bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Numerical Analysis on a Solar Chimney With an Integrated Thermal Energy Storage With Phase Change Material
Authors: Buonomo Bernardo , Capasso Lucia , Fatigati Angelo , Manca Oronzio , Nardini Sergio 
Published: September 2020

Abstract
In this paper, a two-dimensional numerical investigation on a prototypal solar chimney system integrated with an absorbing capacity wall in a south facade of a building is presented. The capacity wall is composed of a high absorbing plate and an assigned thickness of phase change material. The chimney consists of a converging channel with one vertical absorbing wall and the glass plate inclined of 2°. The chimney is 5.0 m high, with the channel height equal to 4.0 m, whereas the channel gap is at the inlet equal to 0.34 m and at the outlet it is 0.20 m. The thermal energy storage system is 4.0 m high with a paraffin wax. The transient analysis on a two-dimensional model in airflow is carried out and the governing equations for natural convection in turbulent flow with Boussinesq assumption and thermophysical properties temperature independent are given in terms of k-ε turbulence model. The problem is solved by means of the commercial code Ansys-Fluent using the finite volume method. The numerical analysis was intended to evaluate the thermal and fluid dynamic behavior of the solar chimney integrated with a latent thermal energy storage system for different values of the PCM thickness. The analysis is accomplished at the 21 June by the sunrise to sunset with the chimney oriented towards south. The simulations are performed considering the solar chimney located in Aversa, Italy. Results are given in terms of wall temperature distributions, air velocity and temperature fields and transversal profiles in the channel whereas in terms of liquid fractions, temperature and stream function fields for the phase change material. Thermal and fluid dynamics behaviors are evaluated in order to have some indications to improve the energy conversion system.
Numerical and Experimental Study of Bioink Transfer Process in Laser Induced Forward Transfer (LIFT) 3D Bioprinting
Authors: Qu Jie , Dou Chaoran , Li Jianzhi , Rao Zhonghao , Xu Ben 
Published: September 2020

Abstract
As a promising 3D bioprinting process, laser induced forward transfer (LIFT) has attracted attention in the last decade due to its advantages of non-contact, nozzle-free, high dropping rate and high resolution. However, the mechanism of bubble/jet formation under laser inducement has not been well comprehended yet. To better understand the multiphase process, the bubble formation and jet process under single laser pulse was explored in this study, using both the Computational Fluid Dynamics (CFD) model and experimental study. The results showed that under a laser pulse with the Gaussian distribution, a vapor bubble was formed around 0.1μs, then the bubble was expanded over time. During the bubble expansion process, the maximum magnitude of velocity could reach as high as 22m/s. The pressure near the laser interaction area was around 4.72 × 107 Pa, which is 470 times of the ambient pressure. After increasing the pulse energy and focal spot area, the liquid bubble layer moved downward to complete the bioink transfer process after the collapse of glycerol vapor bubble, which showed similar flow characteristics as the experimental results under the same laser fluence (1.4J/cm2). When the laser fluence was decreased to 0.8 J/cm2, a regular jet flow could be observed. The proposed multiphase numerical model can be used to understand the mechanism of bubble/jet formation under laser inducement and provide some insights into the bioink transfer during LIFT process, in order to eventually optimize the LIFT 3D-printing process with greater cell viability.
Review of Artificial Bubble Generation Techniques and Their Suitability for Phenomenological Boiling Studies
Authors: Dhillon Navdeep Singh 
Published: September 2020

Abstract
The heterogeneous boiling of liquids on hot surfaces, despite its importance, is an extremely complicated and murky phenomenon. It involves the random probabilistic nucleation of multiple bubbles whose growth, interaction, and departure, further, depends on processes involving heat transfer, fluid flow, and interfacial phenomena. This, and the random tumultuous nature of boiling makes experimental studies of the process extremely difficult. For achieving a phenomenological understanding of boiling, several researchers have relied on experiments involving artificially generated bubbles on solid surfaces. In this paper, we evaluate these methods of artificial bubble generation and explore how closely they replicate actual heterogeneous boiling conditions experienced by bubbles. Based on this, we assess the suitability of these methods for conducting phenomenological boiling studies, and identify their potential advantages and drawbacks.
Sensitivity Analysis of Error Motions and Geometric Errors in the Case of Sphere-Shaped Workpiece
Authors: Li Zongze , Sato Ryuta , Shirase Keiichi 
Published: September 2020

Abstract
Motion error of machine tool feed axes influences the machined workpiece accuracy. However, the influences of each error sources are not identical; some errors do not influence the machined surface although some error have significant influences. In addition, five-axis machine tools have more error source than conventional three-axis machine tools, and it is very tough to predict the geometric errors of the machined surface. This study proposes a method to analyze the relationships between the each error sources and the error of the machined surface. In this study, a kind of sphere-shaped workpiece is taken as a sample to explain how the sensitivity analysis makes sense in ball-end milling. The results show that the method can be applied for the axial errors, such as motion reversal errors, to make it clearer to obverse the extent of each errors. In addition, the results also show that the presented sensitivity analysis is useful to investigate that how the geometric errors influence the sphere surface accuracy. It can be proved that the presented method can help the five-axis machining center users to predict the machining errors on the designed surface of each axes error motions.
Bending Simulation of a Shielding Braid Toward its Lifespan Prediction
Authors: Narita Shuhei , Wakamatsu Hidefumi , Morinaga Eiji 
Published: September 2020

Abstract
In this paper, a method to simulate deformation of a shield braid of a cable, which is useful for predicting its lifespan, is proposed. A shield braid in a shielded cable used in a robot may break due to repetitive movement of the robot. For avoiding that, the lifespan of the shield braid must be known. However, fatigue testing to measure its lifespan requires much time and cost. Predicting the lifespan of a shield braid by simulation would reduce such time and cost. As a shield braid is composed of many strands, each strand is modeled based on the differential geometry at first. After that, a method to predict the shape of one strand from that of another strand adjacent is proposed for reduction of computation time. Then, the deformed shape of the whole braid can be simulated when a shield cable is bent. Furthermore, strain at any point on the braid can be estimated from the deformed shape of the braid simulated. Estimated strain is useful for prediction of the lifespan of the braid when the cable is bent repeatedly.
Online-Correction of Robot-Guided Fused Deposition Modeling
Authors: Mewes Gian , Fay Alexander 
Published: September 2020

Abstract
In this paper, a method for online monitoring and correcting robot-guided fused deposition modeling is presented. For development a six-axis industrial robot was equipped with a 3D printing nozzle and used for 3D printing. Additionally, two laser line sensors were mounted next to the printing nozzle at the robot’s flange for capturing process data. By comparing CAD planning data to a point cloud of a workpiece printed with the robot, the measuring accuracy of the system is presented. For online monitoring and correcting, the measurement data of those sensors are analyzed for the unique structure of the edge of the currently printed layer. By detecting this edge, relevant process parameters are identified and compared with the planning data. Based on this comparison, corrections are derived and used for adjusting the robot’s trajectory. Furthermore, a concept for generating comparative measurement data in a simulation environment is presented.
Change Detection in Drilling Process Based on Temperature Nearby Cutting Edge by LSTM Neural Network
Authors: Metheenopanant Punnawit , Suwa Haruhiko , Tokumura Shogo , Murakami Koji , Nonaka Yoshiaki 
Published: September 2020

Abstract
This study considers to efficiently collect “highly value-added” data for in-process anomaly detection of cutting tools in machining processes, and focuses on collection of time-series data of temperature nearby the tool cutting-edge by using a wireless tool holder system composed of an internal temperature measuring device and a wireless transmitter, which is connected with a thermocouple built-in the cutting tool. We then propose a method to detect a change of tool performance based on a recurrent neural network (RNN) with a long short-term memory (LSTM) structure. The capability of the proposed RNN system with LSTM is demonstrated through computational experiments, and demonstrate the time-series data of temperature nearby cutting tool tip is applicable for change detection of cutting tools status.
A Production Planning Based on Shipping Record for Food Company
Authors: Uematsu Hiroya , Sakaguchi Tatsuhiko , Uchiyama Naoki , Hiramatsu Daichi , Hiramatsu Kensuke 
Published: September 2020

Abstract
The production system of the food factory is basically a make-to-stock production. Therefore, to achieve efficient production, the integration of demand, production, and stock information is essential. In this paper, we propose a production planning method based on the demand prediction in order to decrease the stock in the factory of Japanese traditional food ‘Tsukudani’. In the proposed method, the demand is first predicted according to the shipment records, then the material requirement planning and the capacity requirement planning are done based on the demand prediction. Finally, we carry out numerical experiments to validate the effectiveness of the proposed method.
Development of Sensorless Force-Control-Based End-Effector for Automated Robot Polishing
Authors: Tsukada Takuhiro , Ogawa Shotaro , Koto Katsuki , Kakinuma Yasuhiro 
Published: September 2020

Abstract
As the finishing process in manufacturing a fine mold, manual polishing is typically performed to enhance the surface quality. On the other hand, manual polishing causes increase in costs and health damage to the workers due to sucking polishing dusts. Hence, polishing automation is strongly required by utilizing industrial robots. Regarding robot polishing, highly responsive polishing pressure control is definitely needed so that macro-micro system integrating high-performance end-effector into the articulated robot could be an appropriate approach because response of the robot itself is not sufficiently high. From this viewpoint, the purpose of this study is to develop an end-effector having the ability to simultaneously control polishing force and tool spindle speed. The mechanism and control system of the end-effector are designed and experimentally evaluated. In terms of force control, observer-based force control, which does not require any additional force sensor, is implemented. The experimental results show that the developed end-effector successfully control polishing force with 0.1 N and bandwidth up to 23 Hz.
Fuzzy Classification of Gear Fault Using Principal Component Analysis-Based Fuzzy Neural Network
Authors: Zhou Kai , Tang J. 
Published: September 2020

Abstract
Condition assessment of machinery components such as gears is important to maintain their normal operations and thus can bring benefit to their life circle management. Data-driven approaches haven been a promising way for such gear condition monitoring and fault diagnosis. In practical situation, gears generally have a variety of fault types, some of which exhibit continuous severities of fault. Vibration data collected oftentimes are limited to reflect all possible fault types. Therefore, there is practical need to utilize the data with a few discrete fault severities in training and then infer fault severities for the general scenario. To achieve this, we develop a fuzzy neural network (FNN) model to classify the continuous severities of gear faults based on the experimental measurement. Principal component analysis (PCA) is integrated with the FNN model to capture the main features of the time-series vibration signals with dimensional reduction for the sake of computational efficiency. Systematic case studies are carried out to validate the effectiveness of proposed methodology.
Material Model Calibration by Deep Learning for Additively Manufactured Alloys
Authors: Wang Zihan , Xu Hongyi , Li Yang 
Published: September 2020

Abstract
Metal parts manufactured via Powder Bed Fusion (PBF) process show great potential in industrial applications. Hierarchical, heterogeneous microstructure characteristics of the PBF-built alloys pose a significant challenge to the prediction of structural performances. To enable computational engineering of this type of materials, multiscale microstructure modeling framework has been proposed to predict the stochastic material properties. AlSi10Mg built by Selective Laser Melting (SLM) is selected as the demonstrative example. At the microscale, the epitaxial granular structures are reconstructed based on Scanning Electron Microscopic Electron Backscatter Diffraction (SEM EBSD) images. The microscale analysis provides property inputs for the mesoscale model, which captures the fish scale like melt pools at the millimeter scale. The predicted material properties are compared with the experimental data for further calibration of the material constitutive models. One critical challenge is that some parameters in material models cannot be directly obtained from experimental tests. In this work, we establish a machine learning-based model calibration framework to predict the unknown material parameters. Furthermore, several machine learning methods are compared to shed lights on their capability of capturing the relation between input parameter values and the resultant prediction errors.
Smart Production System Modeling: Bernoulli Serial Line Case
Authors: Sun Yuting , Zhu Tianyu , Zhang Liang 
Published: September 2020

Abstract
The manufacturing industry has entered the era of Industry 4.0/Smart Manufacturing. New technologies have dramatically changed the way manufacturing activities are carried out on the factory floor. In addition to an enhanced level of equipment automation, automation of decision-making has been one of the key objectives of these new initiatives. On the other hand, a critical issue that has been overlooked is the construction of mathematical models in manufacturing research and studies, which are typically done manually. This manual, ad-hoc nature of mathematical modeling is quite problematic when modeling the job flow in a manufacturing process. As a result, the quality of the models obtained may heavily depend on the experience and personal preference of the modeler. The goal of this paper is to develop a method to standardize and automate the modeling process using standard manufacturing key performance indices in the framework of Bernoulli serial production line model.
Investigation of a Magnetic-Field-Assisted Stereolithography Process for Printing Functional Part With Graded Materials
Authors: Joyee Erina Baynojir , Pan Yayue 
Published: September 2020

Abstract
Additive manufacturing (AM) has many advantages over traditional fabrication techniques, especially for manufacturing sophisticated structures with intricate architecture. Most AM techniques however are often limited to single material with homogeneous properties. To incorporate multi-functionality in AM fabricated parts, researchers have recently paid much attention to multi-material AM techniques which allow locally programmed material properties. Challenges still remain to manipulate material during AM process for getting desired functionalities in fabricated parts. In this study, we present a novel magnetic-field-assisted stereolithography (M-SL) AM technique capable of printing particle-polymer composites with graded material distributions. This study characterizes process parameters for particle trace development and establishes the relationship between the process and printed properties.
Effects of Curing-Induced Residual Stress on Composite Strength
Authors: Sun Haotian , Zhang Dianyun 
Published: September 2020

Abstract
An integrated processing-damage model was developed to predict the inter-lamina strength of a plain weave composite flange manufactured using the Resin Transfer Molding (RTM) technique. The plain weave composite flange was subjected to four-point bending test to characterize its inter-lamina strength. A Representative Volume Element (RVE) at the fiber–matrix level was established to study the effect of curing-induced residual stress on the resulting composite strength. In order to calculate the residual stress, the curing cycle of the experiment was imposed on the RVE. After fully cured, the RVE was virtually loaded and the cohesive element and Smeared Crack Approach (SCA) were employed to capture the fiber-matrix debonding and matrix cracking responses, respectively. Due to the nature of stress history dependency, the SCA was formulated in the incremental form to reflect the stress buildup. The result shows that both fiber-matrix debonding and matrix cracking can be captured during the virtual loading. However, the load drop in the simulation mainly depends the matrix cracking. As the stress buildup in the matrix is dramatic, it demonstrates that the residual stress has large impact on the composite inter-lamina strength. The numerical methods in this paper can be used as an efficient tool in optimizing the curing process of composite material.
Exact H2 Optimal Solutions to a SDOF System With Electromagnetic Tuned Inerter Damper for Vibration Control
Authors: Luo Yifan , Sun Hongxin , Wang Xiuyong , Chen Anhua , Zuo Lei 
Published: September 2020

Abstract
In order to improve the performance of the tuned mass damper (TMD) with a smaller physical mass for machining vibration suppression and energy harvesting, a dual-functional inerter-based damper, called electromagnetic tuned inerter damper (ETID), is proposed. To evaluate the performance of the ETID, the model of coupled ETID and a single degree of freedom (SDOF) system has been established. The H2 optimal design of the ETID-SDOF system has been conducted, whose goal is to minimize the value of the root mean square (RMS) of the displacement and absolute acceleration of the SDOF system. The analytical solutions of the design parameters of the ETID-SDOF system, namely, frequency ratio and damping ratio, have been derived. The control performance and robustness for the undamped SDOF system with ETID have been evaluated via parametric study compared with the undamped SDOF system with the TMD system. The potential other layouts of the ETID are also discussed. The influence of the structural damping on design parameters and performance has also been investigated.
Tool Path Generation Method for High-Quality Machining of Free-Form Surfaces
Authors: Takanashi Yuki , Aoyama Hideki 
Published: September 2020

Abstract
Machining data (NC program) is generated by a CAM system, which generates the tool path from the target shape as a plane approximation surface instead of a free-form surface. Owing to this plane approximation, machining accuracy is reduced. In this paper, we propose a method to process the shape with high accuracy by defining the areas where accuracy is not required as a plane approximation surface and defining the part where accuracy is required as free-form surfaces.
The Concurrent Validity of Session-Rating of Perceived Exertion Workload Obtained Face-to-Face Versus Via an Online Application: A Team Case Study
Authors: Fox Jordan L. , O’Grady Cody J. , Scanlan Aaron T. 
Published: September 2020

Purpose: To compare the concurrent validity of session-rating of perceived exertion (sRPE) workload determined face-to-face and via an online application in basketball players. Methods: Sixteen semiprofessional, male basketball players (21.8 [4.3] y, 191.2 [9.2] cm, 85.0 [15.7] kg) were monitored during all training sessions across the 2018 (8 players) and 2019 (11 players) seasons in a state-level Australian league. Workload was reported as accumulated PlayerLoad (PL), summated-heart-rate-zones (SHRZ) workload, and sRPE. During the 2018 season, rating of perceived exertion (RPE) was determined following each session via individualized face-to-face reporting. During the 2019 season, RPE was obtained following each session via a phone-based, online application. Repeated-measures correlations with 95% confidence intervals were used to determine the relationships between sRPE collected using each method and other workload measures (PL and SHRZ) as indicators of concurrent validity. Results: Although all correlations were significant (P < .05), sRPE obtained using face-to-face reporting demonstrated stronger relationships with PL (r = .69 [.07], large) and SHRZ (r = .74 [.06], very large) compared with the online application (r = .29 [.25], small [PL] and r = .34 [.22], moderate [SHRZ]). Conclusions: Concurrent validity of sRPE workload was stronger when players reported RPE in an individualized, face-to-face manner compared with using a phone-based online application. Given the weaker relationships with other workload measures, basketball practitioners should be cautious when using player training workloads predicated on RPE obtained via online applications.