainable computer vision methods may enable automatic machine interpretation, it remains challenging to apply such methods to engineering drawings due to a lack of labeled training data. As one step toward this challenge, we propose a constrained data synthesis method to generate an arbitrarily large set of synthetic training drawings using only a handful of labeled examples. Our method is based on the randomization of the dimension sets subject to two major constraints to ensure the validity of the synthetic drawings. The effectiveness of our method is demonstrated in the context of a binary component segmentation task with a proposed list of descriptors. An evaluation of several image segmentation methods trained on our synthetic dataset shows that our approach to new data generation can boost the segmentation accuracy and the generalizability of the machine learning models to unseen drawings.
Multi-Stop Band Wave Propagation in a Honeycomb Metamaterial With Embedded Resonators
Authors: Murer Mauro , Lacarbonara Walter , Formica Giovanni 
Published: November 2022

Abstract
This work discusses the stop-band propagation properties of a honeycomb metamaterial hosting a periodic arrangement of highly tunable, infinite-dimensional resonators. The cellular material system architecture takes inspiration from lightweight honeycombs — due to their inherent periodicity, high flexural/shear stiffness — which host resonators with lumped masses exhibiting infinitely many frequencies and modes. These resonators are designed to possess high dynamic resilience and frequency/damping tunability.
In the present work, two resonator architectures are investigated and compared, namely, (i) a cantilever with a tip mass, (ii) a spider-web-like structure with a central mass. Contrary to traditional approaches making use of discrete spring-mass-damper resonators, here the infinite-dimensional resonators are intentionally tailored for their potential of generating, in principle, infinitely many band gaps. The nondimensional dispersion curves are obtained via the Plane Wave Expansion method. By retaining the lowest modes of the two investigated resonators designs, the outcomes show the appearance of multiple band gaps whose bandwidth and central frequency depend on the mass ratio and the nondimensional stiffness of the resonators. Preliminary experimental results based on laser scanning vibrometry, and conducted on 3D printed honeycomb samples, corroborate the theoretical model and predictions.
Learning the Part Shape and Part Quality Generation Capabilities of Machining and Finishing Processes Using a Neural Network Model
Authors: Zhao Changxuan , Melkote Shreyes N. 
Published: November 2022

Abstract
Automatically acquiring knowledge of manufacturing process capabilities from existing data is essential for automated process selection in digital and cyber manufacturing. In this work, we present a neural network model to automatically learn the capabilities of discrete manufacturing processes such as machining and finishing from design and manufacturing data. Concatenating a 3D Convolutional Neural Network (3D CNN) with an artificial neural network, the combined model can learn the part shape and part quality generation capabilities of the manufacturing processes. Specifically, the proposed method takes the voxelized part geometry and part quality information as inputs and utilizes a mixed neural network model (3D CNN + artificial neural network) to predict the manufacturing process label as output. The manufacturing process capability knowledge embedded in the neural network model is scalable and updatable as new manufacturing data becomes available. We present an example implementation of the proposed method with a synthesized manufacturing dataset to illustrate how the method enables automatic manufacturing process selection. The high prediction accuracy shows its predictive strength for use in Computer Aided Process Planning (CAPP).
Bifurcation Analysis of a PD Controlled Motion Stage With a Nonlinear Friction Isolator
Authors: Basta Ehab E. , Gupta Sunit K. , Barry Oumar R. 
Published: November 2022

Abstract
The utilization of mechanical-bearing-based precision motion stages (MBMS) is prevalent in the advanced manufacturing industries. However, the productivity of the MBMS is plagued by friction-induced vibrations, which can be controlled to a certain extent using a friction isolator. Earlier works investigating the dynamics of MBMS with a friction isolator considered a linear friction isolator, and the source of nonlinearity in the system was realized through the friction model only. In this work, we present the nonlinear analysis of the MBMS with a nonlinear friction isolator for the first time. We consider a two-degree-of-freedom spring-mass-damper system to model the servo-controlled motion stage with a nonlinear friction isolator. The characteristic of the dynamical friction in the system is captured using the Lu-Gre friction model. The system’s stability and nonlinear analysis are carried out using analytical methods. More specifically, the method of multiple scales is used to determine the nature of Hopf bifurcation on the stability lobe. The analytical results indicate the existence of subcritical and supercritical Hopf bifurcations in the system, which are later validated through numerical bifurcation. This observation implies that the nonlinearity in the system can be stabilizing or destabilizing in nature, depending on the choice of operating parameters.
A Variable Stiffness Continuum Parallel Manipulator With 3D Printed Pneumatic Artificial Muscles
Authors: Jiang Zhujin , Liu Chen , Zhang Ketao 
Published: November 2022

Abstract
This paper presents a variable stiffness continuum parallel manipulator (VSCPM) with 3D printed pneumatic artificial muscles, which is inspired by parallel mechanisms and soft robotics. The proposed VSCPM is designed by connecting a fixed base and a moving platform with three pneumatic bellows muscles in parallel and circular distribution. A set of optimized 3D printing parameters for fabricating air tight pneumatic muscles by flexible Thermoplastic Polyurethane (TPU) filament is introduced, aiming at achieving rapid and low-cost manufacturing in comparison with other approach such as injecting silicon rubber into prefabricated molds. A prototype of the VSCPM is fabricated using the optimized 3D printing settings. The stiffness of a 3D printed bellows muscle under both positive and negative pressures is tested. The position and stiffness of the VSCPM controlled by both positive and negative pressures are also evaluated. Experimental results show that the VSCPM is capable of achieving a range of steering angle from 0° to 360° and a range of inclination angle from 0° to 45°. The stiffness of the VSCPM can also be adjusted by changing the air pressure supplied to the pneumatic artificial muscles. The novel VSCPM is compact, lightweight and soft to ensure the safe interaction with humans, which has a broad range of application such as wrist rehabilitation.
Multi-Fidelity Physics-Constrained Neural Networks With Minimax Architecture for Materials Modeling
Authors: Liu Dehao , Pusarla Pranav , Wang Yan 
Published: November 2022

Abstract
Data sparsity is still the main challenge to apply machine learning models to solve complex scientific and engineering problems. The root cause is the “curse of dimensionality” in training these models. Training algorithms need to explore and exploit in a very high dimensional parameter space to search the optimal parameters for complex models. In this work, a new scheme of multi-fidelity physics-constrained neural networks with minimax architecture is proposed to improve the data efficiency of training neural networks by incorporating physical knowledge as constraints and sampling data with various fidelities. In this new framework, fully-connected neural networks with two levels of fidelities are combined to improve the prediction accuracy. The low-fidelity neural network is used to approximate the low-fidelity data, whereas the high-fidelity neural network is adopted to approximate the correlation function between the low-fidelity and high-fidelity data. To systematically search the optimal weights of various losses for reducing the training time, the Dual-Dimer algorithm is adopted to search high-order saddle points of the minimax optimization problem. The proposed framework is demonstrated with two-dimensional heat transfer, phase transition, and dendritic growth problems, which are fundamental in materials modeling. With the same set of training data, the prediction error of the multi-fidelity physics-constrained neural network with minimax architecture can be two orders of magnitude lower than that of the multi-fidelity neural network with minimax architecture.
Investigating Priming Effects of Sketch Evaluation Instructions on Idea Generation Productivity
Authors: Weaver Morgan B. , Buck Jacob , Merzdorf Hillary , Dorozhkin Denis , Douglas Kerrie , Linsey Julie 
Published: November 2022

Abstract
Engineering design involves intensive visual-spatial reasoning, and engineers depend upon external representation to develop concepts during idea generation. Previous research has not explored how our visual representation skills influence our idea generation effectiveness. A designer’s deficit in sketching skills could create a need for increased focus on the task of visual representation reducing cognitive resources available for the task at hand — generating concept. Further, this effect could be compounded if designers believed that their sketching skill would be evaluated or judged by their peers. This evaluation apprehension could cause additional mental workload distracting from the production of idea generation.
The goal of this study is to investigate and better understand the relationship between designers’ sketching skills and idea generation abilities. In this paper, we present preliminary results of the relationship between independent measures of sketching skill and idea generation ability from an entry-level engineering design and graphics course. During data collection, task instructions were given in two ways to independent groups: one group was instructed upfront that sketching would be evaluated, while the second group was kept blind to the sketch evaluation. In this paper, we also examine the potential priming effects of sketch quality evaluation apprehension on idea generation productivity. The results show that sketching quality and idea quantity are largely independent, and that the priming effects of sketch evaluation instructions are small to negligible on idea generation productivity.
Ellipse Synthesis of a Five-Bar Linkage
Authors: Plecnik Mark 
Published: November 2022

Abstract
This paper formulates synthesis equations for a two degree-of-freedom planar five-bar linkage to reproduce specified velocity ellipses at specified points in its workspace. The synthesis procedure finds a mechanism that exactly reproduces two ellipses, and affords the designer the free choice of one ground pivot. This paper shows how four solutions to these synthesis equations can be obtained in closed form. Each solution describes a five-bar linkage. The solution procedure reveals shared structure between these five-bars. The new synthesis procedure is applied to a few examples.
Multi-Objective Bayesian Optimization Supported by Gaussian Process Classifiers and Conditional Probabilities
Authors: Valladares Homero , Tovar Andres 
Published: November 2022

Abstract
In the last years, there has been an increasing effort to develop Bayesian methodologies to solve multi-objective optimization problems. Most of these methods can be classified in two groups: infilling criterion-based methods and aggregation-based methods. The first group employs an index that quantifies the gain that a new design can produce in the current Pareto front while the last group uses a (possibly non-linear) aggregation function and a weighting vector to identify a Pareto design. Most infilling-based methods have been developed to solve two-objective optimization problems. Aggregation-based methods enable the solution of many-objective optimization problems but their performance depends on the set of weighting vectors, which are often selected randomly. This study proposes a novel multi-objective Bayesian framework that exploits the rich probabilistic information that can be extracted from Gaussian process (GP) classifiers and the ability of conditional probabilities to capture design preferences. In the proposed framework, a GP classifier is trained to identify design zones that potentially contain Pareto designs. The training process involves the inference of a latent GP that encodes input-space interactions that describe a Pareto design. This latent GP enables the solution of many-objective optimization problems with any standard acquisition function and without the prescription of a weighting vector. Conditional probabilities are utilized to define design goals that promote a uniform expansion of the Pareto front. The proposed approach is demonstrated with two benchmark analytical problems and the design optimization of sandwich composite armors for blast mitigation, which involves expensive finite element simulations.