bluuebunny/crossref_metadata_2025_split huggingface dataset:
River Crossings: Developing a Mobile GIS Approach to Monitoring Activities
Authors: Carnicero Martin , Vázquez Maureen 
Published: November 2019

Abstract
TGN operates a system of 9,000 kilometers of natural gas pipelines with numerous river crossings. According to the mandatory monitoring program, river crossings are visited at least once a year with additional visits for major rivers during the rainy season. Basic data such as depth of cover for each line, photographs and descriptions are surveyed in the field. Later on, this information is manually entered in an electronic form for its use in risk analysis, to evaluate the need of remediation works. This task has two main problems: first, it is very time consuming for surveyors, and second, it is difficult to know the location within the river crossing where data was taken. At the end, monitoring forms came late in the year and its information is difficult to understand. To cope with this problem, a new approach was developed. A GIS mobile application was developed and installed in tablets used in the field, guiding the surveyor through the completion of an electronic form along each pipeline, having a satellite image in the background, as a global reference of where he is standing. All the information is geo-referenced using a built-in GPS. Once it is finished, by means of a simple WIFI/4G connection, information is sent to GIS servers, without the need to be typed at the office. Later on, it is captured and placed into the monitoring form format. Specialists can access and evaluate this information from the database visualizing it in the corporate GIS with minimum delay. This improvement has resulted in a significant decrease in time for the entire data flow process and a better quality of the information gathered, which results in a more realistic risk analysis.
Monitoring and Screening of Pipelines for Movement: A Fast and Cost-Effective Alternative for Pipeline Operators to Measure Drift in Pipelines
Authors: Schorr Michael , Kole Klaas , Foessing Ferdinand 
Published: November 2019

Abstract
For pipelines in the oil, gas, and mining industry, movement of pipelines is one of the main integrity hazards. This movement in most cases is caused by landslides instigated by heavy rain, earthquakes or volcanic activities. If the pipeline movement remains undetected at an early stage, it can lead to the need for costly repairs to prevent, remove, or repair potential or actual damage. Moreover, if the movements stay undetected for too long, these lines may fail and lead to catastrophic events.
This paper will illustrate what a fast and cost-effective solution to avoid these threats at an early stage looks like and how it works. It will explain the process and demonstrate the full power of this technology on the basis of a case study.
The standard solution for pipelines without a permanent position monitoring system at the time of installation includes the use of intelligent tools that are able to detect even the slightest changes in the trajectory. These inspection tools are quite expensive to run, especially when multiple screening runs are required throughout a year, e.g. before and after the rainy season or after a seismic event. Other monitoring solutions are either limited to only a specific area where the movement has already been detected at an earlier stage or lack the precision required to serve as an early warning system, such as LIDAR or satellite image comparison.
Over the years, ROSEN has developed a technology that can bridge the gap between frequent measurements and cost-effective service. It is based on an electronic gyroscope that is commonly used in Inline Inspection tools but can also be installed in readily available cleaning tools. When first run in a pipeline, it records the whole pipeline trajectory, leaving no segment undocumented. The next step is to compare these recorded pipeline routing measurements with already existing trajectory baseline data, recorded earlier by any ILI tool with an optical gyro or similar. This comparison will reveal any deviation between both trajectories and precisely determine any pipeline movement. A case study will demonstrate how the comparison is achieved.
When performing repetitive inspections, this screening comparison enables the operators to detect the onset of movements and monitor the progress of any known pipeline movement. It allows them to distinguish between stable areas from dynamic ground movements and keeps close track of changes in the pipe course. Through regular repetitions, any further development of the movement is tracked, and appropriate reactive measures can be scheduled in a timely manner. This new service provides a cost-effective and powerful early warning tool for geological pipeline integrity threats that can lead to loss of integrity, the asset, or — worst-case scenario — loss of life or environmental contamination, while at the same time, it reduces the necessity of pipeline intervention that will affect production.
Smart Factory of Industry 4.0: Connection Infrastructure, Data Acquisition, Data Processing, and Applications
Authors: Zhiqiang Zhao , Quan Chua Wei , Xiaoming Ding , Ashok Prabhu Vinayak 
Published: November 2019

Abstract
Smart factory adopts cyber-physical technologies integrating independent discrete systems into a context-sensitive manufacturing environment to optimize manufacturing processes using decentralized information and real-time communication. This paper presents our work in the realization of a smart factory, which comprises of a four-layer hierarchical architecture, i.e. connection infrastructure, data acquisition, data processing and smart applications. In the connection infrastructure layer, all shopfloor machines are connected through diverse protocols, IoT sensors, PLC interfaces and DNC connectors. A centralized IoT gateway supports such a scalable and adaptable connection and ensures a reliable communication among all heterogeneous manufacturing systems. In the data acquisition layer, the real-time machine and job data are acquired from shopfloor systems. Machine data indicates machines’ working condition and job data reveals the production information. The data processing layer comprises of three modules, i.e. shopfloor monitoring, data visualization and data analytics, which monitor and visualize shopfloor activities and analyze the semantics of various data using AI-based TPM engines providing the scientific indicators for next-step decisions. The smart application layer provides with several decision-making and remote control functions for shopfloor productivity and efficiency, such as predictive maintenance, shopfloor management, machine & job optimization and digital twin. The smart factory system has been implemented in the manufacturing shopfloor at Nanyang Polytechnic. The results and validation show that the system can simultaneously collect and analyze the manufacturing data from shopfloor systems, and further communicate with and control the shopfloor systems with decision-support functions. The overall shopfloor efficiency and flexibility can be significantly improved towards a smart factory of Industry 4.0.
FEM Investigation of Damage Evolution at Elevated Temperatures With Multiple Impact Hits in Vibration Assisted Nano Impact Machining by Loose Abrasives
Authors: Duong Nick H. , Ma Jianfeng , Lei Shuting , Sundaram Murali , Jahan Muhammad P. 
Published: November 2019

Abstract
In this paper, a computational study of a novel nanomachining process, Vibration Assisted Nano Impact machining by Loose Abrasives (VANILA), is conducted using the commercial FEM software package ABAQUS. In this novel nanomachining process, an atomic force microscope (AFM) is utilized as a platform and the nano abrasives are injected in the slurry which is located between the workpiece and the vibrating AFM probe. These nano abrasives impact the workpiece and result in nanoscale material removal. In this research, diamond particles are used as loose abrasives and the ductile mode machining is used to describe the behavior of the brittle silicon workpiece. This study aims to investigate the effects of operating temperature and number of multiple impact hits on material removal mechanism of VANILA process. The impact speed of the loose abrasives is kept constant at 200 m/s and the impact angle is fixed at 90°. The frictional coefficient during the machining is considered to be 0.05. The material removal mechanism at various operating temperatures (20°C, 100°C, 200°C, 400°C, 600°C, and 800°C) and multiple impacts are tested. It is found that the operating temperature and number of impact hits have substantial influence on material removal volume in the VANILA process.
Numerical Simulation of Pure Water Jet Machining of Al 6061-T6 With Experimental Validation
Authors: Pasken Greg , Ma Jianfeng , Jahan Muhammad P. , Lei Shuting 
Published: November 2019

Abstract
Pure water jets are not as effective as abrasive water jets for cutting hard materials at large scales. Pure water jets can have kerfs as small as 0.076 mm, which is approximately the width of a human hair. This allows for small detailed cuts on workpiece material [1]. Research into using pure water jet to machine aluminum at small scales is important, as this will allow small scale and precision machining of the work piece material. At micro scales, water jet cutting with typical abrasives is not possible because the abrasive particles are typically in the micron range which is around the size of the cut. At small scales a pure water jet is more effective than abrasive water jet machining, as special nanometer size abrasives would be needed at small scales. A pure water jet only needs the correct size orifice to conduct machining at the small scale. These are the reasons why this study uses a pure water jet to conduct small scale machining of aluminum. This study investigates the use of ABAQUS’s Smoothed Particle Hydrodynamics to simulate pure water jet machining of metals and compares the simulation results of a water jet machining of Al6061-T6 to experimental results using the same material. The simulation results compare favorably to experimental results with only 2.81% error in the width of the cut. The predictive FEM modeling is then conducted for other combinations of machining parameters (orifice diameter and inlet pressure). It is found that orifice diameter and inlet pressure have substantial influence on the width and depth of cut. The results of the study open new possibilities for machining metals using a pure water jet at the micrometer scale and at smaller scales.
Experimental Investigation of Stratasys J750 PolyJet Printer: Effects of Orientation and Layer Thickness on Mechanical Properties
Authors: Wei Xingjian , Bhardwaj Abhinav , Shih Chin-Cheng , Zeng Li , Tai Bruce , Pei Zhijian 
Published: November 2019

Abstract
The J750 PolyJet printer is the newest model of full-color, multi-material 3D printer from Stratasys. Currently, limited information is available about the effects of control factors on mechanical properties such as elastic modulus, ultimate tensile strength, and elongation. In this study, the effects of two control factors, orientation and layer thickness, on mechanical properties of samples printed by the Stratasys J750 printer are investigated. The results show that orientation significantly affects mechanical properties. Specifically, samples printed with its axial direction parallel to the direction of printing have the highest elastic modulus, and elongation, whereas samples printed with its axial direction perpendicular to the direction of printing have the highest ultimate tensile strength. Also, layer thickness makes a significant difference for mechanical properties, and larger layer thickness leads to higher ultimate tensile strength and elongation. These results would be valuable to researchers and practitioners who use J750.
Assessment of Microstructure and Mechanical Properties in Laser Cladding, Welding and Surface Polishing Through Online Monitoring of Thermal History
Authors: Nath Ashish Kumar , Gopinath Muvvala 
Published: November 2019

Abstract
Monitoring and controlling the microstructure, phases, and thermal stresses in laser cladding of materials which determine their mechanical properties is essential for ensuring repeatability and reproducibility in refurbishing engineering parts and building functional parts by layer-by-layer deposition in additive manufacturing process. Several studies have been reported on on-line monitoring of temperature, melt-pool geometry, and porosity etc. in laser powder deposition process, but only a few on the assessment of solidification morphology, microstructure, and thermal stresses. Since these features are dictated by the melt-pool lifetime, cooling and solidification rates, their effects on the evolution of microstructure and the state of ceramic particles in laser deposition of Ni-super alloy and metal matrix composites of WC and TiC are investigated in the current study. Good correlation exists between the thermal history monitored online and the solidification characteristics. Process maps based on the melt-pool lifetime as a function of laser cladding parameters for these materials are developed. On-line monitoring of thermal cycle is extended to laser welding of stainless steel and titanium which are difficult to join together due to the formation of brittle intermetallic phases, and laser polishing of thermal sprayed ceramic coating to develop a better understanding and control of these processes. Melt-pool lifetime is found to have significant effect on the crack growth in fusion welding and by optimizing the former the later could be mitigated. Similarly, the cooling rate in laser polishing of thermal sprayed ceramic coating is found to have significant influence on the surface roughness and residual stress. These studies show that the online monitoring of thermal history can be exploited for controlling the process quality and ensuring the repeatability and reproducibility in different laser material processing modalities.
Adaptive Tool Path Planning Strategy for 5-Axis CNC Machining of Free Form Surfaces
Authors: Mane Hrishikesh , Pande S. S. 
Published: November 2019

Abstract
This paper presents a curvature based adaptive iso-parametric strategy for the efficient machining of free form surfaces on 5-axis CNC machine using the flat end mill tool. One iso-parametric boundary of the surface is selected as the initial tool path. Set of cutter contact (CC) points are chosen adaptively on the initial tool path considering desired profile tolerance. Adjacent iso-parametric tool paths are computed adaptively based on the scallop height constraint unlike the traditional iso-parametric approach. The path topology is post-processed to generate the part program for 5-axis CNC machine in ISO format. The system was rigorously tested for various case studies by comparing the results with the traditional 5-axis iso-parametric tool path strategy, iso-scallop strategy and iso-planar strategy of a commercial software. Our system was found to generate efficient tool paths in terms of part quality, productivity and memory storage compared to the conventional strategies.
Multi Response Optimization and Process Capability Analysis of Fused Filament Fabrication and Chemical Vapor Smoothing Operations for Rapid Casting of Biomedical Implants
Authors: Chohan Jasgurpreet Singh , Singh Rupinder , Boparai Kamaljit Singh 
Published: November 2019

Abstract
Despite numerous applications of Fused Filament Fabrication (FFF), the poor surface finish, being an inherent defect, is major obstruction against utilization of prototypes for rapid casting applications. The conventional finishing methods are effective, however, the dimensional stability is compromised which is completely unacceptable in case of biomedical implants as minute dimension variations may lead to post-operative complications. The paper explores possibilities to produce chemically finished plastic replicas of hip implant through FFF which can be further used as patterns for investment casting. An advanced post-processing technique i.e. Chemical Vapor Smoothing (CVS) is tested to improve surface characteristics and dimensional accuracy of ABS replicas. The repeatability and consistency of coupled CVS and FFF processes is tested through experimentation and statistical analysis in order to endorse an alternative process for mass production of biomedical implants. The Taguchi L18 DOE was used to perform experiments which measures impact of two input parameters of FFF i.e. orientation angle and density, and four parameters of Chemical Vapor Smoothing i.e. pre-cooling time, smoothing time, smoothing time and number of cycles. The multi response optimization technique was employed to acquire optimum set of parameters yielding best surface finish, dimensional accuracy and surface hardness and hence, the overall desirability of 0.7891 was achieved. The process capability and reliability was tested at optimum settings by manufacturing twenty replicas by measuring surface roughness, hardness and dimensional accuracy at different locations of parts. It was observed that values of Cp for all the response parameters was greater than 1.33 while Cpk was greater than 1. The analysis of histograms and capability indices reveal that the FFF-CVS process carried out at optimized conditions can be defined as “statistically controlled” for fabrication of ABS replicas for biomedical applications.
An Indirect Method for the Measurement of Micro-Milling Forces
Authors: Lu Xiaohong , Wang Furui , Yang Kun , Feng Yixuan , Liang Steven Y. 
Published: November 2019

Abstract
Nowadays, the measurement of micro-milling forces is mainly achieved by a force transducer. However, the frequency of force signal is high, due to the spindle super-speed, which leads to failure of the micro-milling forces measurement by using common force sensors. Additionally, micro force sensors with high-resolution and high sampling frequency are preferred, but they are often expensive. To determine the average micro-milling force with low cost and high precision, we propose an indirect method, by determining the power of the main transmission system of a micro-milling machine. First, the measurement system for the micro-milling machine tool power was introduced, and various sensors were used to measure the current and voltage respectively. Then, a high-frequency sampling system based on the Labview was developed to process the current and voltage signals, and to obtain the power data of the main transmission system. Through this process, the indirect measurement of micro-milling forces was achieved. Finally, we validated the effectiveness of the developed on-line measurement system and the proposed indirect measurement method for average micro-milling force by using experiments. The proposed method is practical and low-cost, and it can lay the foundation for further research on cutting energy consumption.
Modelling of Brushing Processes
Authors: Sommerfeld Christian , Uhlmann Eckart , Hoyer Anton 
Published: November 2019

Abstract
Brushes consist of a body with fixed highly flexible filaments and can be used for deburring and surface finishing operations. During the brushing process, axial and tangential deflections of the highly flexible filaments lead to an adaptation to the shape of the workpiece and interaction between the filaments. The described complex contact behavior has been insufficiently investigated so far.
For a better understanding of the contact between a brush and the workpiece surface, this paper presents a model based on physical principles. The model describes the dynamic behavior of a brush in contact with different workpiece geometries and consists of separate physical descriptions for the filaments of the brush, the workpiece surface and the occurring contacts. A description of a single filament is given by a multi-body system of rigid links. The rigid links are connected by joints which approximate the material behavior of the filaments. To approximate different geometries, the workpiece surface is specified by a polynomial. Contact can occur between the filaments and the workpiece surface as well as between the filaments. For the description of the occurring contacts, Hertz’s theory of elastic contact and Coulomb’s law of friction are used. The aforementioned physical descriptions are included in the Lagrange’s equations to obtain a system of equations of motion that calculates the deflection of the filaments of the brush and the acting forces during the contact with the workpiece surface. A numerical solution to the system of equations of motion was calculated by using experimentally determined material and contact properties of the filament. A comparison of the calculated forces with experimentally determined values shows good correlations for different workpiece surfaces and process parameters. In this context, the developed model calculates the progression and the maximum value of the acting contact forces. The results show a shorter contact length of the filament lc for a circular surface compared to a plane surface, and a rise of the maximum normal force Fn with the depth of cut ae. Furthermore, consideration of the filament interactions leads to a more accurate approximation of the brush-workpiece contact. Based on the findings, the developed model can be used to calculate predictions for different brushing processes which reduce the number of time-consuming preliminary tests for the process design.
Convolutional Neural Network Using Bayesian Optimization for Laser Welding Tailor Rolled Blanks Penetration Detection
Authors: Zhang Zhehao , Zhang Yi , Luo Feng , Li Jie , Lu Cheng , Zhao Yuze , Zhang Hang , Lu Ange 
Published: November 2019

Abstract
Convolutional neural network (CNN) is an efficient and robust method which can accurately detect the Tailor Rolled Blank laser welding pool penetration status. To select proper hyperparameters and optimization of CNN model are black box problem. In this paper, an innovative method based on CNN to identify the penetration status of the weld pool during laser welding was introduced. A coaxial monitoring platform is set up, as well as two-class, three-class and four-class datasets are created for training and validating the CNN. The Bayesian Optimization (BO) method is used to optimize hyper-parameters which are adopted for training CNN model, determine the best parameters of depth, initial learning rate, momentum and L2 regularization. The results show that using BO method leads to accuracy improvement compared with the CNN model trained from scratch with default hyper-parameters, hence it can effectively solve the problem that the hyper-parameters of CNN are difficult to adjust. Under various laser welding parameters, high-accuracy detection of penetration status can be acquired with the test accuracy of four-class reaching 95.2%, which slightly lower than the test accuracy of the three-class and two-class.