bluuebunny/crossref_metadata_2025_split huggingface dataset:
Failure Initiation Point Control in Blown Powder Directed Energy Deposition
Authors: Burl Alan , Heinrich Lauren , Saldaña Christopher , Feldhausen Thomas 
Published: August 2024

Abstract
Additive manufacturing defects are often cited as being detrimental to the final product and significant effort has been expended to mitigate said defects. However, in applications where failure of the component is a necessity to prevent undo wear and tear, these defects may be leveraged as a mechanism to control part failure location. This work explored the use of varying deposition parameters to induce failure of a component at a specified location. A blown powder directed energy deposition system coupled with a five-axis machine tool was utilized for the manufacture of test specimens. A thin section of a part was intentionally deposited with parameters, namely laser power, outside those known to produce dense components in an attempt to introduce lack of fusion porosity. Porosity within the band was shown to vary with changing laser power. Hardness testing of the components showed a vertical gradient along the build direction. A slight increase in hardness of 25 HV is noted at 2 mm above the intended failure region. Micrographs of the failure region revealed a distinct boundary between the failure region and the nominally correct regions. This work has shown that porosity location can be controlled in a directed energy deposition process.
A Simulated Evaluation of Powder Flowability Through a Partially Obstructed Consumable in Blown Powder Directed Energy Deposition Systems
Authors: DeWitte Lisa , Fu Katherine 
Published: August 2024

Abstract
In the interest of continued industrialization of metal additive manufacturing in modern production environments, cost is often referenced as a primary deterrent to new adopters. Conventional economic models for additive systems, processes, and supply chains often focus on specific process applications with little generalizability, or they neglect significant costs associated with production such as machine maintenance and consumable part replacement. Compounding the latter issue are substantial knowledge gaps in consumable part wear characterization for additive and other convergent manufacturing systems. In coaxial blown powder directed energy deposition systems, gas atomized metal powder is wasted during material deposition at a rate that is partly dependent on present wear phenomena in a consumable nozzle housed in the cladding head assembly. The price and lead time required to replace the nozzle incentivizes its reuse even when visibly worn. Often this initiates a process quality decline in the form of underbuilt geometry and internal defects due to losses in powder catchment efficiency. While depositing H13 steel using a hybrid manufacturing machine tool equipped with such a deposition system, a unique partial clog with a bridge-like structure formed at the consumable nozzle exit when supporting argon gas flows failed mid-process. To further understand coaxial multi-phase powder flow in the event of support gas failure, a computational fluid dynamics simulation is tailored to relevant process parameters, H13 powder material profile, and machine operator observations collected after the incident. The resulting differences in powder flow compared to control gas flow parameters is presented and discussed. The powder flowability and performance of the clogged nozzle is then assessed by using an optical profilometer to extract the profile of the clog and recreate the clog geometry within the simulation environment. In past work this simulation has been experimentally validated for a 316L steel powder material profile and used specifically for analyzing powder stream geometry and catchment efficiency. After the initial powder flow characterization, the clog is removed, and the nozzle is reprofiled. After removing the obstructing clog, the newly unobstructed nozzle geometry, the original off the shelf nozzle geometry, and additional nozzle profiles exploring different consumable refurbishment strategies are reevaluated in the simulation. Powder catchment efficiency for all variant nozzle geometries and relevant flow variables are compared and discussed, along with potential mitigation strategies for optimizing powder flowability with worn consumables. This work expands on the known morphology of blown powder obstructions and wear defects present in consumable coaxial nozzles while discussing pragmatic simulation driven responses to unanticipated subsystem failure in hybrid manufacturing machining platforms.
Online Monitoring of Infrared Pyrometer Captured Thermal Signature for Prediction of Iron Dilution and its Effect on Corrosion Properties of Laser Deposited Inconel 625 Coating on SS 304L
Authors: Misra Saurav , Mohanty Ipsita , Kumar Cheruvu Siva , Saha Partha 
Published: August 2024

Abstract
The current study deals with understanding the implication of iron (Fe) dilution from underlying stainless steel (SS) substrate (SS304L) on corrosion properties of laser deposited Inconel 625 through insitu/online monitoring of thermal signatures captured by infrared (IR) pyrometer. Online thermal signature monitoring helps to detect any changes in temperature during deposition. The thermal signature showed an early initiation of solidification at higher temperatures with the increase in laser input power from 600 W to 1200 W, thus indicating the possibility of an early nucleation. Higher fluence led to increased penetration depth and meltpool life coupled with a greater extent of Marangoni convection, thus resulting in higher % Fe dilution into the coating. This increase in Fe dilution from the SS 304L substrate might have caused early nucleation due to the formation of higher melting point iron carbides in the deposited tracks. The elemental dispersive X-ray spectroscopy (EDS) and X-Ray diffraction (XRD) data reveal an increment in % Fe dilution with the increase in laser fluence. This increase in Fe dilution might have an impact on the corrosion property of deposited Inconel 625 on SS 304L. Electrochemical corrosion test was performed in a simulated seawater medium to understand the implications of iron dilution on the corrosion property of deposited Inconel 625. Potentiodynamic polarization (PDP) test showed lower Ecorr values for samples deposited under 1200 W as compared to 600 W. Passivation at lower currents was seen for deposit conditions of 600 W and 800 W compared to 1200 W. Nyquist plot obtained from Electrochemical Impedence Spectroscopy (EIS) test showed lower radius of curve at 1200 W laser power compared to 600 W. A decrease in corrosion resistance under higher laser power was also evident from the Bode impedance and phase plots. A decrease in corrosion resistance property with the increase in Fe dilution at higher laser fluence was thus evident from PDP and EIS analysis results. Therefore, an increase in solidification shelf temperature detected through IR pyrometer due to Fe dilution could be seen as an indication of a decrease in the corrosion property of the deposit and thus might be utilized for predicting the corrosion behavior directly from insitu thermal signals and also be helpful in finding the optimum input processing conditions for laser cladding.
Brief Paper: A Theoretical Cutting Force Model Coupled With Surface Topography and Vibrations in Milling of Laser Cladding Layers
Authors: Shi Daming , Ma Senlin , Huang Tao , Zhang Xiaoming 
Published: August 2024

Abstract
The milling process is crucial for enhancing the surface quality and accuracy of laser cladding layers in blade repair. However, the cutting force characteristics significantly differ due to the coarse profile of the laser cladding layer, leading to variable tool-workpiece engagement and dynamic responses during the machining process. In this research, we introduce a novel cutting force model formulated through a Fourier series expression, incorporating coefficients that account for vibrations caused by the uneven surface of the laser cladding. This advanced model aims to provide an analytical solution for force-induced vibrations and to quantify the impact of the coarse profile on these dynamics. To validate the efficacy of our proposed model, we conducted milling simulations and compared the results with those obtained from milling normal surfaces. Our findings indicate that the topography of laser cladding layers results in a lower peak cutting force and alters the natural frequencies of the system. This research not only validates the superiority of our model but also offers significant insights into optimizing the milling process for laser cladding blade repair, promising enhanced performance and reliability.
Enhancing Low-Carbon Routing Through Additive Manufacturing of Reconfigurable Products: An Exploratory Study
Authors: Yun Lingxiang , Han Muyue , Wang Di 
Published: August 2024

Abstract
The reduction and neutrality of carbon emissions have emerged as major concerns spanning various stages of product life cycles. Additive manufacturing (AM), with its process efficiency and decentralized production characteristics, has been seen as an enabler for more sustainable practices in both product production and distribution. Recent advancements in AM of shape memory materials offer promising prospects for the production of smart products that can re-configure and re-shape in response to external stimuli, such as heat and electricity. This new manufacturing paradigm holds the potential to enable green product logistics by facilitating easier transportation and storage of compact or coalesced products, which can later revert to their desired shapes. Nonetheless, it should be noted that these benefits are accompanied by trade-offs stemming from the additional carbon emissions generated during the stimuli-triggered reshaping. In this research, an integrated vehicle assignment and routing approach is demonstrated for logistics distribution networks that involve reconfigurable products made by AM, considering stochastic travel and customer demand. Case studies were performed on the distribution networks of reconfigurable lamp shades, and the results indicate the effectiveness of the demonstrated method in determining the best product-to-vehicle assignments and sequence of trips with minimized carbon emissions.
Direct Ink Writing of SiC-Fe3O4-Polymer Nanocomposites for Magnetic Humidity Sensors: Rheology Characterization
Authors: Khecho Anasheh , Hewlin Rodward , Joyee Erina Baynojir 
Published: August 2024

Abstract
This paper presents the 3D printing of novel SiC-Fe3O4 nanocomposites for humidity sensor application via the Direct Ink Writing method. 3D printing is a transformative manufacturing technology that has achieved notable advancements in fabricating complex polymer-ceramic nanocomposite structures that exhibit enhanced properties for various applications. Within the complex framework of additive manufacturing (AM), extrusion-based techniques such as the direct ink writing (DIW) method, significantly contribute to broadening the range of 3D-printable materials with unconventional structures, particularly for sensors.
Considering the versatility of the DIW method in developing sensing systems, silicon carbide (SiC)-polymer nanocomposites have garnered substantial attention due to their promising properties for humidity sensing applications. Humidity sensors play a crucial role in human comfort and health, energy efficiency, enhancing product quality by protecting valuable equipment, etc. Therefore, the combination of polymer-ceramic nanocomposites with DIW produces highly sensitive sensors with accurately engineered mechanical properties. However, the DIW process requires careful consideration of the nanocomposite ink to ensure compatibility with the extrusion and deposition process.
This study investigates a novel 3D-printable ink based on SiC-polymer nanocomposites for the DIW process. The inks are composed of SiC and magnetite (Fe3O4) particles dispersed within a polyvinyl alcohol (PVA) matrix. The printability including extrudability and viscoelasticity of the inks was thoroughly evaluated for proper flow through the nozzle and rapid shape recovery of the ink after deposition. In addition, DIW process parameters were optimized to print an interdigitated pattern for humidity sensing. It was observed that the same SiC and Fe3O4 ratio provided the best rheological behavior and was successfully 3D printed with high printing accuracy without any defects. Besides the rheological behavior, DIW process parameters were optimized to print an interdigitated pattern for humidity sensing.
Operational Modal Analysis for Chatter Prediction in Milling
Authors: Zorlu Ayberk , Ahmadi Keivan 
Published: August 2024

Abstract
Unstable self-excited vibrations in machining are known as chatter and must be avoided by selecting appropriate machining parameters (e.g. spindle speed and cutting depth). Nonetheless, even when the machining parameters are carefully selected according to chatter models, vibrations may still become unstable due to unmodeled dynamics and process variations. Therefore, it is critical to monitor the process and detect chatter promptly.
This paper is an extension of our lab’s previous work, where we used Operational Modal Analysis (OMA) to monitor the loss of stability while the process is still stable, unlike current methods that detect chatter only after it occurs. This approach was applied to turning in our previous work, and the present work reports the preliminary results of extending its application to milling. While turning dynamics are time-independent, milling dynamics exhibit periodic variations due to tool rotation. Consequently, it becomes essential to adopt fundamentally different OMA theories and data acquisition methods to address time-periodic characteristic. To address this, we construct a lifted time-independent representation of the periodic dynamics from the measured process vibrations, enabling the application of standard OMA methods to the periodic system. The effectiveness of the presented method in predicting different types of milling chatter is demonstrated by numerical simulations and comparison with the Semi Discretization Method (SDM).
A Deep MLP-CNN Model Using Metadata to Predict Melt Pool Morphology in LPBF: Effect of Geometrical Factors
Authors: Vaghefi Ehsan , Hosseini Seyedmehrab , Mirkoohi Elham 
Published: August 2024

Abstract
The laser powder bed fusion (LPBF) process enables complex geometries to be created in bespoke parts. The LPBF process reduces lead times and costs for high-value parts by reducing material waste, unlike traditional subtractive manufacturing techniques. However, the fabrication of complex geometries using the LPBF process for industrial applications remains challenging in terms of achieving consistent mechanical properties. Experimental investigations show that geometrical factors such as shape and size impact the thermal history, microstructure, defect structure, and thus mechanical and fatigue properties. Consequently, the partially known effect of design parameters on part quality causes significant challenges in the standardization, qualification, and certification of additively manufactured (AM-ed) parts. In this paper, a deep Multilayer Perceptron and Convolutional Neural Network (MLP-CNN) is proposed to predict the melt pool depth for various geometries during LPBF of stainless steel 316 L using metadata (i.e., numerical and image data). Quite often the melt pool depth generated during the AM processes is employed as a surrogate for thermal, defect- and micro-structural signatures. To predict melt pool depth during the LPBF process of complex geometries, a digital twin environment using a finite element model (FEM) is developed and validated using experimental melt pool measurements for different geometries within multi-tracks and layers. The FEM was in agreement with experimentation with an error of less than 15%. Through the process simulation, a large dataset of melt pool depths was obtained for various part geometries at different locations and process parameters. Next, the MLP-CNN framework was established to identify the impact of part design and process parameters on melt pool depths. To enhance the performance and robustness of this model, data augmentation has been implemented by rotating and transferring geometries to artificially expand the dataset. Data augmentation helps to mitigate overfitting and promote better generalization, especially in the context of limited training data. After training, the proposed model is found to give accurate melt pool prediction even for new geometries not considered during training. The fusion of CNN and MLP has led to melt pool predictions for unseen geometries with an accuracy of 95%.
Quantifying Trapped Powder in Electron Beam Powder Bed Fusion
Authors: Johnstone Brian , Massey Caroline , Lewis Mat , Huggins William , Saleeby Kyle , Saldaña Christopher 
Published: August 2024

Abstract
Electron beam powder bed fusion (PBF-EB) shows great potential for manufacturing complex parts including those with internal cavities for heat exchanger, manifold systems, or energy absorption purposes. PBF-EB allows for the manufacture of channel geometries without the need for support structures. Due to the nature of the powder spreading process, powder feedstock is often trapped in intentionally manufactured cavities. This trapped powder can often be difficult to remove and can disturb the intended flow of fluid through the cavity or damage downstream components in its use case. These trapped powder particles present a risk of contamination and component failure if not completely evacuated. Ti6Al4V is a choice material for aerospace applications due to its high strength to weight ratio and its composition as a nonferrous metal; however, in weight sensitive applications excess entrapped powders or powders loosely attached to the surface could cause undesirable weight increases. The inherent spreading process of PBF-EB is different than laser powder bed fusion (PBF-LB) in its operational temperature, sintering. In addition, PBF-EB is less commonly studied in literature compared to its PBF-LB counterpart, and as a result the complexity of the semi-sintered powder and its spreading behavior are not well understood. Prior work has investigated the difficulty in removing trapped powder from PBF-EB, but these studies do not address how to quantify the amount of trapped powder in the cavity. Thus, an accurate method to measure the amount of trapped powder in the cavity must be investigated. In this work, Ti6Al4V coupons were manufactured with horizontal and vertical cavities of three different sizes. Archimedes testing allows for the determination of density differences caused by porosity and trapped powders by measuring mass and volumetric dispersion. Computed tomography (CT) is well suited for segmenting the internal structure and features of a part and has been studied for applications including voids, porosity, and dross. Thus, CT was explored as a method for evaluating trapped powder content in this work. The volumetric representation of the segmentation of the reconstructed CT volume can vary greatly depending on the input filter and thresholding methods. In this study, four different types of segmentation approaches were evaluated to determine the best approach for segmenting the volume as compared to an operator labeled ground truth. The percentage density results from the Archimedes testing were compared to the volumetric percent density from the computed tomography approach. Differences in packing density between two different internal channel features were investigated. Overall, this work sought to validate the use of computed tomography for the detection of trapped powders and present a framework for volumetric segmentation.
Brief Paper: A Preliminary Study on Depowdering of Fragile Objects Using Robots in Binder Jetting Additive Manufacturing
Authors: Ye Junjiang , Alam Md Moktadir , Okwudire Chinedum 
Published: August 2024

Abstract
The depowdering process in Binder Jetting Additive Manufacturing is indispensable for the integrity of 3D-printed objects, necessitating the removal of excess powder from their surfaces. However, this step poses a formidable challenge to the productivity of powder-based AM, as traditional depowdering methods reliant on manual labor prove time-consuming, labor-intensive, monotonous, and hazardous for human operators. Existing automated depowdering solutions exacerbate this challenge, with some systems demanding preliminary manual depowdering, nullifying automation benefits, while others lack the needed adaptability for diverse 3D-printed objects. In response, this manuscript proposes a robotic depowdering system designed to handle fragile 3D-printed components with precision beyond manual or less sophisticated automated methods. The proposed robotic depowdering system comprises two robots: Robot 1, responsible for generating trajectories to eliminate powder from the powder bed environment using fixed STL files, and Robot 2, which fine-tunes force and pressure while handling delicate, printed 3D objects, safeguarding their intricate structures. Preliminary experimental results confirm that the proposed depowdering system effectively handles fragile 3D objects from the powder bed environment and removes residual powder to a substantial degree. This accomplishment is of paramount significance as it directly contributes to the quality of the final product. By successfully addressing both of these critical aspects, the system demonstrates its capacity to ensure the structural integrity and overall quality of the end product, marking it as a valuable tool in the realm of additive manufacturing.