bluuebunny/crossref_metadata_2025_split huggingface dataset:
Generative Adversarial Networks With Domain Knowledge Fusion for Metal Additive Manufacturing
Authors: Faegh Meysam , Haghighi Azadeh 
Published: August 2024

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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 &gt; 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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.