bluuebunny/crossref_metadata_2025 huggingface dataset:
Failure Initiation Point Control in Blown Powder Directed Energy Deposition
Authors: Burl Alan , Heinrich Lauren , Saldaña Christopher , Feldhausen Thomas
Published: August 2024

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

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

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

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

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

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

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

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

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

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.
Generative Adversarial Networks With Domain Knowledge Fusion for Metal Additive Manufacturing
Authors: Faegh Meysam , Haghighi Azadeh
Published: August 2024

Metal additive manufacturing (AM) processes entail complex physical phenomena, leading to intricate process-structure-property (PSP) relationships. As PSP relationships are highly linked with thermal fields in metal AM, the appropriate selection of process parameters and their real-time adjustment can be highly beneficial. The infrared images obtained from the LPBF process contain crucial information, such as the size and shape of the melt pool. These details provide valuable insights into the thermal history, structure, and properties of the printed part. Consequently, establishing a diverse set of thermal images is of utmost importance to comprehensively explore the PSP relationships. To unravel the underlying mechanisms in AM and identify real-time optimal parameters, a wide variety of experimental approaches as well as physics-based models have been investigated. However, conducting experimental studies or using physics-based models to attain accurate thermal history data are highly costly and time-consuming. Generative adversarial networks (GAN), known for their capability to generate synthetic images, present a promising solution to the challenge of data availability.

The current study proposes a conditional GAN capable of visualizing the metal AM temporal thermal data with process domain knowledge fused into the image generation procedure. Targeted temporal thermal field images of a laser powder bed fusion process are generated by considering laser power, scan speed, and laser spot size process parameters as the fused domain knowledge. The goal is to capture the relationship between the process parameters and thermal images and eventually generate new images with various unseen combinations of process parameters sampled from the design space. A customized data loader is designed to merge experimental thermal images with the corresponding domain knowledge. The architecture of the generator is intricately designed to combine a set of process parameters, i.e., the laser power, scan speed, laser spot size, and time step, with a vectorized noise drawn from a latent space to produce a synthetic image. Moreover, the discriminator scrutinizes a collection of both experimental and synthetic images, along with their associated domain knowledge, to discern their authenticity. The hyperparameters of both generator and discriminator networks are tuned and the methods used for stabilizing the training process are discussed. Finally, the performance of the model is evaluated, and future research directions are highlighted.
Macroscopic and Microstructural Analysis of Ultrasonic Vibration on the Compression of Pure Aluminum Al1100-O
Authors: Lee Youngrok , Banu Mihaela
Published: August 2024

The use of ultrasonic vibrations in manufacturing has been demonstrated its potential pathway to replace heating-assisted forming with significant benefits, including lowering the carbon footprint and increasing cost-effectiveness. This technology utilizes high-frequency mechanical vibrations, typically in the ultrasonic range (20 kHz or higher), to enhance the deformation and flow of metals during drawing, tube bending, punching, equal channel angular pressing, and incremental sheet forming. The utilization of high-frequency vibration in these manufacturing methods has revealed a fascinating interplay between the mechanical properties of metals and the dynamic forces introduced by the ultrasonic vibration. While the application of ultrasonic vibration in metal forming processes has shown promise in surface finish and reducing forming forces, there is still a need for more in-depth studies on the macroscopic response of materials during conventional mechanical testing for optimizing the process and ensuring the quality and reliability of formed components. In this study, the cyclic effect of ultrasonic vibration on the macroscopic material response of commercially pure aluminum, namely Al1100-O, was investigated through the implementation of ultrasonic-assisted compression (UAC) tests. Cyclic vibrations were applied to the compression of Al1100-O around a strain of 10%, 20%, and 30% with increasingly higher vibration amplitudes. Acoustic softening results presented in this work were calculated at each engineering strain. Both acoustic softening and residual effect due to ultrasonic vibration are experimentally verified in commercially pure aluminum in the range of 1μm and 3μm amplitude with 20 kHz vibration frequency. The mechanism of acoustic softening is centered on the localization of acoustic energy at defect sites, such as dislocations. This localization reduces the critical energy required for slip. In contrast, acoustic residual hardening is primarily attributed to the increase in dislocation density, resulting from the multiplication of dislocations induced by ultrasonic vibration. To validate the proposed mechanism, electron backscatter diffraction (EBSD) tests were carried out on formed samples. Comparing the misorientation maps, the overall low angle grain boundaries (LAGB) misorientation density increased for the UA sample, implying a smaller subgrain size. The formation of LAGB is a common method to reduce the overall internal defect density. Greater intensities in the kernel average misorientation (KAM) and geometrically necessary dislocations (GND) maps support the supposition that greater subgrain boundaries are formed during forming. The findings confirmed the significant effects of high-frequency vibration on metal plasticity and provided a basis for understanding the underlying mechanisms of vibrationassisted forming.
Brief Paper: Process Monitoring and Fault Detection in Laser Powder Bed Fusion Using In-Situ Acoustic Emissions
Authors: Bevans Benjamin D. , Riensche Alexander , Plotnikov Yuri , Sions John , Snyder Kyle , Hass Derek , Rao Prahalada
Published: August 2024

This work concerns process monitoring in the laser powder bed fusion additive manufacturing process. In this work, we developed and applied a novel in-situ solution for process stability monitoring and flaw detection using acoustic emission sensing. Current process monitoring methods in laser powder bed fusion only focus on the top surface of the deposition process, using an array of sensors to capture data on a layer-by-layer basis. Common sensors used for in-situ monitoring of the laser powder bed fusion process are optical, infrared, and highspeed imaging cameras along with pyrometers and photodiodes. A critical flaw with traditional top surface monitoring methodologies is that they are unable to reliably monitor the subsurface phenomena that occur in the laser powder bed fusion process. These subsurface effects are caused by the meltpool penetrating multiple layers below the top surface, leading to the re-solidification of the microstructure and potentially generating keyhole porosity. By only monitoring the top surface of the laser powder bed fusion process, the meltpool depth aspects and effects are ignored. To overcome the limitations of current in-situ monitoring of subsurface effects, this work utilizes four passive acoustic emission sensors attached to the build plate. These acoustic emission sensors monitor the energy emissions generated from the surface-level laser material interactions. Moreover, the acoustic emission signals are capable of traveling through the previously deposited layers, through the build plate, and to the sensors. Therefore, the acoustic waveform generated by the laser can capture process phenomena ranging from the crystallographic level to the macro-scale layer level which are at the root of flaw formation inside the deposited part. Hence, acoustic emission monitoring has the ability to monitor the subsurface effects in the laser powder bed fusion process. To monitor and analyze this acoustic waveform, novel wavelet-based decomposition is combined with heterogeneous sensor fusion to not only capture the acoustic waveform in time, but also in locational space on the build plate. Locational acoustic emission data enables the ability to determine the source of the generated acoustic waveform which is advantageous when the location of flaws is desired. This extracted spatially placed acoustic waveform data is able to detect the effect of processing parameters with a statistical fidelity of 99%. The proposed locational acoustic waveform monitoring method correlates to the resulting surface roughness of manufactured samples with a fidelity of 86%. Additionally, we show that acoustic waveform monitoring detects the onset of part failure, recoater crashes, and warpage prior a priori to the actual failure point.
Brief Paper: Evaluating Effects of Ultraviolet Exposure Strategies on Multi-Material Interfaces in Direct Ink Write Additive Manufacturing
Authors: Thale Supreet S. , Williams Christopher B.
Published: August 2024

Direct Ink Write (DIW) is a material extrusion additive manufacturing (AM) technique in which viscous inks are extruded through a nozzle and selectively patterned in a layer-wise manner to create three-dimensional parts. In UV-assisted DIW, ultraviolet light is used to quickly cure the extruded bead to limit spreading, and to induce sufficient strength to support subsequent layers. Printing and UV curing multiple inks within the same layer enables the fabrication of multi-material components; however, it also creates distinct interfaces in the printed part, which can become regions of failure after undergoing cyclic loading and unloading.

The aim of this work is to understand how UV irradiance, UV exposure, and the sequence of UV dosing affects the resultant multi-material part’s mechanical properties and failure mode (i.e., whether it fails at the multi-material interface or inside the weaker material). To understand the effect of UV irradiance, UV exposure, and sequence of UV dosing on the properties of the multi-material parts, we explore two tool-pathing strategies on a two-component tensile specimen printed in the XY plane using photocurable urethane acrylate resins: (1) ‘Ex-situ’ curing strategy applies UV irradiation across the entire layer following deposition of both materials; (2) ‘Alternate’ UV curing applies UV irradiation following deposition of each individual material. Multi-material specimens printed using the ‘ex-situ’ UV cure strategy consistently failed within the gauge region irrespective of the UV irradiance and exposure, and also exhibited the highest adhesion strength. However, for specimens fabricated via ‘alternate’ UV cure strategy, the failure mode (within gauge or at the interface) was found to be a function of cure depth, UV irradiance, and UV exposure. The findings will help inform the process with relevant processing parameters — amount of UV irradiance, UV exposure, sequence of UV cure, and the tool-pathing strategy required to print multi-material parts with desired and tunable properties and performance.
Manufacturing Cathodes via Dry-Processing for Lithium-Ion Batteries
Authors: Tao Runming , Steinhoff Bryan , Cheng Yang-Tse , Li Jianlin
Published: August 2024

Conventional lithium-ion battery (LIB) electrodes are prepared through a wet slurry process with n-methyl pyrrolidone solvent, especially for cathodes. The wet slurry process encounters several disadvantages such as binder migration, electrode cracking in thick electrodes, energy intense heat-dry NMP solvent removal, and costly NMP recovery. The cost and energy consumption of coating and drying of electrode are about 11.5 % and > 46 % in LIB manufacturing, respectively. Thereby, it is essential to develop a facile roll-to-roll solvent-free LIB electrode processing for reducing the cost and energy consumption.

Recently, the Maxwell-type dry processing (DP) shines new lights on LIB manufacturing, which mainly bases on dry mixing (DM) of electrode component powder followed by calendering into electrode films and laminating onto current collectors, realizing the rapid manufacturing of LIB electrodes in a powder-to-film manner for industries. This report shares some recent progress on the DP from our group. We aim to further advance the manufacturing science of DP by correlating the processing conditions with electrode properties and performance. Particularly, we investigate the effect of DM, and compression on the polytetrafluoroethylene (PTFE) binder fiberization, porosity, mechanical properties, electrical conductivity and electrochemical behaviors of electrodes.

The DM study suggests that PTFE fiberization heavily relies on the degree of DM. Insufficient DM results in poor PTFE fiberization while outrageous DM damages the formed PTFE fibers. Both negatively affect the mechanical behaviors of the electrodes and their rate capability. However, moderate DM is highly beneficial. In addition, our study of the porosity impact reveals that LiNi0.8Mn0.1Co0.1O2 (NMC) secondary particles can be broken into primary particles due to compression, especially at low porosity. Those fractured NMC secondary particles exhibit lower modulus. We propose that a moderate porosity of around 32% favors the electronic conductivity, charge transfer impedance and rate capability. The study of the cathodic electrolyte interphase layer of PTFE-based DPed electrode confirms that side reactions of PTFE binder due to the formation of LiF in LiClO4-based electrolyte.
Residual Stress Measurement Using Nanoindentation on Parts Additively Manufactured by Laser Powder Bed Fusion (LPBF)
Authors: Nguyen Dan T. , Haridas Ravi Sankar , Mirshams Reza A. , Siller Hector R.
Published: August 2024

This article introduces an innovative study on residual stress analysis in Laser Powder Bed Fusion (LPBF) fabricated materials, focusing on an Al-3Ni-Ti-0.8Zr sample. By leveraging Swadener’s theoretical approach alongside nanoindentation techniques, the research offers a cost-effective and easily applicable method for mapping residual stress patterns, distinguishing itself from more expensive and complex techniques like X-ray diffraction or destructive methods such as contour or drilling. The investigation correlates residual stress patterns with critical mechanical properties, including yield strength 265.03 MPa and ultimate tensile strength up to 347.5 MPa, thereby demonstrating the significant influence of LPBF process parameters on material characteristics. The findings provide initial validation of this accessible methodology, emphasizing its potential as a tool for understanding and mitigating residual stresses. As an ongoing endeavor, future work will expand upon these insights through broader data collection, rigorous validation, and comparative analyses with other stress detection techniques. This study marks a significant advancement in additive manufacturing research, aiming to optimize LPBF processes and improve material performance by offering a more straightforward yet effective alternative to traditional stress analysis methods.
Physics-Informed Loss Functions With Explainable AI to Predict Emission in Powder Bed Fusion
Authors: Regal Ethan , Gawade Vidita , Guo Weihong
Published: August 2024

Despite the growing prevalence and use of custom metal-based parts produced by powder bed fusion (PBF) processes, overheating remains an issue that prevents stability and zero-defect parts from being printed. Data-driven methods utilizing existing data of prior prints are often employed to help avoid overheating. Despite the impressive predictive power associated with these methods, there is no guarantee that the model’s behavior will align with the physical understanding of the printing process. That is, the model may capture a trend within the data that is impossible in the real-world setting. One solution to this problem is using custom physics-based loss functions designed with the known physics knowledge of the PBF printing process to enforce model behavior that obeys physical understanding. This paper proposes two custom loss functions to enforce a physics-obeying model. These loss functions are then implemented into a developed sequential neural network, and the model’s predictive performance is evaluated using root mean square error. To increase transparency and interpretation of the model, an explainable AI technique, Accumulated Local Effects (ALE), measures the ability of the loss functions to enforce physical behavior in the model. It is observed that the custom loss functions successfully enforce physical behavior in the model with a slight compromise in predictive power.
A Bayesian Network-Based Control Charting Framework for Identifying the Root Causes of Process Shifts in Multistage Manufacturing Systems
Authors: Mondal Partha Protim , Ferreira Placid M. , Kapoor Shiv G. , Bless Patrick N.
Published: August 2024

Multistage manufacturing systems (MMS) are a pervasive and essential part of high-volume production systems. An MMS is characterized by many interdependent stages, each with multiple process settings, accepting inputs characterized by many features and producing outputs with several features related in a complex manner to the input features and process settings. This makes it challenging to pinpoint specific causes of any quality issues observed in the output of the system. Although conventional control charts are the most widely used tool for quality monitoring in an MMS, they do not provide insights into the root causes of quality departures from normal to help focus corrective actions. Historically, in industrial applications, the responsibility of identifying the underlying root causes of observed quality excursion has rested with human operators, who, in turn, rely on empirical methods to analyze the data and come up with potential causes. Therefore, in this paper, a Bayesian network-based control charting framework is proposed to directly track the current state of process inputs and settings of a multistage system for efficiently identifying the root causes when quality departures are observed in the system’s output. The overall framework consists of two components: a) A Bayesian network (BN) for inferring the states of the process inputs and settings, based on observed system outputs, and b) A control charting procedure for tracking the states of the process inputs and settings to determine if each is in or out-of-control. To illustrate the concepts presented in this paper, a tractable two-stage manufacturing system is used as an example process. The simulation results demonstrate the framework’s practical application, highlighting how it integrates root cause diagnosis into quality monitoring, showcasing a more efficient way of diagnosing root causes in multistage systems.
A Deep Reinforcement Learning Approach for Production Scheduling in Computer Server Industry
Authors: Radman Azzam , Aqlan Faisal , Parikh Pratik , Alam Md. Noor E.
Published: August 2024

Computer Server Industry is characterized by extensive test processes to ensure high quality and reliability of the servers. Computer Server Industry production systems utilize Configure-To-Order (CTO), also known as fabrication/fulfillment, strategy which provides an effective balance between demand and supply by synchronizing the flow of materials, equipment, and labor throughout the production process. In the fabrication stage, components or sub-assemblies are produced, tested, and assembled based on a projected production plan. They are then kept in stock until an actual order is received from a customer. In the fulfillment stage, final products are assembled according to actual customer orders. Assignment of products to test cells during the fulfillment stage can be a challenging task due to high quality requirement and limited resources. Current practices tend to assign products to test cells based on a specific criterion such as on-time shipment or maximum test cell occupancy, which can result in higher levels of energy consumption or delayed orders. This paper introduces a Deep Reinforcement Learning (DRL) approach to effectively assign servers to test cells considering a multi-objective reward function that combines multiple criteria. A proposed simulation model serves as the environment with which the DRL agent interacts, learning a policy that develops a test schedule for the products. The proposed approach is tested with a case study from a high-end server manufacturing environment. Sensitivity analysis is conducted to analyze the impact of the different values of the system’s variables on its performance.
Investigating Jet Stability in Inkjet Printing Through a Novel Sensing Modality
Authors: Chivate Aditya , Li Zebin , Kamble Prachi , Sun Hongyue , Zhou Chi
Published: August 2024

In recent years, inkjet 3D printing has rapidly gained prominence as a disruptive fabrication technique that has witnessed ever-increasing demand in the fields of biomedicine, metal manufacturing, electronics, and functional material production. This innovative approach involves precise deposition of controlled amounts of material onto a moving substrate through a nozzle, achieving impressive sub-millimeter scale resolution by leveraging the concepts of micro-droplet deposition. However, the dynamic nature of the process introduces significant challenges related to consistency and quality control, especially in terms of reproducibility and repeatability. The key input parameters governing this process, such as pressure, voltage, jetting frequency, and duty cycle, are interrelated, entailing the identification of optimal settings in order to realize high-quality jetting. At present, the data collection heavily relies on image-based methods which are inherently slow and often fail to encompass the entirety of the data, making it difficult to determine the relation between the input parameters and jet characteristics. To address this multidimensional difficulty, we developed a unique approach based on light-beam field interruption to collect critical jet data at high speeds. This novel approach collects both temporal and spatial information on droplet evolution, making it a vital tool for enhancing our ability to attain high accuracy and control in inkjet 3D printing. To illustrate the efficacy of our approach, we model the extracted features derived from the process parameters and the extracted data to predict the droplet jetting behavior and droplet size. Specifically, a decision tree classifier is used to predict the jetting behavior and discern between “ideal” and “non-ideal” jetting behaviors. Simultaneously, a linear regression model was employed to predict the droplet size within the “ideal jetting” class based on the interplay of process parameters and the extracted features. The results emphasize the system’s accuracy in capturing the droplet behavior and size using our light-beam field interference sensing module. Furthermore, these findings establish a crucial foundation for the implementation of real-time feedback control loop in the inkjet printing process, promising advancements in adaptability and precision.
Manufacturing Novel Textile Wearables Through Kirigami-Inspired Computerized Embroidery Stitching
Authors: Bisram Marisa , Wu Yunyun , Chen Kevin , Cao Jian
Published: August 2024

Textile manufacturing has seen remarkable advancements through the years in output speed and automation. Traditional approaches to crafting wearables a laminated composites from reinforcement rely on joining various textile pieces cut from a larger stock material together, a laborious and time-consuming process with high waste. In recent years, the introduction of seamless 3D knitting has been a revolutionary leap in this field, as it allows for a full structure without additional joining; however, it is not without its limitations with complex software and infrastructure requirements. The challenge that manufacturers now face is developing an easily accessible alternative to sustainable production of textiles. In this paper, we propose a novel method of fiber reinforcement and 3D wearable construction using kirigami inspired embroidery stitches. Advanced kirigami approaches coupled with digital embroidery technology allow for highly customizable textiles that can be optimized to form into varying shapes and stiffnesses. This work introduces a process for manufacturing embroidery-based textiles using kirigami patterns. Experimental testing occurs to understand the tensile mechanics of the structure as kirigami slits are included in the design, creating a foundation so that further optimization of the process can occur.
Near zero-field magnetoresistance and defects in gallium nitride pn junctions
Authors: Elko M. J. , Hassenmayer D. T. , Higgins A. A. , Lenahan P. M. , Flatté M. E. , Fehr D. , Craven M. D. , Larsen T. D.
Published: August 2024

As gallium nitride (GaN) grows in importance, an atomic scale understanding of device performance is of significant relevance. In this paper, we present the first observations of near zero-field magnetoresistance (NZFMR) detecting electrically active defects in GaN-based devices. Our observations involve recombination current in GaN pn junction diodes. We attribute the NZFMR response to gallium vacancies.
Interfacial reaction between the nickel-based superalloy and the Al2O3 crucible during vacuum induction melting
Authors: Zhang Guodong , Zhang Jinhong , Yang Haoxue , Wang Jun , Liu Xianghong , Li Jinshan
Published: August 2024

The effects of holding time on the interfacial reaction between a nickel-based superalloy and an Al2O3 crucible during the vacuum induction melting process were studied at 1450 °C. The results show that the reaction products at the interface are intermittently distributed and then gradually become dense and continuous as the holding time increases. The average thickness of the reaction layer increases and then decreases when the melting time is extended. Through microstructure characterization, the interfacial reaction layer extending from the boundary to the matrix consists of a continuous Al2O3 reaction layer and a discontinuous TiC layer. Finally, the process of the interface reaction is summarized.