bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Effect of Carbon Fiber Types on Tool Wear During Edge Trimming of 0°, 45°, 90°, and 135° Carbon Fiber Reinforced Plastic Laminates
Authors: Khawarizmi Ryan , Kwon Patrick , Sayem Bin Abdullah Mohammad , Han Yinyin , Kim Dave 
Published: August 2021

Abstract
Although carbon fiber reinforced plastics (CFRP) materials are widely used in high-strength and low-weight applications such as aerospace, defense, and automotive industries, they are one of the difficult-to-machine materials due to extensive tool wear. This paper investigates the impact of carbon fiber types on tool wear in edge-trimming CFRPs, each with particular ply angle of 0°, 45°, 90°, or 135°, using uncoated tungsten carbide endmills at a high spindle speed of 6000 rpm and a constant feed of 0.0508 mm/rev. Three distinct types of carbon fiber tows, including T300 as standard modulus (SM), IM-7 as intermediate modulus (IM), and K13312 as high modulus (HM), have been vacuum infused into CFRP laminates and edge-trimmed to investigate wear characteristics. Three wear criteria measured are flank wear, edge rounding radii, and worn area. The results show that tool wear is influenced by carbon fiber properties, such as fiber tensile strength, tensile modulus, and fiber microstructure. Overall, Intermediate modulus carbon fibers with the highest tensile strength produced the largest tool wear due to brushing effects by abrasive carbon fibers. Out of four fiber directions, the largest tool wear was obtained from the 45° angle while the lowest tool wear occurred in the 0° angle.
Emissions of Volatile Organic Compounds From 4D Printing and Associated Control Strategies Towards Workplace Safety
Authors: Han Muyue , Zhao Jing , Li Lin 
Published: August 2021

Abstract
The integration of additive manufacturing technologies with stimuli-responsive shape memory materials allows the dynamic self-adaptation of fabricated parts upon exposure to external stimulations. The additional dimension of time inspires the concept of four-dimensional printing technology. However, the emerging feedstock materials and the employment of external stimuli in 4D printing are also associated with new workplace hazards and occupational health concerns. Current evaluation studies on additive manufacturing are mainly focused on the safety and health effects that originated from the part production phase and cannot be directly applied in 4D printing processes. In this study, the emissions of volatile organic compounds from stereolithography-based 4D printing process with thermo-responsive materials are targeted as the potential safety concern. Real-time total volatile organic compound monitoring is conducted during various production phases to align the emission sources with critical operating activities, including both operator interventions and machine operations. Comparative experiments are performed to evaluate the effectiveness of proposed emission control strategies. In particular, alterations in operation procedures such as stirring speed in material mixing and post-printing stimulation method can contribute positively to air emission control during manual operations. In addition, the installation of activated carbon fiber filters inside the machine build chamber can lead to a significant reduction of air emissions during part fabrication with an overall total volatile organic compound concentration reduction of 58.91%.
Manufacturing of Biodegradable Intramedullary Nail With High Strength
Authors: Shao Huifeng , Jing Zhuoluo , Zhou Rougang , Nian Zhiheng , Liu Haiqiang , Gong Youping , He Yong 
Published: August 2021

Abstract
Intramedullary nails (INs) have significant advantages in rigid fracture fixation. Conventional metal INs for long bone fracture fixation often lead to delay union or even nonunion healing due to their stress shielding effect and lack of biological activity. Besides, undegradable metals require a second surgery to remove them, which will not only impose a potential risk to the patient but also cause additional costs. Manufacturing high-strength biodegradable INs (BINs) is still a challenge. Here, an entirely new type of high-strength bioactive magnesium-containing silicate (CSi-Mg) BIN is manufactured by using casting, freeze drying and sintering technique. It has extremely high bending strength (&gt; 41 MPa) and stable internal and external structure. We have systematically studied the influence of parameters such as the paste component, freeze drying process, and sintering process on the mechanical strength involved in the manufacturing process. According to our manufacturing method, a wide range of inorganic ceramic implants and BINs with different sizes can also be fabricated. The CSi-Mg BIN also has good bioactivity and biodegradation property. This bioceramic BIN and manufacturing process are expected to be applied to a variety of orthopedic medical devices. This novel bioactive BIN is expected to replace the traditional metal IN and become a more effective way of treating fracture.
Design of Knowledge Graph in Manufacturing Services Discovery
Authors: Li Yunqing , Raman Shivakumar , Cohen Paul , Starly Binil 
Published: August 2021

Abstract
Knowledge graph networks powering web search and chatbot agents have shown immense popularity. This paper discusses the first steps towards building a knowledge graph for manufacturing services discoverability. Due to the lack of a unified widely adopted schema for structured data in the manufacturing services domain as well as the limitations of existing relational database schemas to represent manufacturing service definitions, there does not exist a unified schema that connects manufacturing resources service descriptions and actual manufacturing service business entities. This gap severely limits the automated discoverability of manufacturing service business organizations. This paper designs a knowledge graph covering over 8,000+ manufacturers, the manufacturing services they provide and corresponding linkage with manufacturing service definitions available from Wikidata. In addition, this work also proposes extensions to Schema.org to assist small business manufacturers to contain embedded search engine optimization (SEO) tags for search and discovery through web search engines. Such vocabulary extensions are critical to the rapid identification and real-time capability assessment particularly when the service providers themselves are responsible for updating tags. A wider scale enhancement of manufacturing specific vocabulary extensions to schema.org can tremendously benefit small and medium scale manufacturers. This paper concludes with the additional work that must be done for a comprehensive addition to manufacturing service graph that spans the entire manufacturing knowledge base.
Prediction of Thermal Conditions of DED With FEA Metal Additive Simulation
Authors: Heinrich Lauren , Feldhausen Thomas , Saleeby Kyle S. , Saldana Christopher , Kurfess Thomas R. 
Published: August 2021

Abstract
This paper presents the integration of wire-arc additive manufacturing (WAAM) using Gas Metal Arc Welding (GMAW) into a machine tool to create a retrofit hybrid computer numeric control (CNC) machine tool. GMAW, along with other direct energy deposition systems, has the capacity to deposit material faster than the excess thermal energy can dissipate. This results in the need to allow the part to cool between consecutive layers, which is the most time-consuming part of the additive process. Finite element analysis (FEA) was used in conjunction with monitored build plate surface temperatures during deposition samples to improve adequate dwell time prediction and to develop a cooling system. A deposition was completed where no dwell time was used and the build plate along with the machine table temperatures were monitored. A second deposition was completed where only one bead was deposited and the traverse speed was increased. The GMAW welder was mounted on a 3-axis CNC machine where two square deposition samples were completed. A FEA model was designed and verified using the monitored samples. The model will be used to determine improved depositions speeds and whether forced cooling would allow for an increased deposition rate without structural failure. It was determined the FEA software can be used to accurately model and predict the thermal response of WAAM AM components.
Functional Fiber Junctions for Circuit Routing in E-Textiles: Deterministic Alignment of MEMS Layout With Fabric Structure
Authors: Challa Sushmita , Islam M. Shafquatul , Wei Danming , Beharic Jasmin , Popa Dan O. , Harnett C. K. 
Published: August 2021

Abstract
Fabrics and fibrous materials offer a soft, porous, and flexible substrate for microelectromechanical systems (MEMS) packaging in breathable, wearable formats that allow airflow. Device-on-fiber systems require developments in the field of E-Textiles including smart fibers, functional fiber intersections, textile circuit routing, and alignment methods that adapt to irregular materials. In this paper, we demonstrate a MEMS-on-fabric layout workflow that obtains fiber intersection locations from high-resolution fabric images. We implement an image processing algorithm to drive the MEMS layout software, creating an individualized MEMS “gripper” layout designed to grasp fibers on a specific fabric substrate during a wafer-to-fabric parallel transfer step. The efficiency of the algorithm in terms of a number of intersections identified on the complete image is analyzed. The specifications of the MEMS layout design such as the length of the MEMS gripper, spatial distribution, and orientation are derivable from the MATLAB routine implemented on the image. Furthermore, the alignment procedure, tolerance, and hardware setup for the alignment method of the framed sample fabric to the wafer processed using the custom gripper layout are discussed along with the challenges of the release of MEMS devices from the Si substrate to the fabric substrate.
Auto-Calibration for Vision-Based 6-D Sensing System to Support Monitoring and Health Management for Industrial Robots
Authors: Qiao Guixiu , Li Guangkun 
Published: August 2021

Abstract
Industrial robots play important roles in manufacturing automation for smart manufacturing. Some high-precision applications, for example, robot drilling, robot machining, robot high-precision assembly, and robot inspection, require higher robot accuracy compared with traditional part handling operations. The monitoring and assessment of robot accuracy degradation become critical for these applications. A novel vision-based sensing system for 6-D measurement (six-dimensional x, y, z, yaw, pitch, and roll) is developed at the National Institute of Standards and Technology (NIST) to measure the dynamic high accuracy movement of a robot arm. The measured 6-D information is used for robot accuracy degradation assessment and improvement. This paper presents an automatic calibration method for a vision-based 6-D sensing system. The stereo calibration is separated from the distortion calibration to speed up the on-site adjustment. Optimization algorithms are developed to achieve high calibration accuracy. The vision-based 6-D sensing system is used on a Universal Robots (UR5) to demonstrate the feasibility of using the system to assess the robot’s accuracy degradation.
Microscale 3D Printed Patterns for Nanoscale Particle Assembly
Authors: Jambhulkar Sayli , Xu Weiheng , Zhu Yuxiang , Ravichandran Dharneedar , Song Kenan 
Published: August 2021

Abstract
Directed particle assembly has broad applications in sensors, actuators, microelectronics, robotics, and the biomedical area. Currently available methods include external fields such as electrical or magnetic fields, surface treatment on substrates, and DNA-assisted templates. However, these methods are most efficient at the nanoscale and would lose their efficiency and scalability above microscales. We reported in this research the uses of the 3D printed surface to direct the assembly of nanoparticles. We used carbon nanofibers (CNFs) as an example to show the long-range orders after dipping the 3D printed substrates in CNFs suspensions. The anchoring of CNFs at the solid-liquid-air contact lines will initiate the assembly procedure and further induced the neighboring CNFs because of the van der Waals forces. As a result, the CNFs formed well-regulated bands with controlled spacing and close-packing. These assembled CNFs were demonstrated in sensing applications. A gauge factor regulated the methanol at different concentrations and temperatures to pass the sensor, with the device resistivity change. In this way, the sensitivity as a function of analyte concentration and temperatures was obtained. This research studied nanoparticles’ microscale assembly based on a simple 3D printing surface and shed light on a new hybrid manufacturing for nanoparticle assembly.
SQL and NoSQL Databases for Cyber Physical Production Systems in Internet of Things for Manufacturing (IoTfM)
Authors: Gamero David , Dugenske Andrew , Kurfess Thomas , Saldana Christopher , Fu Katherine 
Published: August 2021

Abstract
In this paper, the design and performance differences between Relational Database Management Systems (RDBMS) and NoSQL Database Systems are examined, with attention to their applicability for real-world Internet of Things for manufacturing (IoTfM) data. While previous work has extensively compared SQL and NoSQL for both generalized and IoT uses, this work specifically examines the tradeoffs and performance differences for manufacturing applications by using a high-fidelity data set collected from a large US manufacturing firm. Growing an IoT system beyond the pilot stage requires scalable data storage; this work seeks to determine the impact of selected database systems on data write performance at scale. Payload size and message frequency were used as the primary characteristics to maintain model fidelity in simulated clients. As the number of simulated asset clients grow, the data write latency was calculated to determine how both database systems’ performance were affected. To isolate the RDBMS and NoSQL differences, a cloud environment was created using Amazon Web Services (AWS) with two identical data ingestion pipelines: writing data to an RDMBS (1) using AWS Aurora MySQL, and (2) using AWS DynamoDB NoSQL. The findings may provide guidance for further experimentation in large-scale manufacturing IoT implementations.
Forecasting Repair and Maintenance Services of Medical Devices Using Support Vector Machine
Authors: Liao Hao-yu , Cade Willie , Behdad Sara 
Published: August 2021

Abstract
Accurate prediction of product failures and the need for repair services become critical for various reasons, including understanding the warranty performance of manufacturers, defining cost-efficient repair strategies, and compliance with safety standards. The purpose of this study is to use machine learning tools to analyze several parameters crucial for achieving a robust repair service system, including the number of repairs, the time of the next repair ticket or product failure, and the time to repair. A large dataset of over 530,000 repairs and maintenance of medical devices has been investigated by employing the Support Vector Machine (SVM) tool. SVM with four kernel functions is used to forecast the timing of the next failure or repair request in the system for two different products and two different failure types, namely random failure and physical damage. A frequency analysis is also conducted to explore the product quality level based on product failure and the time to repair it. Besides, the best probability distributions are fitted for the number of failures, the time between failures, and the time to repair. The results reveal the value of data analytics and machine learning tools in analyzing post-market product performance and the cost of repair and maintenance operations.
Impact of Process Parameters on the Tensile Properties of DLP Additively Manufactured ELAST-BLK 10 UV-Curable Elastomer
Authors: Meem Asma Ul Hosna , Rudolph Kyle , Cox Allyson , Andwan Austin , Osborn Timothy , Lowe Robert 
Published: August 2021

Abstract
Digital light processing (DLP) is an emerging vatphotopolymerization-based 3D-printing technology where full layers of photosensitive resin are irradiated and cured with projected ultraviolet (UV) light to create a three-dimensional part layer-by-layer. Recent breakthroughs in polymer chemistry have led to a growing number of UV-curable elastomeric photoresins developed exclusively for vat photopolymerization additive manufacturing (AM). Coupled with the practical manufacturing advantages of DLP AM (e.g., industry-leading print speeds and sub-micron-level print resolution), these novel elastomeric photoresins are compelling candidates for emerging applications requiring extreme flexibility, stretchability, conformability, and mechanically-tunable stiffness (e.g., soft robotic actuators and stretchable electronics). To advance the role of DLP AM in these novel and promising technological spaces, a fundamental understanding of the impact of DLP manufacturing process parameters on mechanical properties is requisite. This paper highlights our recent efforts to explore the process-property relationship for ELAST-BLK 10, a new commercially-available UV-curable elastomer for DLP AM. A full factorial design of experiments is used to investigate the effect of build orientation and layer thickness on the quasi-static tensile properties (i.e., small-strain elastic modulus, ultimate tensile strength, and elongation at fracture) of ELAST-BLK 10. Statistical results, based on a general linear model via ANOVA methods, indicate that specimens with a flat build orientation exhibit the highest elastic modulus, ultimate tensile strength, and elongation at fracture, likely due to a larger surface area that enhances crosslink density during the curing process. Several popular hyperelastic constitutive models (e.g., Mooney-Rivlin, Yeoh, and Gent) are calibrated to our quasi-static tensile data to facilitate component-level predictive analyses (e.g., finite-element modeling) of soft robotic actuators and other emerging soft-matter applications.
The Cooling Rate Effect on Graphene Synthesis Using Low Pressure Chemical Vapor Deposition
Authors: Lee Byoungdo , Chu Weishen , Li Wei 
Published: August 2021

Abstract
Low-pressure chemical vapor deposition (LPCVD) is the most efficient method to synthesize large-scale, high-quality graphene for many potential applications such as flexible electronics, solar cells, and separation membranes. The quality of LPCVD is affected by process variables including methane/hydrogen (CH4/H2) ratio, time, pressure, temperature, and cooling rate. The cooling rate has been recognized as one of the most important process variables affecting the amount of carbon source, nucleation, reaction time, and thus the quality of the LPCVD. In this research, we investigate the effect of cooling rate on the quality of graphene synthesize by changing the cooling rate and the gas feeding time. Graphene coverage is measured by Raman mapping. It is found that fast cooling rate leads to decreased carbon source reaction time, which in turn results in higher coverage by monolayer graphene. The temperature-dependent gas feeding time corresponding to different cooling rates can be used to properly supply the carbon source onto the copper surface, also leading to a higher graphene coverage.
A Deep Neural Network Model for Learning Runtime Frequency Response Function Using Sensor Measurements
Authors: Qu Yongzhi , Vogl Gregory W. , Wang Zechao 
Published: August 2021

Abstract
The frequency response function (FRF), defined as the ratio between the Fourier transform of the time-domain output and the Fourier transform of the time-domain input, is a common tool to analyze the relationships between inputs and outputs of a mechanical system. Learning the FRF for mechanical systems can facilitate system identification, condition-based health monitoring, and improve performance metrics, by providing an input-output model that describes the system dynamics. Existing FRF identification assumes there is a one-to-one mapping between each input frequency component and output frequency component. However, during dynamic operations, the FRF can present complex dependencies with frequency cross-correlations due to modulation effects, nonlinearities, and mechanical noise. Furthermore, existing FRFs assume linearity between input-output spectrums with varying mechanical loads, while in practice FRFs can depend on the operating conditions and show high nonlinearities. Outputs of existing neural networks are typically low-dimensional labels rather than real-time high-dimensional measurements. This paper proposes a vector regression method based on deep neural networks for the learning of runtime FRFs from measurement data under different operating conditions. More specifically, a neural network based on an encoder-decoder with a symmetric compression structure is proposed. The deep encoder-decoder network features simultaneous learning of the regression relationship between input and output embeddings, as well as a discriminative model for output spectrum classification under different operating conditions. The learning model is validated using experimental data from a high-pressure hydraulic test rig. The results show that the proposed model can learn the FRF between sensor measurements under different operating conditions with high accuracy and denoising capability. The learned FRF model provides an estimation for sensor measurements when a physical sensor is not feasible and can be used for operating condition recognition.
Solid Transfer of Large Particles by Dipping in a Heterogeneous Mixture
Authors: Khoda Bashir , Nazmul Ahsan A. M. M. , Shovon S. M. Naser 
Published: August 2021

Abstract
Solid transfer technology from mixtures is gaining ever-increasing attention from both materials scientists and production engineers due to their high potential in near net shape production of cost-effective engineering components. Dip coating, a wet deposition method is an effective and straightforward way of thin-film/layers formation. It is extensively used as a coating method due to its simplicity, low cost, and reasonable control over the thickness. The dipping mixture can be homogeneous, composite, hybrid, or heterogeneous. The mixtures are often embedded with inorganic fillers, nanoparticles, or clusters (d &lt; 30 nm) that produce a thin film ranging from nm to couple microns. An increase in the volume of solid transfer by the dipping process can open-up a novel technique for the 3D near-net-shape production process via sintering, robocasting or additive manufacturing, and material joining. Adding larger inorganic particle size (&gt; 1μm) and/or by adding higher solid fraction will increase the solid transfer but may result in a multi-phase heterogeneous mixture or slurry.
In this work, the physical mechanism of an increased volume of solid transfer with a larger particle size (&gt; 5 μm) is investigated. The metallic particles are spherical in shape with an average diameter of 5.69 μm is considered as the coating material. Polymer-based glue and evaporating solvent are mixed to construct the liquid carrier system (LCS) for large inorganic hard particles. Moderate volume fraction (VF) of inorganic particles (20% &lt; ϕp &lt; 50%) are added into the LCS solution as solid loading. Cylindrical AISI 304 steel wire with dia 0.81 mm is used as the substrate for dipping and coating. The coating thickness (CT) and the surface packing coverage by the particles are measured in our lab. The results presented the influence of volume fraction of inorganic particle and glue composition on the solid transfer from the heterogeneous mixture.