bluuebunny/crossref_metadata_2025_split huggingface dataset:
Meta-Data for In-Situ Monitoring of Laser Powder Bed Fusion Processes
Authors: Feng Shaw C. , Lu Yan , Jones Albert T. 
Published: January 2021

Abstract
Increasingly, a wide range of in-situ sensors are being instrumented on additive manufacturing (AM) machines. These sensors collect a variety of data that is used to monitor process performance and part quality. The amount, type, and speed of the collected data are unprecedented. Consequently, several data-related, standards issues are impeding the use of both data analytics and tool integration. Those issues include registration, curation, organization, storage, and management. This paper focuses on registration. It proposes the use of meta-data as a foundation for new interface and exchange standards. Standards that will facilitate the use of several types of in-situ sensors that monitor laser powder bed fusion (L-PBF) processes. The paper also includes an example data model that captures the properties and relationships among those meta-data. The data elements in that model provide industrial users with the capabilities and formats to capture, exchange, and share L-PBF process data.
Method for Obtaining Light Curing Printing Condition Based on New Theoretical Description
Authors: Li Yang , Yin Jun 
Published: January 2021

Abstract
Digital light processing (DLP) is widely used in tissue engineering in recent years. High resolution and high speed are the advantages of this printing method. The method of determining DLP process printing conditions by forming experiments is restricted by the formability of the material and it is difficult to apply to soft materials and materials that are not easily formed. In this study, through theoretical analysis that the concept of absorbances and gel point is introduced into the relationship between exposure time and forming thickness. This allows the forming conditions to be obtained by measurement of only physical quantities related to the nature of the material itself rather than through forming experiments. Which facilitates high-precision DLP printing of biomaterials.
Bio-Inspired Self-Folding of Shape Memory Polymer Sheets by Laser Rastering
Authors: Line Joshua , Abdulhafez Moataz , Bedewy Mostafa 
Published: January 2021

Abstract
Origami-based fabrication strategies open the door for developing new manufacturing processes capable of producing complex three-dimensional (3D) geometries from two-dimensional (2D) sheets. Nevertheless, for these methods to translate into scalable manufacturing processes, rapid techniques for creating controlled folds are needed. In this work, we propose a new approach for controlled self-folding of shape memory polymer sheets based on direct laser rastering. We demonstrate that rapidly moving a CO2 laser over pre-strained polystyrene sheets results in creating controlled folds along the laser path. Laser interaction with the polymer induces localized heating above the glass transition temperature with a temperature gradient across the thickness of the thin sheets. This gradient of temperature results in a gradient of shrinkage owing to the viscoelastic relaxation of the polymer, favoring folding towards the hotter side. We study the influence of laser power, rastering speed and number of passes on the fold angle. Moreover, we investigate process parameters that produce the highest quality folds with minimal undesired deformations. Our results show that we clean folds up to and exceeding 90°, which highlights the potential of our approach for creating lightweight 3D geometries with smooth surface finishes that are challenging to create using 3D printing methods. Hence, laser-induced self-folding of polymers is an inherently mass-customizable approach to manufacturing, especially when combined with cutting for integration of origami and kirigami.
Effect of Oil Flow Rate on Production Through-Tool Dual Channel MQL Drilling
Authors: Raval Jay K. , Stephenson David A. , Tai Bruce L. 
Published: January 2021

Abstract
Minimum quantity lubrication (MQL) drilling has been known for decades, but limited knowledge is available on two-channel through-tool MQL drilling due to the lack of accessibility to production systems. A common problem in MQL drilling is the absence of a rational approach to select the oil flow rate. The limited entry and exit area, and fixed energy available to the flow make the behavior complicated. This study leverages the capabilities in Ford’s manufacturing lab to abridge the research gap. Four different oil flow rates (0 ml/h, 15 ml/h, 30 ml/h and 60 ml/h) and two different drills (twist drill and straight drill) were used to find out the influence of oil flow rate on the cutting performance. Tool life, tool wear, cutting force and torque were monitored as the cutting performance indicators. It was concluded that, the common belief of higher oil flow rate providing better tool life, does not hold true for through-tool MQL drilling. The tool life for 30 ml/hr. oil flow rate appeared to be the highest compared to all the other cases for both the drills. Increasing the oil flow rate above 30 ml/hr. decreased the tool life. However, it is to be noted that the optimal oil flow rate values may be specific to the case.
Uncertainty Quantification in Metallic Additive Manufacturing Through Data-Driven Modelling Based on Multi-Scale Multi-Physics Models and Limited Experiment Data
Authors: Wang Zhuo , Jiang Chen , Horstemeyer Mark F. , Hu Zhen , Chen Lei 
Published: January 2021

Abstract
One of significant challenges in the metallic additive manufacturing (AM) is the presence of many sources of uncertainty that leads to variability in microstructure and properties of AM parts. Consequently, it is extremely challenging to repeat the manufacturing of a high-quality product in mass production. A trial-and-error approach usually needs to be employed to attain a product with high quality. To achieve a comprehensive uncertainty quantification (UQ) study of AM processes, we present a physics-informed data-driven modeling framework, in which multi-level data-driven surrogate models are constructed based on extensive computational data obtained by multi-scale multi-physical AM models. It starts with computationally inexpensive metamodels, followed by experimental calibration of as-built metamodels and then efficient UQ analysis of AM process. For illustration purpose, this study specifically uses the thermal level of AM process as an example, by choosing the temperature field and melt pool as quantity of interest. We have clearly showed the surrogate modeling in the presence of high-dimensional response (e.g. temperature field) during AM process, and illustrated the parameter calibration and model correction of an as-built surrogate model for reliable uncertainty quantification. The experimental calibration especially takes advantage of the high-quality AM benchmark data from National Institute of Standards and Technology (NIST). This study demonstrates the potential of the proposed data-driven UQ framework for efficiently investigating uncertainty propagation from process parameters to material microstructures, and then to macro-level mechanical properties through a combination of advanced AM multi-physics simulations, data-driven surrogate modeling and experimental calibration.
Multi-Robot Path Planning for Cooperative 3D Printing
Authors: Elagandula Saivipulteja , Poudel Laxmi , Sha Zhenghui , Zhou Wenchao 
Published: January 2021

Abstract
Cooperative 3D printing (C3DP) is an emerging technology that employs multiple mobile 3D printers to work together on printing the desired part cooperatively for better scalability and more flexibility. In our previous work, we developed a chunker, a geometric partition tool, to divide a part into smaller chunks and a computational framework to automatically generate schedules for collision-free printing. However, the framework can only generate print schedules for one print job. To schedule multiple robots to print multiple jobs, job assignment and path planning for robots to move between jobs also needs to be considered. In this paper, we apply the previously developed framework to generate print schedules for multiple jobs concurrently, and then developed a new approach to combine the independent print schedules with two heuristic methods: 1) Job by Job heuristics and 2) Row by Row heuristics. Once a single schedule is generated using one of the two heuristics, the conflict-based search algorithm is used to find the optimal path for robots to move from one print location to next. A case study is conducted where three different jobs are concurrently scheduled on a floor with four available printing robots. The results validate the utility of the proposed approach in generating viable print schedules with collision-free path planning.
Production of Medium-Scale Metal Additive Geometry With Hybrid Manufacturing Technology
Authors: Saleeby Kyle S. , Kurfess Tom , Feldhausen Tom , Love Lonnie 
Published: January 2021

Abstract
Oak Ridge National Lab’s (ORNL) Metal Big Area Additive Manufacturing (mBAAM) and Hybrid Manufacturing teams in the Manufacturing Demonstration Facility (MDF) was tasked with developing a part that exceeded 24 inches in length and 100lbs in weight on a laser hotwire welding system. The Mazak VC-500A/5X AM HWD hybrid 5-axis CNC laser hotwire system was leveraged to complete the task. The proposed geometry was manufactured with 410 stainless steel on AISI1018 steel substrate in just over 36 hours of build time. The proposed geometry was sliced and additively manufactured in three segments; the base, and two extrusions measuring 10 inches in length and 14 inches in length. Production of this component demonstrated hybrid capabilities requiring fixed 5-axis rotations to complete geometry that exceeded the defined build volume of the machine. Multiple repairs of the part were conducted in-situ leveraging the additive and subtractive capabilities of the Mazak hybrid system. Significant overbuilding challenges were encountered and manually addressed by leveraging subtractive capabilities of the hybrid system. Future processes are presented for development to reduce overheating of top layers. Final part measurements exceeded 107 lbs. in weight and 24.5 inches in length along the principal axis; the largest and heaviest part created by a hybrid system at the MDF.
Computational Study of Solvent Based Extrusion 3D Printing
Authors: Rath Usharani , Pandey Pulak Mohan 
Published: January 2021

Abstract
In the present work, authors have performed a finite element modelling and simulation of the solvent based extrusion 3D printing process. The present work can be considered as a pioneering work as there is no available literature on the modelling aspects of the solvent based extrusion 3D printing process. Two physics interfaces of a commercially available multiphysics modelling tool, such as computational fluid dynamics, and heat transfer along with a mathematical approach such as the Arbitrary Lagrangian-Eulerian method were integrated to simulate the aforementioned 3D printing process. The physical model consisted of a homogenous solution of polycaprolactone polymer and a volatile solvent namely methylene chloride. A two dimensional axisymmetric model was formulated to simulate the flow behaviour and evaporation behaviour of the polymer solution from the nozzle inlet to the platform on which the solution gets deposited. The velocity and temperature field of the polymer solution during the printing of one layer is analyzed. Furthermore, the evaporation mechanism followed by evaporative cooling of the deposited layer is also illustrated. It was observed that the flow velocity obtained was 16 mm/s at the nozzle exit for a feed rate of 40 mm/min at the nozzle inlet. The evaporative cooling of the Methylene chloride solvent resulted in the reduction in temperature with an evaporation rate of 1.74 g/m3s.
Digital Thread Enabled Manufacturing Automation Towards Mass Personalization
Authors: Ramesh Akhilnandh , Qin Zhaojun , Lu Yuqian 
Published: January 2021

Abstract
Manufacturing industries are moving towards mass personalization, which refers to the rapid production of individualized products, with large scale efficiencies. This shift from push-type mass customization to pull-type mass personalization will pose critical operational challenges to manufacturing businesses, with complexities ranging from effective requirements elicitation to design, manufacturing, commissioning and after-sales support. Aiming at addressing these challenges, a feasible operational framework for enabling efficient manufacturing automation for mass personalization is proposed in this paper. A key element of this operational framework is the Digital Thread, which streamlines information flow associated with design, manufacturing, maintenance and servicing of a personalized product, each of which are represented as Digital Twins. An As-Designed Digital Twin is created from the beginning of the product co-design process, which then evolves into the subsequent design and manufacturing process and systems resulting in As-Designed Digital Twin evolving to As-Planned Digital Twin and then to As-Built Digital Twin. The personalized product, after it’s commissioning and installation constitutes the As-Maintained Digital Twin of the product, which stores product data related to field performance. The data exchange and communications between these Digital Twins that reside in the various departments of the organization and the management systems create a seamless Digital Thread, capturing the lifecycle information of each personalized product. Personalized product is proposed to be developed through a self-organizing shopfloor, working on a multi-agent mechanism and controlled by a central agent control algorithm, which can coordinate and provide individualized process plans. The Digital Twins, interlinked by a Digital Thread and realized by a self-organizing shopfloor, thus result in increased level of automated control in engineering and manufacturing. To validate the feasibility of this proposed framework, we tested the information flow in the Digital Thread with a case study in the construction industry. Finally the challenges faced by such an automation framework and the area of future work are also discussed.
DEM As a Tool for Optimizing Powder Metallurgy Processes
Authors: Bobba Dhanooj , Tabei Seyed A. , Pando Miguel , Cherukuri Harish P. 
Published: January 2021

Abstract
Powder metallurgy processes such as cold and hot isostatic pressing and powder-bed fusion additive manufacturing are being extensively used to produce metallic and ceramic parts due to several significant benefits these techniques provide, including reduction in wasted material, more precise handling of complicated shapes and requirement of fewer steps to achieve the final geometry and tolerance. Often the quality of the products obtained through these processes is greatly affected by the homogeneity of the feedstock powders used. But the presence of particles of different shapes and sizes in the powders may lead to inhomogeneous distribution which give rise to segregation of particles. In this study, discrete element method was used to investigate the degrees of segregation of a binary mixture in a cylindrical die with sharp stepped features. To study the effect of the composition of the binary mixture, three different mass ratios are considered in the study. In addition, vibrations are applied to the cylindrical die to increase the compaction of the particles in the stepped regions of the die. The effects of vibration amplitude and vibration frequency on the size segregation of the particles in the die are studied. At higher amplitude location dependent segregation patterns were observed.
Robot-to-Robot Collaboration for Fixtureless Assembly: Challenges and Opportunities in the Automotive Industry
Authors: Saez Miguel , Spicer Patrick 
Published: January 2021

Abstract
This paper summarizes the latest developments in robot-to-robot collaboration for fixtureless assembly. In addition, some of the technology challenges and potential applications in the automotive industry are described. Fixtureless assembly systems have been developed to increase the flexibility of body shop systems. These systems eliminate the need for using dedicated fixtures to set the geometry of body assemblies. A key technology enabler for a fixtureless assembly process is robot-to-robot collaboration based on perception and advanced control solutions. In a fixtureless assembly process, robots move and hold parts together in the geometry setting pose for assembly instead of using fixtures. In this paper the technology requirements and performance results of a production-relevant demonstration cell are presented. Moreover, the technology and performance requirements to use the fixtureless approach in other automotive assembly applications are reviewed. Results show that dimensional performance from the newly developed fixtureless process was acceptable and within the tolerance range. Initial cost estimate show that a fixtureless process offers a significant cost benefit over a conventional assembly process, especially for a multi-style production line or when new styles are frequently introduced.
Online Quality Monitoring of Plastic Parts Using Real-Time Data From an Injection Molding Machine
Authors: Loftis Jonathan , Farahani Saeed , Pilla Srikanth 
Published: January 2021

Abstract
Every day-increasing connectivity and access to data can provide valuable insight to the plastics industry. While the amount of accessible data has been increasing, the means to process and store it efficiently while squeezing valuable process information out of it has not been prioritized. The increase in connectivity has led to much of this data being stored and used in cloud computing systems which can be both monetarily and computationally expensive. Motivated by this fact, the feasibility of using real-time data directly captured from injection molding machine is investigated in terms of their capabilities for online quality monitoring. Using the built-in sensors that are usually existed in the standard injection molding machines (barrel pressure, screw position, and clamp force) and a dimensional reduction method, models are derived to predict quality of injection molded parts (Weight, Thickness, and Diameter). The developed models show high predictive capability with R2 values ranging from 0.89–0.97. Moreover, the combination of the proposed feature extraction method and implementation of Partial Least Squares Regression (PLS) demonstrates that most of the computing for automatic quality control can be done on local edge computing hardware with a significantly summarized data, and only control commands need to be sent through the cloud.
Powder Thermal Conductivity Measurements in L-PBF Using Powder-Included Build Specimens: Internal Geometry Effect
Authors: Zhang Shanshan , Lane Brandon , Chou Kevin 
Published: January 2021

Abstract
This study investigates the thermal conductivity of 17-4PH stainless steel powder that was encapsulated within specimens with different internal geometries in laser powder bed fusion (L-PBF) additive manufacturing (AM). The objective is to evaluate the effect of the internal geometry of the specimens on the measurement of the powder thermal conductivity and to compare the thermal properties amongst the 17-4PH and two additional powder materials used in L-PBF. Continued from the previous work [1], three new cone configurations in the hollow specimens were designed and fabricated in an L-PBF system. The thermal conductivity of the internal powder was indirectly measured using an experimental-numerical approach, combined with laser-flash testing, finite element (FE) heat transfer modeling and multivariate inverse method. The results reveal that the thermal conductivity of 17-4PH powder ranges from 0.67 W/(m·K) to 1.34 W/(m·K) at 100 °C to 500 °C, and varies with the internal geometry of the specimens. In addition, the measurement of the hollow specimen with a convex cone seems to be a more reliable evaluation. Further, the thermal conductivity ratio of the powder to the solid counterpart of 17-4PH approximately ranges from 3.9 % to 5.5 % at tested temperatures, which is similar to the results obtained from the nickel-based super alloy 625 (IN625) and Ti-6Al-4V (Ti64) powders measured in a previous study.
The Environmental Impacts of Metal Powder Bed Additive Manufacturing
Authors: Liao Jiankan , Cooper Daniel R. 
Published: January 2021

Abstract
Additive manufacturing (AM) is widely recognized as a critical pillar of advanced manufacturing and is moving from the design shop to the factory floor. As AM processes become more popular, it is paramount that engineers and policymakers understand and then reduce their environmental impacts. This article structures the current work on the environmental impacts of metal powder bed processes: selective laser melting (SLM), direct metal laser sintering (DMLS), electron beam melting (EBM), and binder jetting (BJ). We review the potential benefits and pitfalls of AM in each phase of a part’s lifecycle and in different application domains (e.g., remanufacturing, hybrid manufacturing etc.). We highlight critical uncertainties and future research directions throughout.
The environmental impacts of AM are sensitive to the specific production and use-phase context; however, several broad lessons can be extracted from the literature. Unlike in conventional manufacturing, powder bed production impacts are dominated by the generation of the direct energy (electricity) required to operate the AM machines. Combined with a more energy-intensive feedstock (metal powder) this means that powder bed production impacts are higher than in conventional manufacturing unless production volumes are very small (saving tool production impacts) and/or there are significant material savings through part light weighting or improved buy-to-fly ratios.
Experimental Investigation of Effects of Needle Diameter on Algae Cell Growth in Green Bioprinting
Authors: Thakare Ketan , Jerpseth Laura , Qin Hongmin , Pei Zhijian 
Published: January 2021

Abstract
Bioprinting can fabricate structures based on layer-by-layer deposition of biomaterials. Bioprinting of animal cells shows great promise for tissue regeneration and tissue research. Plant cells can also be used when synthesizing bioink. Bioprinting with bioink that has been synthesized with plant cells instead of animal cells is called green bioprinting. Green bioprinting offers greater spatial control over the growth of plant matter by immobilizing cells. It is known that certain variables affect the growth and propagation (cellular reproduction) of cells in green bioprinted constructs post bioprinting. These variables include accessibility to components required for photosynthesis, such as nutrients, moisture and light. However, multiple parameters can also vary during the extrusion of bioink in the bioprinting process, including extrusion pressure, extrusion temperature, needle diameter, and printing speed. This paper reports a study to examine the effects of needle diameter during bioprinting on the growth of Chlamydomonas reinhardtii algae cells in green bioprinted constructs. Growth of Chlamydomonas reinhardtii algae cells was quantified by measuring cell count. The constructs were bioprinted with needle diameters of 22 gauge, 25 gauge, and 27 gauge. It was found that decreasing needle diameter was correlated with decreased cell count on the second and fifth days post bioprinting. Furthermore, the magnitude of cell count increase between the second and the fifth days post bioprinting decreased with decreasing needle diameter. Future research is needed to examine the effects of other printing parameters on cell growth.