bluuebunny/crossref_metadata_2025_split huggingface dataset:
Microfabrication of Fish Scale-Inspired Surfaces via Glancing Angle Deposition (GLAD) With Line Seeds
Authors: Qu Chuang , Caruso Luca , Walsh Kevin 
Published: August 2024

Abstract
This paper presents an innovative micro fabrication process for fish scale inspired surfaces, achieved through glancing angle deposition (GLAD) with line seeds. Fish scales exhibit diverse shapes, sizes, and distributions. Artificial surfaces mimicking fish scales can be scaled-up for practical applications, such as antifouling coatings, swimsuits, and solar energy harvesting devices. While previous researches have explored recreating fish scale surfaces using micro/nano fabrication techniques like additive manufacturing and nanoimprint lithography, certain challenges persist.
In response, we propose recreating both leptoid and placoid scales using GLAD. GLAD is a physical vapor deposition process that generates intricate three-dimensional (3D) micro and nanostructures by harnessing the shadowing effect of nucleation sites for directional vapor deposition control. Using these pre-determined nucleation sites (also called seeds), one can transform two-dimensional line seeds into three-dimensional stacking features, while also customizing the size, shape, and distribution of the scales. This paper offers a comprehensive exploration of the line seed design for replicating fish scales. It further delves into the fabrication process, covering both line seeding through conventional photolithography and the GLAD procedure. After one GLAD session using Ge as the material, a 20 nm parylene layer is applied to enhance surface robustness. The resulting surfaces, mimicking fish scales, undergo rigorous testing for their hydrophobic properties, exhibiting a water contact angle exceeding 110 degrees.
Improving the Efficiency of WAAM-Based Hybrid Manufacturing Through Selective In-Situ Machining Based on Height Error Prediction
Authors: Hendrickson Nicholas , Valizadeh Sotubadi Saleh , Nguyen Vinh 
Published: August 2024

Abstract
Hybrid manufacturing processes combine additive and subtractive methods that offer a promising avenue for achieving intricate geometries and enhanced printed properties. Parts produced using additive manufacturing technologies typically have inferior surface finish and dimensional accuracies compared to subtractive machining processes. A key advantage of hybrid manufacturing is the allowance of a single platform to produce a printed part that contains improved surface finish and tolerances of selective features compared to traditional printing. Therefore, critical to the success of hybrid processes is maintaining precise control over the part’s dimensional accuracy, particularly the in-situ layer height. Currently, the Wire Arc Additive Manufacturing (WAAM) process begins by determination of weld bead geometry. This is achieved by printing lines of test beads consisting of the desired levels of prominent print parameters that include welding voltage, torch travel speed, and wire feed speed. The test beads are then measured, these measurement values are then used to set the bead geometry used to establish the layer height and step over values used in the part program. Currently, in-situ machining is conducted in hybrid systems to provide a flat surface for subsequent layers. However, the machining heights established during programming are currently arbitrarily based on nominal layer heights. Printing using constant voltage, torch speed and wire feed speed do not produce consistent bead geometry, causing varying layer heights. Because of varying layer heights, this non-adaptive method of in-situ machining results in inefficient and repetitive tool changes that limit the efficiency of hybrid manufacturing processes. The research presented in this paper aims to investigate a method to adaptively determine the in-situ machining height through in-process temperature measurement. This study was conducted by printing sample parts and collecting detailed height measurements of WAAM parts to assess the deviation from the actual printed part height to the nominal part model height. These measurements were then correlated with temperature data collected throughout the manufacturing process to derive a relationship between temperature measurements and the physical layer height. The relationship was then used to determine the proper machining height for in-situ machining. The results of this study were conducted on a robotic WAAM-based and milling hybrid system. The results of this study offer critical insights into the in-process timing of when to switch between printing and machining to enable more robust and precise manufacturing strategies. Hence, this research contributes to the overall ongoing advancement of hybrid manufacturing techniques and their broader adoption in industrial applications.
MScan: An Automated In-Situ Fault Detection System for Desktop Fused Filament Fabrication 3D Printers Utilizing a Non-Contact Sensor
Authors: Lyu Hanyao , Wu Pinyi , Okwudire Chinedum 
Published: August 2024

Abstract
Fused filament fabrication (FFF) has gained widespread recognition across diverse industries owing to its rapid prototyping and cost-effectiveness advantages. As a result, it is the most prevalent modality for desktop 3D printing. However, FFF can be susceptible to a variety of printing defects and jeopardize the printing quality. Monitoring when defects occur during 3D printing and promptly stopping faulty printing remains a significant challenge. To address this challenge, engineers have developed techniques for detecting and characterizing defects during the FFF printing process. They can be categorized into contact and non-contact detection methodologies. Non-contact methods usually rely on computer vision or laser scanning. However, computer vision needs the assistance of machine learning and demands a substantial amount of training data for accurate detection. Moreover, computer vision is susceptible to ambient light conditions. The laser scanning method detects the printing defects by comparing the point cloud obtained from scanning the printed object with the CAD model. However, this approach heavily depends on the precision of the laser scanner, and achieving high accuracy often entails a significant financial investment for a good laser scanner. To improve accuracy and cost-effectiveness, a low-cost contact-based detection system called MTouch was developed in prior work. However, using contact sensors carries a risk of damaging fragile prints and leading to printing failures. In response, this paper introduces a non-contact, cost-effective, and robust detection method, MScan, to detect defects during the printing process. In the MScan setup, a laser-camera sensor is designed with a laser stripe emitter and a camera module based on laser triangulation to assess the absence of the printed object during the printing process. Additionally, MScan employs an effective and straightforward image processing and data acquisition algorithm to ensure its robustness and computational efficiency. The effectiveness of MScan is demonstrated experimentally by deploying it on an Ender 3 desktop FFF 3D printer. A fault detection accuracy of over 95% is achieved. Furthermore, MScan’s robustness to lighting variations is experimentally demonstrated.
Correlation Study Between the In-Situ Thermal Signatures and Surface Roughness Produced by Laser Powder Bed Fusion
Authors: Yan Dongqing , Fan Zhaoyan , Pasebani Somayeh 
Published: August 2024

Abstract
Laser Powder Bed Fusion (LPBF) is one of the prominent additive manufacturing methods developed in the last decades to fabricate metal parts with high geometric resolution. To fully leverage the advantages of LPBF, it is generally desired to produce low-roughness surfaces to minimize the efforts in post-process machining. However, understanding the formation of the surface roughness remains obscure due to the complexity of the fabrication process. This paper presents a machine-learning approach to monitor the surface roughness in-situ using a high-speed infrared (IR) camera observing the printed tracks during LPBF process. Thermal signatures such as temperature gradient surrounding the melt pool in different orientations, maximum temperature, and maximum temperature standard deviations were extracted from the IR images and compared with the local surface roughness. Experimental studies have been conducted to train and validate the model. The results have shown good correlations between the temperature gradient and the roughness.
Study on the Interfacial Properties of Dissimilar Structures Fabricated by Hybrid Injection Molding
Authors: Stan Felicia , Sandu Ionut-Laurentiu , Fetecau Catalin 
Published: August 2024

Abstract
Among the hybrid manufacturing techniques, hybrid injection molding is a cutting-edge manufacturing strategy that combines the benefits of additive manufacturing (AM) and conventional injection molding (IM), enabling the production of parts with complex internal structures and functionalities. To take full advantage of the hybrid injection molding process, understanding interfacial bonding between materials with different degrees of stiffness is critical, as it can significantly affect the overall structural and mechanical integrity.
In this study, the hybrid injection molding process is used to produce nano-reinforced hybrid structures with two layers of acrylonitrile butadiene styrene (ABS) and carbon nanotube polyurethane nanocomposite (TPU/CNT). ABS substrates were manufactured by fusion filament fabrication (FFF) and then over-molded with TPU/CNT nanocomposite to add functionality. For comparison, ABS-TPU/CNT structures were also produced by conventional over-molding and FFF. The interface properties (interface bonding and adhesion mechanisms) of the ABS-TPU/CNT specimens were investigated by lap-shear tensile test. It was found that the interface of the hybrid ABS-TPU specimens is as strong as that of those obtained by over-molding but about 35% higher than the 3D-printed interface. On the other hand, the hybrid ABS-TPU specimens with 5 wt.% CNTs exhibit electrical conductivity in the semi-conduction range, comparable with that of the over-molded specimens and about one order of magnitude lower than that of the 3D-printed specimens. This work provides guidance for assessing the potential of using hybrid injection molding technology (e.g., 3D printing and injection molding) for engineering applications.
Advancing Digital Twin Technology in Manufacturing: A Comprehensive Study on Data Capture and Simulation of End Mills
Authors: Anbalagan Arivazhagan , Kauffman Marcos , Long Tengfei 
Published: August 2024

Abstract
This research presents a significant advancement in digital twin technology within the manufacturing sector, focusing on enhancing data acquisition and simulation processes during critical product lifecycle phases of endmill: design modeling and finite element analysis (FEA). The core of this study lies in the innovative creation of virtual assets that emulate real-time end milling operations. Our approach leverages CAD/CAE kernels as a pivotal bridge, connecting physical assets with virtual processes through process identification (pid) numbers. This methodology not only captures core utilization and other vital characteristics of computer systems as time series data signals but also introduces a novel angle in the data capture process, enhancing the fidelity of digital twins in manufacturing simulations. A key understanding of our research is the establishment of a direct correlation between CPU signal spikes and enhanced processing capacity, especially in relation to CAD/CAE kernel activities. This insight is critical for informed decision-making in tool and material selection, significantly reducing the development time for new material mathematical models. Our findings underscore the efficiency and practicality of digital twins in real-time milling processes, offering a robust framework for decision-making in both design and simulation phases. The study’s methodological innovations promise substantial contributions to digital twin technology, setting a new benchmark for precision and efficacy in manufacturing simulations.
Reducing Warpage in Additive Manufacturing With Novel Toolpath Design
Authors: Godderidge Todd , Tekinalp Halil , Ozcan Soydan , Garg Nikhil , Kim Seokpum 
Published: August 2024

Abstract
Warpage in Additive Manufacturing (AM) is one of the major unsolved problems, especially with Fused Filament Fabrication (FFF). As layers cool, internal stresses cause non-uniform shrinkage, resulting in undesirable shapes and therefore limited applicability in precision industrial manufacturing. Existing solutions include methods to increase bed adhesion such as brims, rafts, and methods to control the environment such as uniformly heated enclosures, and materials with less shrinkage such as carbon fiber-reinforce polymers to improve stability and resistance to warpage. Recent approaches utilizing pre-deformation involve modifying the initial part geometry to compensate for the warpage rather than preventing it, such that the shrinkage process itself transforms the part into the desired final shape. However, designing such inverse geometries is often imprecise because shrinkage does not occur the same way reversely and it depends strongly on the part shape. Therefore, all of these approaches have pros and cons. In this technical paper, we propose a novel concept to reduce the warpage, demonstrate through FFF printing, and verify the concept by measuring the warpage amount. We provide this novel approach as an additional option as a solution for the users to choose when it comes to a warpage issue.
The novel approach we present in this paper, currently filed as a patent application, involves adding extra material into each bead using customizable out-of-plane deposition toolpaths. This method preserves the original part dimensions, allows for compensation in unavoidable shrinkage during solidification, and keeps the total bounding volume constant. We present 3D printing experiments with large shrinkage polymers, Polypropylene and Nylon-11, verifying the applicability of the method with FFF test parts, and demonstrate a warpage reduction by over 40% compared with conventional printing approaches. We intend to extend our method by including various materials, applying them to a larger scale AM and a large range of user-defined input geometries.
Site-Specific Property Informed Interface Design of Multi-Material Laser Powder Bed Fusion
Authors: Griffis Jacklyn , Masker Andrew , Schreiber Jeremy , Manogharan Guha 
Published: August 2024

Abstract
Laser powder bed fusion (LPBF) is a widely used metal additive manufacturing (AM) method, capable of complex geometry fabrication with high resolution and surface quality. Recent advances have introduced multi-material powder bed AM, which allows for selective powder deposition across multiple layers or within a single layer to truly achieve 3D voxel gradation As this process may combine two dissimilar metals with varying thermal properties along with effects of AM-induced defects, such as as-built cracking, delamination, or residual stresses, are common. Preliminary work has demonstrated that thermomechanical induced cracking at the fusion boundary, leads to premature failure which propagates along the defects at or near the fusion plane. The novelty in this study lies in combining nanoindentation-informed finite element analysis (FEA) to evaluate the use of mechanical interlocking improve mechanical performance in the face of defective interfacial adhesion.
To assess the local mechanical properties across multi-material fusion boundary, nanoindentation is applied to determine the gradient of material hardness. Oliver and Pharr’s analytical method to determine Young’s modulus from nanoindentation is then used to inform FEA. The fusion boundary was modeled as an effective third material partition to generate a digital twin of the fusion boundary. By designing interlocking patterns, we aimed to alternate tensile and compressive stress across the metamaterial structure for an overall reduction in the stress concentration at the site of as-built cracks to reduce the defect’s likelihood of propagating to the state of premature failure. Three mechanically interlocked designs are evaluated to demonstrate the capacity to locally load regions of the fusion boundary in compression despite global tensile loading.
Integrated Data Processing and Model Selection in Machine Learning Framework Development to Predict Dimensional Errors in Wire Arc Additive Manufacturing (WAAM)
Authors: Valizadeh Sotubadi Saleh , Hendrickson Nicholas , Nguyen Vinh 
Published: August 2024

Abstract
In-situ monitoring of Wire Arc Additive Manufacturing (WAAM) is crucial to provide real-time insights into the process for corrective feedback. Machine Learning (ML) approaches have proven to provide reasonable correlations between the input data and the process outputs in WAAM. However, there is a lack of knowledge with the development of an integrated ML development framework to address the simultaneous consideration of data processing and ML model development. This study aims to provide a search method to select ML frameworks with the combination of data processing and ML models with holistic performance metrics. Acoustic, welding current, welding fume, and the plate temperature datasets were collected during each printing operation. Subsequently, the collected datasets were preprocessed and used for feature extraction of the input data based on the intrinsic physical behavior of each time series dataset. The processed and extracted features were used to train six ML regression models to predict the corresponding measured height of the WAAM-printed workpieces. Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), and R-Squared metrics were then used to compare the performance of each of the regression models. Therefore, this study efficiently searches among different integrated ML frameworks to result in the combination of the data processing method and ML model with the best performance for the prediction of height in WAAM.
Toward Rapid Process Qualification of Laser Powder Bed Fusion: Model Predictive Control of Part Thermal History
Authors: Riensche Alexander , Bevans Benjamin , Sions John , Snyder Kyle , Plotnikov Yuri , Hass Derek , Rao Prahalada 
Published: August 2024

Abstract
This work pertains to the laser powder bed fusion (LPBF) additive manufacturing process. The goal of this work is to mitigate the expense and time required for qualification of laser powder bed fusion processed parts. In pursuit of this goal, the objective of this work is to develop and apply a physics-based model predictive control strategy to modulate the thermal history before the part is built. The key idea is to determine a desired thermal history for a given part a priori to printing using a physics-based model. Subsequently, a model predictive control strategy is developed to attain the desired thermal history by changing the laser power layer-by-layer. This is an important area of research because the spatiotemporal distribution of temperature within the part (also known as the thermal history) influences flaw formation, microstructure evolution, and surface/geometric integrity, all of which ultimately determine the mechanical properties of the part. Currently, laser powder bed fusion parts are qualified using a build-and-test approach wherein parameters are optimized by printing simple test coupons, followed by examining their properties via materials characterization and testing — a cumbersome and expensive process that often takes years. These parameters, once optimized, are maintained constant throughout the process for a part. However, thermal history is a function of over 50 processing parameters including material properties and part design, consequently, the current approach of parameter optimization based on empirical testing of simple test coupons seldom transfers successfully to complex, practical parts. Rather than instinctive process parameter optimization, the model predictive control strategy presents a radically different approach to LPBF part qualification that is based on understanding and modulating the causal thermal physics of the process. The approach has three steps: (Step 1) Predict – given a part geometry, use a rapid, mesh-less physics-based simulation model to predict its thermal history, analyze the predicted thermal history trend, isolate potential red flag problems such as heat buildup, and set a desired thermal history that corrects deleterious trends. (Step 2) Parse – iteratively simulate the thermal history as a function of various laser power levels layer-by-layer over a fixed time horizon. (Step 3) Select – the laser power that provides the closest match to the desired thermal history. Repeat Steps 2 and 3 until the part is completely built. We demonstrate through experiments with various geometries two advantages of this model predictive control strategy when applied to laser powder bed fusion: (i) prevent part failures due to overheating and distortion, while mitigating the need for anchoring supports; and (ii) improve surface integrity of hard to access internal surfaces.
Brief Paper: Adaptive Layer Height Control for WAAM Oscillatory Toolpaths Enabled by In-Situ 3D Scanning
Authors: Molla Bemnet , Kosmal Tadek , Williams Christopher 
Published: August 2024

Abstract
Wire Arc Additive Manufacturing (WAAM) is a relatively low-cost, high throughput additive manufacturing technology that uses arc welding to deposit metal beads in a layer-wise fashion to produce large-scale, complex parts. Oscillatory deposition strategies in WAAM are known for increasing deposition rate, obtaining more uniform layer surfaces, and their ability to reduce thermal cycling. In addition, small changes to the oscillatory path planning can result in a wide variety of bead shapes, which can be used to enable adaptive layer height throughout the build. The ability to build part regions at different layer heights could further mitigate process constraints (e.g. thermal input) during fabrication, compensate for build defects to enable manufacture of unique geometries, and ultimately increase throughput. However, to enable adaptive build height in WAAM, both predictive bead geometry models and in-situ control must be used to manage the interface between varying deposition regions and obtain consistent geometries.
This study compares a bead geometry prediction model to enable adaptive layer height via oscillatory toolpathing with an in-situ 3D scanning strategy. The bead geometry prediction model incorporates multiple processing parameters, including arc voltage, arc current, mass flow rate, travel speed, along with intrinsic material properties. This model, in concert with oscillatory path frequency and amplitude, can be used to predict bead geometry (width and height). During manufacturing, in-situ top-down 3D scans are collected at every layer to gather depth information of as-built geometries. This depth information is used to evaluate the efficacy of the oscillatory deposition with respect to each deposition type/region. This comparison is demonstrated with the manufacture of large-scale, functional metal parts.