bluuebunny/crossref_metadata_2025_split huggingface dataset:
Combining 4D MRI With CFD for Investigating Patient-Specific Cardiovascular Flows: A Comprehensive Comparison of ANSYS, COMSOL, and SimVascular Illustrated With the Prediction of Thoracic Aortic Hemodynamics
Authors: Tajeddini Farshad , Romero Torres David A. , McClarty Davis , Chung Jennifer , Amon Cristina H. 
Published: September 2022

Abstract
Computational Fluid Dynamics (CFD) is widely used in both scientific and industrial contexts to provide valuable insights into cardiovascular flows. CFD supports the development of medical devices, describes complex flow physical phenomena associated with disease generation and progression, and even informs surgical planning. Non-invasive technologies such as 4D MRI provide detailed information about blood flow for a given patient, yet CFD allows higher spatial and temporal resolution less invasively. However, the advantages of CFD methods can only be realized through faithful geometry reconstruction, high-quality mesh generation, and suitable definition of patient-specific inlet and outlet boundary conditions. In this regard, 4D MRI measurements can provide the required data to calibrate and validate patient-specific CFD models. Hence, the combination of 4D MRI and CFD is crucial for accurate and efficient in-silico cardiovascular flow predictions for patient-specific geometries. Multiple CFD software, such as ANSYS, COMSOL, and SimVascular, have been widely used in published research works, yet the graphical user interfaces of these software packages have no explicit provisions for leveraging spatio-temporal 4D MRI data. Here, we present a framework to create accurate patient-specific CFD simulations leveraging spatiotemporal 4D MRI data and patient monitoring data. We discuss all aspects of patient-specific modeling, including geometry reconstruction, meshing, numerical simulation, and post-processing of results, focusing on the suitability of each software package and the ease with which the presented workflow can be implemented with the software.
Effect of Phase Difference on Wake Characteristics and Propulsive Performance of Pitching Foils in Side-by-Side Configurations
Authors: Gungor Ahmet , Khalid Muhammad Saif Ullah , Hemmati Arman 
Published: September 2022

Abstract
Numerical investigations of unsteady flows around two pitching foils arranged in side-by-side (parallel) configurations, as a simplified models of fish schools are conducted in OpenFOAM for a range of Strouhal number from 0.15 to 0.5 and at a fixed Reynolds number of 4000. Effects of the phase difference between motions of the two foils is examined in the range of ϕ = 0 – π of increments of π/6. Phase differences of ϕ = π/6, π/2, and 5π/6 with Strouhal numbers of St = 0.25 and 0.4 are selected for analyzing transitions and evolution in the wake. The phase difference has a major impact on characteristics of the wake. Smaller phase angles, such as ϕ = π/6, lead to the development of a merged wake, resembling the wake of two foils undergoing in-phase pitching motion. On the other hand, a split wake is observed when the phase difference is set to higher values, e.g., ϕ = 5π/6, which resemble the wake of those performing out-of-phase pitching motion. Also, ϕ = π/2 yields maximum disparity between the propulsive performance of the foils, which drops to zero as the phase angle approaches to in-phase or out-of-phase.
Inkjet Printing of PEDOT:PSS Inks for Robotic Skin Sensors
Authors: Olowo Olalekan O. , Zhang Ruoshi , Sherehiy Andriy , Goulet Brian , Curry Alexander , Wei Danming , Yang Zhong , Alqatamin Moath , Popa Dan O. 
Published: September 2022

Abstract
Enhancing physical human-robot interaction requires the improvement in the tactile perception of physical touch. Robot skin sensors exhibiting piezoresistive behavior can be used in conjunction with collaborative robots. In past work, fabrication of these tactile arrays was done using cleanroom techniques such as spin coating, photolithography, sputtering, wet and dry etching onto flexible polymers. In this paper, we present an addictive, non-cleanroom improved process of depositing PEDOT: PSS, which is the organic polymer responsible for the piezoresistive phenomenon of the robot skin sensor arrays. This publication details the patterning of the robot skin sensor structures and the adaptation of the inkjet printing technology to the fabrication process. This increases the possibility of scaling the production output while reducing the cleanroom fabrication cost and time from an approximately five-hour PEDOT: PSS deposition process to five minutes. Furthermore, the testing of these skin sensor arrays is carried out on a testing station equipped with a force plunger and an integrated circuit designed to provide perception feedback on various force load profiles controlled in an automated process. The results show uniform deposition of the PEDOT: PSS, consistent resistance measurement, and appropriate tactile response across an array of 16 sensors.
What Role Does Maintenance Play in Achieving Sustainability in Manufacturing? - A Scoping Literature Review
Authors: Bredebach Christina 
Published: September 2022

Abstract
Sustainability has become an important topic in society, industry, and academia. It influences decisions on many levels and there are already many approaches to achieving sustainability in everyday life.
One of the areas with the greatest impact on sustainability is production. Maintenance is often neglected when considering sustainable development, although it can play an important role in achieving sustainability within the three dimensions economy, ecology and social.
This paper is a scoping literature review that identifies and classifies publications on sustainable maintenance in the manufacturing industry for the period 2017 to November 2021 and presents the results organized into categories. The categories include the contribution of maintenance to sustainability, influence of sustainability on maintenance, methods, and tools for sustainable maintenance, and enabling technologies for sustainable maintenance 4.0.
A trend can be identified that sustainable maintenance has received increasing attention in recent years. The research takes place mainly in theory. Numerous methods, tools and frameworks have been identified that can increase sustainability in maintenance. Not all outcomes however can be classified as covering all three pillars of sustainability. While many focus on ecology, only few are committed to social sustainability as well.
For the future, it is important to consider the three areas of sustainability as a unit rather than separately, and to apply the research conducted in sustainable maintenance in practice.
Development of Autonomous Robotic Bin Picking System Using Convolutional Neural Network (CNN) Initially Trained by Human Skills
Authors: Yun Huitaek , Park Jinho , Jun Martin Byung-Guk 
Published: September 2022

Abstract
Smart Manufacturing (SM) emphasizes autonomous self-adoption and decision making, which is possible by the aid of information technology such as big data, sensors, and machine learning techniques. Picking objects autonomously by industrial robots from cluttered bins (Bin picking) is one of topics that the technologies could be applied to manufacturing processes, especially in flexible input and output logistics. One of the methods is to analyze 3D point clouds from depth sensors, and are matched to the geometry model to calculate possible robot posture, which required heavy calculation and complex algorithm to handle the point clouds. Another method is to train neural networks from reinforced learning, however it requires huge amount of trials and trainings to establish the model, starting with failures. In this paper, a convolutional neural network (CNN) model was initially trained from human skills, and it was trained by itself to improve the job accuracy. In the initial stage, an operator selected a block with a depth image from a Lidar sensor by their intuition that a block can be picked up by a robot. The robot tried to pick up the block, and the image of block with the result of the trial by the robot was recorded. CNN was trained after collecting 500 datasets by the operator. Next, in the self-learning stage, the system automatically tried to pick up candidate blocks from the CNN’s prediction. Collected data during the trial was utilized to gradually train the CNN model. The result shows that the job accuracy was 39% with initial CNN, and improved by 71% after 2,000 trials by self-learning step. The collaboration between human and autonomy would enable to apply the system in shop floors by reduced time, simple development, and improved pick-up accuracy.
Net-Shape Tensile Specimens as Representatives of Material Properties of Metal Additive Manufacturing: Evaluation and Correction Factor
Authors: Bass Nicholas , Jalui Sagar , Manogharan Guha 
Published: September 2022

Abstract
Tensile testing is the most prevalent method for characterizing the mechanical properties of additively manufactured (AM) materials. During qualification of metallic AM properties, near-net AM parts are often machined prior to mechanical testing. The aim of this study is to understand the influence of net-shaped tensile coupons without post-AM machining on the accuracy of bulk material properties. The motivation for this study lies in: (1) reducing the qualification time and costs by (2) formulating and validating a correction factor to estimate bulk AM properties from mechanical testing of as-AM coupons. This research focused on the tensile testing of Laser Powder Bed Fusion (LPBF) produced Inconel 718 to isolate the effects of as-AM surface roughness. Six different surface conditions were produced by varying two different laser processing conditions, with and without contour laser scans. Specimens (n = 5 per condition) were tested in both net-shape and post-AM machined conditions. Surface roughness was analyzed using both stylus contact profilometry and micro-computed tomography (micro-CT) non-contact analysis. ANOVA analysis was performed to derive inference on processing conditions and resulting mechanical properties. It was observed that the measurement error in gauge diameter primarily accounts for variability in mechanical properties between machined and net-shape coupons, specifically Ultimate Tensile Strength (UTS). This study presents a methodology to determine corrected gauge diameter based on depth of surface roughness. Findings from this study will enable net-shape tensile data to be compared against machined data for accurately predicting the strength of parts with as-AM surfaces. By accounting for surface roughness depth, tensile strength of net-shape AM coupons was within 1% accuracy to that of machined AM coupons.
Finding Robust Pareto-Optimal Solutions Over Time for Dynamic Disassembly Sequence Planning
Authors: Zhang Xin , Fang Yilin , Liu Quan 
Published: September 2022

Abstract
Disassembly sequence planning plays a crucial role in the reuse and remanufacturing of end-of-life products, which is a combinatorial optimization problem and has been studied by many researchers. However, it is challenging to obtain optimal disassembly sequences due to great uncertainty owing to various unpredictable factors. We note that some of the uncertainties accompanying the products disassembly process are characterized by dynamic changes and can actually be regarded as dynamic disassembly sequence planning problem. Robust Pareto-optimal over time (RPOT) is a good approach to aovid the inconvenience of tracking optimization by finding solutions that remain acceptable over an extended period. Since there are few studies on applying RPOT to combinatorial optimization, the autoregressive prediction model in RPOT is more suitable for continuous search space problems than combinatorial optimization. In this paper, we develop a dynamic disassembly sequence planning problem considering the uncertainty caused by dynamically changing product states. Finding robust Pareto-optimal solutions over time for dynamci disassembly sequence planning to avoid the consumption of frequent switching solutions. To better apply RPOT to combinatorial optimization, online prediction model is proposed to replace the autoregressive prediction model. Experiment is executed in the three scale problems and compared with tracking optimization. The results indicate that online predictors can effectively improve the accuracy of prediction and improve the performance of the algorithm, and RPOT with new predictor is a more practical and time-saving method of addressing dynamic disaseembly sequence planning problem than tracking optimization.
Autoclave Molding of Carbon Fiber Laminates With Interlaminar Carbon Nanotubes
Authors: Quadrini Fabrizio , Bellisario Denise , Iorio Leandro , Proietti Alice , Gallo Nicola , Santo Loredana 
Published: September 2022

Abstract
Carbon nanotubes (CNTs) are deposited between prepreg plies of a carbon fiber reinforced (CFR) laminate during lamination to improve laminate strength. An easy manufacturing procedure has been implemented for this aim in laboratory. CNTs are diluted in a solvent, and subsequently sprayed on commercial woven fabric prepregs for aeronautics. Solvent evacuation is carried out at room temperature. Final composite laminates are produced by vacuum bagging and autoclave molding of coated prepreg plies, by following the typical industrial procedure of aeronautic parts. Quasi-isotropic square laminates with 10 plies have been manufactured by using a unidirectional (UD) CFR prepreg tape with 0/90 stacking sequence. After molding, the square laminates (150 × 150 mm2) were cut to extract rectangular specimens. Mechanical tests were made by bending up to break. Results confirm the positive role of the interlaminar CNTs if they are correctly integrated into the final composite structure.
Double Interpolation to Achieve Linear Strain Path for AISI 1008 Steel Cruciform Specimen
Authors: Hoffman Jordan , Ha Jinjin , Kinsey Brad , Banerjee Dilip , Iadicola Mark A. 
Published: September 2022

Abstract
The automotive industry relies heavily on sheet metal forming processes for many components. Material data solely from uniaxial testing is insufficient to fully define the material behavior of the complex plastic deformation during numerical simulations of the forming processes. In-plane biaxial testing using a cruciform type specimen is a more comprehensive representation than the traditional uniaxial testing alone. Wide ranging biaxial stress states can be imposed by applying different loading conditions on each cruciform axis. However, this can create a challenge to achieve desired deformation paths due to the non-linear relationship between the control parameter, e.g., displacement, and the output of interest, e.g., strain path. In this paper, an interpolation method to develop the displacement control that produces a linear strain path with a desired strain ratio is revisited and expanded upon from the authors’ previous work [1,2]. In the first iteration, linear biaxial displacements were applied to the specimen and the corresponding strain paths were obtained from the numerical simulations. The non-linear strain paths, due to geometry effects of the specimen, were used to reverse engineer a new displacement path that results in a linear strain path. Interpolation is revisited to show increased success with a second iteration. Analysis of the simulation results shows that linear strain paths of a given model can be determined and improved by successive iterations of interpolating the strain data from adjacent deformation paths.
Analysis of the Unique Mechanics of Shear Localization in Metal Cutting Processes
Authors: Fazlali Mohammadreza , Ponga Mauricio , Jin Xiaoliang 
Published: September 2022

Abstract
Recent experimental observations show that the frequency of stress and temperature fluctuations on the cutting tool’s rake face and the frequency of residual stress fluctuation at the finished surface of the workpiece are equal to the shear band formation’s frequency. In this article, new experimental observations of the shear band formation in cutting processes are presented. Then, the spacing between neighboring shear bands (which determines the shear band formation’s frequency) is obtained from different theoretical methods and compared with the experimental results. It is shown that the shear band spacing in cutting processes cannot be obtained from the theories developed in other dynamic deformation applications, including dynamic compression and torsion tests and ballistic impacts, due to the unique mechanics of cutting. In addition, we show that due to the intense plastic deformation in the primary deformation zone, the cooling rate of the shear band formed during cutting processes is considerably higher than the workshop cooling rates (6.85 × 108 K·s−1 for the cutting speed of 60 m·min−1 compared to 50 K·s−1 - 2 × 104 K·s−1 for workshop cooling rate of Ti-6Al-4V). The rapid cooling rate indicates the considerable amount of heat transferred into the cutting tool and explains the ductile to brittle transition in the fracture mechanism of shear band formation in cutting processes.
Ultrafast Laser Texturing of Metal Surfaces: Effects of Process Parameters on Surface Reflectance and Possibility of Hierarchical Structuring
Authors: Khadka Nishan , Yang Yucheng , Haug John , Palei Milan , Rosenberger Matthew , Hoffman Anthony , Kinzel Edward 
Published: September 2022

Abstract
Ultrafast laser processing has been widely studied for surface texturing. The complex interaction between the laser energy, plasma, and surface chemistry produces complex morphologies including Laser-Induced Periodic Surface Structures and random higher aspect ratio geometries. Laser texturing allows engineering of metallic surface’s wettability as well as the reflectance on either broadband or narrowband basis. This paper experimentally maps the laser process parameters to the surface morphology and diffuse reflectance for stainless steel, aluminum, and copper substrates. All experiments are conducted with a 1030 nm wavelength, 230 fs pulse length laser in an ambient environment. The results show how the common morphological regimes shift with material and how the reflectance varies with morphology. To further decrease the reflectance, hierarchical structures are generated by first locally micromachining the surface to form a lattice of trenches using the focused laser beam, before texturing the surface with a rastered, defocused laser beam. The micromachined features interact with laser texturing and increase light trapping on the surface. This is a function of the depth and periodicity of the hierarchical structures as well as the surface topography. This approach provides the ability to lower the surface reflectance and add an extra level of control for directing deep micro-cavities along the surface.
A Surface Roughness Characterization Method for Additively Manufactured Products
Authors: Wang Andi , Jafari Davoud , Vaneker Tom H. J. , Huang Qiang 
Published: September 2022

Abstract
In many additive manufacturing processes, surface roughness is a critical quality concern. Due to the nature of the layer-by-layer manufacturing process, the pattern of surface roughness depends on the location on the surface, i.e., the layer number and the location within each layer. Adequate description of the surface roughness enables us to develop effective post-processing plans, reveal the root causes of the roughness, and generate accurate compensation schemes.
In this work, we propose a three-step surface roughness characterization method (SRCM). This method is based on the dense point cloud data generated from the surface scan of additively manufactured products. First, we use a double kernel smoothing spatial variogram estimator to represent the heterogeneous roughness property at different surface locations. Second, we extract the magnitude and scale of surface roughness from the estimated variogram. Third, we use Gaussian Process to build a roughness map on the entire surface based on the roughness characterization on these sampled points.
The SRCM is demonstrated from a high-density 3D scan of a cylindrical product fabricated by a wire-arc additive manufacturing process. It shows that our approach serves as an effective tool to infer the roughness map from the 3D point cloud data. In the end, we will briefly discuss how to use the inferred roughness map to develop an optimal surface smoothing method.
Nanoporous Carbon Nanotube Coating for 3D Printing of High-Performance Continuous Fiber Reinforced Polymer Composites
Authors: Pappas John M. , Dong Xiangyang 
Published: September 2022

Abstract
High strength and lightweight continuous carbon fiber reinforced composites are desirable structural materials for applications in various industries including aerospace, automotive, and defense. Additive manufacturing (AM) of such important materials may provide multiple benefits including reduced cost, improved manufacturing efficiency, and the ability to fabricate complex structures not possible with traditional methods. Despite these benefits, a significant challenge with AM of continuous carbon fiber composites is poor impregnation of the fiber bundle with matrix material. When there is a lack of matrix material, voids develop within the fiber bundle and reduce mechanical properties of the composite including strength and stiffness. To minimize void formation, low speed manufacturing is typically necessary to facilitate impregnation. In this work, it was shown that fiber bundle impregnation can be significantly improved by applying thin, nanoporous coatings to the continuous fiber bundle. Using an electrophoretic deposition process, the coating microstructure, including thickness and nano pore size, was easily controlled through effective tuning of process parameters. Ultimately, individually coated carbon fibers were obtained and provided improvements in fiber bundle impregnation without sacrificing the flexibility of the fiber bundle. A highly absorbent yet flexible fiber bundle was desirable for 3D printing applications and would facilitate fabrication of complex geometries. With such tailored nanoporous coatings, fifteen-fold improvement in resin absorption time due was observed due to improved wicking by the nanoporous structure. Such improvements in absorption characteristics have a great potential for drop on demand or other resin-based 3D printing techniques. Furthermore, mechanical characterization demonstrated the potential of nanoporous coatings for additive manufacturing of high performance carbon fiber reinforced composites.