bluuebunny/crossref_metadata_2025_split huggingface dataset:
Investigation of the Morphological and Migration Behavior of Breast Cancer Cells on a Micropillar-Based Topography
Authors: Molinares Ponce Marielena , Wolpert Nicholas , Gollahon Lauren , Xu Changxue 
Published: August 2024

Abstract
In the case of cancer studies, comprehension of the processes and signals that regulate tumor invasion and metastasis requires a thorough understanding of cancer cell migration. The risk of cancer leading to patient death increases when carcinogenic cells undergo intravasation, leaving the primary site through vessel and developing secondary tumors after extravasation occurs. The relevance of the study of cell migration lies in being able to understand the mechanisms that promote and regulate cell motility until the invasion of adjacent tissues which physicochemical conditions favor cancer cell spreading. Changes in cell adhesion, cytoskeletal remodeling, and signaling pathways, in addition to interactions with the extracellular matrix in the microenvironment are part of the metastatic process that defines cancer as a multifactorial disease. Among the types of cues that modulate the behavior and migration of cells, topotaxis is based on the geometrical modification of the substrate surfaces which leads to the possibility of representing the architecture of the extracellular matrix at a micro or nanometer scale. For instance, breast tissue is complex to represent given its variety of cells, fibers, and proteins but representation of its extracellular matrix topography is becoming an important tool to approximate both normal and tumor microenvironment (TME). To represent part of the changes in structure that cells might undergo during attachment and migration within the extracellular matrix, we propose topography variation on polymeric substrates which manufacturing started with the design of micropillar array with distance gradient, followed by maskless lithographic to obtain a silicon master and finally transfer to PDMS. Corona discharge treatment was used to obtain a hydrophilic surface and enhance cell attachment. Prior to cells seeding, collagen, one of the proteins that promotes growth and progression in tumor sites, was included as substrate coating. As part of the proof-of-concept study of the mechanisms underlying progression, MDA-MB-231 as a high metastatic potential or invasive cell line was used in the experiment. Cellular morphology is studied in terms of aspect ratio and attachment time, and the preliminary migration investigation includes cancer cell migration speed with the incubation time. The resulting fundamental knowledge regarding cancer cell migration and invasion is beneficial to potential applications including early detection of cancer, inhibition of cancer cell migration and invasion, and drug delivery for cancer therapy.
Brief Paper: a Study of Forces and Energy Consumption in Constrained Cutting of OFHC Copper
Authors: Leahy Osian P. , Sencer Burak , Guo Yang 
Published: August 2024

Abstract
Constrained cutting is a novel cutting process which employs an additional constraining tool in combination of the cutting tool to enable the control of chip-forming deformation in the cutting process. In this study, a linear cutting setup equipped with force sensors was developed and used to conduct constrained cutting experiments on highly ductile OFHC copper at three controlled chip thickness ratios. The chip thickness ratio was controlled by altering the constraining tool position in the setup. The setup allows the cutting tool forces and constraining tool forces to be measured separately in the constrained cutting process. Free cutting experiment was also conducted for comparison. The results show that the total cutting force (or equivalently the cutting energy consumption) is up to 50% lower in constrained cutting than in free cutting. Both the cutting and thrust forces on the cutting tool are reduced by constrained cutting compared to free cutting. The cutting force on the constraining tool is negligibly small while the thrust force on the constraining tool is only moderate. These results indicate the energy consumption by the constraining action is negligibly small while its effect on the cutting energy reduction is substantial. The study confirms the great potential of constrained cutting as an efficient material removal process.
Effect of Layer Time Control via Physics-Based Model on Layer Temperature in Extrusion-Based Large Format Additive Manufacturing
Authors: Jo Eonyeon , Liu Lu , Ju Feng , Garg Nikhil , Ozcan Soydan , Vaidya Uday , Kim Seokpum 
Published: August 2024

Abstract
In polymer extrusion-based large format additive manufacturing (AM), the strength and structural integrity of printed parts depend on interlayer bonding, which is highly affected by layer temperature. Layer deposition with an improper layer temperature can result in undesirable defects such as warping, cracking, and debonding. A critical factor in this layer deposition process is the layer deposition time, commonly known as “layer time,” which significantly impacts layer temperature and interlayer bonding strength. For example, an excessively long layer time results in an over-cooled surface, leading to interlayer debonding, cracking, and warping. Conversely, an excessively short layer time causes overheating, leading to material collapse due to insufficient stiffness when new layers are deposited. Traditionally, layer time have been determined based on the experience of skilled operators, employing a fixed rate throughout the entire printing process. This approach often fails to address dynamic changes during the printing process. Furthermore, it is limited by the absence of comprehensive guidelines based on accurate data and systematic analysis. Determining the best optimal layer time is further complicated by the influence of variable environmental conditions, geometric complexities, and printing parameters. There exists a clear need for a layer time control framework that can determine the best optimal layer time to achieve the desired layer temperature across all deposited layers, ultimately leading to an optimal printing outcome. In response to these challenges, a finite element analysis (FEA) model was used to simulate the additive deposition process. After the deposition of each layer, the simulation process paused, and the temperature data was fed into the previously developed layer time optimization framework. With the optimization framework, an optimal layer time for each successive layer was determined based on the cooling behavior of previously deposited layers. By updating the layer time based on the results from the optimization framework, the FEA model continued the deposition process continuously. The layer temperature is adjusted with the adaptive layer time control process. By implementing this approach, a mismatch between the target temperature and the simulated layer temperature was reduced from 27.8% to 4.7% for the second layer. In addition, the mismatch for the third layer was reduced from 11.7% to 0.3%. We aim to illuminate the influence of layer time control on layer temperature during polymer extrusion-based large format AM. This research will contribute to the advancement of realtime control systems in large format AM.
Assessing the Sustainability Impacts of Additive Manufacturing: A Review
Authors: Khan Md Tarique Hasan , Rezwana Saki 
Published: August 2024

Abstract
This study provides a comprehensive review of the sustainability aspects of Additive Manufacturing (AM), examining its evolution from prototyping to mainstream production with a focus on material efficiency, energy consumption, and waste reduction. It highlights the adoption of recycled and bio-based materials in AM processes as a step towards environmental sustainability. The potential of AM to streamline supply chains, thereby reducing transportation emissions, and its economic impact, including the displacement and creation of jobs, are discussed. However, the review identifies significant challenges, including technical limitations, the energy-intensive nature of certain AM processes, and the need for broader market acceptance. It calls for future research to address these shortcomings, emphasizing the importance of integrating sustainability into the core of AM development. This analysis underlines the complexity of achieving sustainability in AM, suggesting a balanced approach that considers both technological advancements and their socio-economic impacts.
Enhanced Data-Driven Virtual Metrology on Chemical Mechanical Planarization Process Using Dual Linear Kalman Filter
Authors: Rahman Amanat Ur , Han Xu , Jia Xiaodong 
Published: August 2024

Abstract
This paper explores a new virtual metrology method using Dual Linear Kalman Filter (DLKF) to track the gradual data variations in the semiconductor manufacturing process. The prediction of Material Removal Rate (MRR) in the Chemical Mechanical Planarization (CMP) is adopted to demonstrate the results. The proposed approach ensures dynamic tracking of CMP process changes and jointly updates state and parameter accordingly for the MRR prediction. This recursive nature of DLKF allows for tracking the process drifts and self-adjusting the parameters accordingly. The effectiveness of the proposed methodology for MRR prediction is tested based on the dataset from the Prognostics and Health Management (PHM) Data Challenge 2016. The prediction performance will be evaluated and benchmarked again with other peer algorithms that are used in practice.
Brief Paper: Physics-Guided Scan Paths Optimization for Controlled Microstructure in Laser Powder Bed Fusion
Authors: Twumasi Augustine , Roy Prokash Chandra , Choi Jin Young , Gan Zhengtao 
Published: August 2024

Abstract
Laser Powder Bed Fusion (L-PBF) is a renowned additive manufacturing technique, celebrated for its capability to construct intricate metal components with remarkable precision. However, one of the main challenges with L-PBF is the formation of complex microstructures, which can significantly impact the final material properties. To address this issue, our study proposes a physics-guided and machine-learning-aided approach to optimize scan paths for achieving desired microstructure outcomes, such as the generation of equiaxed grains that enhance material properties. By using phase-field modeling, a physics-based computational method, we gain insights into microstructure evolution. To reduce computational costs, we train a surrogate machine-learning model using a 3D U-Net convolutional neural network and single-track phase-field simulations as dataset. This enables the machine learning model to predict crystalline grain orientations accurately based on the initial microstructure and thermal history. As a preliminary approach, we investigate three primary scanning strategies — vertical serpentine, spiral serpentine and diagonal scanning — at various hatch spacings to identify the most effective paths for achieving the desired microstructure. This lays the foundation for a comprehensive examination of how different scan paths and parameters affect the resulting microstructure. By combining this strategic analysis with our advanced modeling techniques, we provide insights into how scan path influences the attainment of optimal crystalline grain structure in L-PBF processes. This approach not only enhances our ability to predict microstructural outcomes but also advances the precision manufacturing capabilities of L-PBF, merging theoretical knowledge with practical application to guide future advancements in additive manufacturing. Importantly, our methodology achieves a computational time reduction by approximately three orders of magnitude, underscoring the efficiency of our ML approach in accelerating the design process.
MOF-Laden 3D Printed Monolithic Filter Design and Manufacturing
Authors: Zia Akhter , Khoda Bashir 
Published: August 2024

Abstract
In recent years, 3D printing, a highly promising additive manufacturing technique, has garnered significant attention as it allows the addition of fused materials layer by layer, to create intricate structures with ease and also offers the flexibility to incorporate multiple materials within the same structure simultaneously, reducing production time and costs. The versatility of 3D printing has led to its application in diverse fields, including nanotechnology. For nanotechnological applications, the manipulation of the 3D printing process to achieve specific patterns or designs is crucial for desired outcomes. As a result, continuous efforts are being made to advance the 3D printing process. Recent studies have focused on varying manufacturing process parameters to analyze their impact on the mechanical properties of 3D-printed products. Parameters such as raster width, raster angle, and printing speed have been identified as influencing factors, particularly on the strength of the printed items. An exciting development in the application of 3D printing in nanotechnology involves integrating the synthesis of metal-organic frameworks (MOFs) into the 3D-printed porous structure. This innovation has opened up new possibilities, especially in fields like water treatment. The synthesis of Cu-based MOFs on 3D-printed porous PLA structures has been explored in this study, with different raster angles and air gaps. Decreasing the air gap and low raster angle have been found to increase MOF growth, and vice versa. Dispersion of MOFs reaches the maximum with an increase in air gap before starting to decrease for any further addition of air gap. Optimizing manufacturing parameters can maximize MOF availability, leading to enhanced adsorption performance for water treatment.
A Mobile Additive Manufacturing Robot Framework for Smart Manufacturing Systems
Authors: Li Yifei , Park Jeongwon , Manogharan Guha , Ju Feng , Kovalenko Ilya 
Published: August 2024

Abstract
Recent technological innovations in the areas of additive manufacturing and collaborative robotics have paved the way toward realizing the concept of on-demand, personalized production on the shop floor. Additive manufacturing process can provide the capability of printing highly customized parts based on various customer requirements. Autonomous, mobile systems provide the flexibility to move custom parts around the shop floor to various manufacturing operations, as needed by product requirements. In this work, we proposed a mobile additive manufacturing robot framework for merging an additive manufacturing process system with an autonomous mobile base. Two case studies showcase the potential benefits of the proposed mobile additive manufacturing framework. The first case study overviews the effect that a mobile system can have on a fused deposition modeling process. The second case study showcases how integrating a mobile additive manufacturing machine can improve the throughput of the manufacturing system. The major findings of this study are that the proposed mobile robotic AM has increased throughput by taking advantage of the travel time between operations/processing sites. It is particularly suited to perform intermittent operations (e.g., preparing feedstock) during the travel time of the robotic AM. One major implication of this study is its application in manufacturing structural components (e.g., concrete construction, and feedstock preparation during reconnaissance missions) in remote or extreme terrains with on-site or on-demand feedstocks.
Characterization of Elastoplastic Properties of Additively Manufactured Specimens From Indentation Data Using Stochastic Inverse Modeling
Authors: Olabiyi Ridwan , Weaver Jordan , Iquebal Ashif 
Published: August 2024

Abstract
Rapid characterization of mechanical properties and material structure of additively manufactured (AM) components via non-destructive techniques (NDT) is crucial for their wider adoption. However, accurate characterization of AM components using NDT remains a challenge. To this end, our work focuses on characterizing the elastoplastic properties of AM components from instrumented indentation measurements, addressing the inverse indentation problem. Previous approaches to this problem have limitations in generalization or in estimating the variability of elastoplastic properties. In this work, we explore a stochastic inverse problem (SIP) formulation, estimating a distribution over elastoplastic properties (Young’s modulus, yield strength, and strain hardening exponent) that aligns with observed indentation data. Implementing this methodology for AM components subjected to different heat treatments, we achieve predictions of the strain hardening exponent (n), Young’s modulus (E), and yield strength (σy) to within 1.1%, 1%, and 5% of the actual values, respectively. The recovered distributions closely match those from standard tensile tests, indicating our methodology’s accuracy in characterizing mean elastoplastic properties and their variability through high throughput indentation measurements.
A Physical Comprehensive Model for Studying Temperature Evolution in FDM 3D Printing
Authors: Sun Haofan , Albert Magnas Joseph , Remani Srikar Anudeep , Guo Shenghan , Nian Qiong 
Published: August 2024

Abstract
Along with emerging engineering requirements in multiple fields, how to fully realize the three-dimensional (3D) design has become a critical manufacturing challenge. To tackle this challenge, there is continuous research in recent years for development of additive manufacturing processes, that is 3D printing techniques. Fused deposition modeling (FDM) is one among the most extensively studied 3D printing techniques that generate 3D models, prototypes and especially end products from polymers. FDM printer uses a continuous filament of thermoplastic polymer or its nanocomposite, which is fed to the heated printer extruder head and then deposited onto the growing work. This technology offers multiple advantages over existing techniques, including fabricating complex designs, multi-material printing, rapid prototyping, high spatial resolution, on-site and on-demand production, as well as efficient material utilization with little or no waste. For example, according to existing literatures related to the FDM technique, various polymers, and their composites, such as ABS, PLA, PE, PEEK, Nylon, etc., have been designed and applied to build functional structures. In spite of current research and advancement, there are still limitations and disadvantages reported regarding FDM, e.g.., the lower strength exhibited in FDM parts compared to those by injection molding or compression molding techniques. Reasons could be explained by the insufficient bonding between the adjacent layers and the distorted shape of extruded polymers while printing, which hence needs to be improved by controlling the temperature related parameters, such as the speed of printing, temperatures of nozzle and bed. Therefore, validated by the videos from thermal camera, we here propose a new physical comprehensible model to simulate the temperature evolution during FDM 3D printing. In specific, we use finite element method (FEM) to build a heat transfer model with element activation function in ANSYS. Each element simulation is activated for multiple layers with over 1000 elements each layer at the bottom, middle and top of the sample to simulate the drop-by-drop and layer-by-layer extrudate “printing” behavior. The mesh temperature and element initial temperature are set as the same values to the bed and nozzle temperature, while the time interval of adjacent elements is set according to the printing speed in experiments. Thermal videos are generated by FLIR A320 Tempscreen from Teledyne FLIR. To further prove the accuracy of the FEM model, we collect and compare the temperature plots of the same area in thermal video and simulation. This work not only introduces a new physical comprehensive FEM model for FDM 3D printing process but also shed light on the printing process optimization and potential new future process development.
Laser Hairpin Stripping With a New Generation CO2 Laser at Lower Wavelength for E-Drive Manufacturing
Authors: Caprio Leonardo , D’Arcangelo Simone , Chesi Davide , Nocciolini Daniele , Corbinelli Rubino , Previtali Barbara , Demir Ali Gökhan 
Published: August 2024

Abstract
Electric drives are conventionally produced with copper hairpin windings. Rather than the flexible winding of thin wires, Cu bars with typically rectangular cross section are separately bent into the desired form and inserted to the stator to form a continuous winding. The open ends to be connected generate the so-called hairpin couples. These regions should be free of the insulating polymer coating in order to apply the successive laser welding operation successfully. The presence of polymeric material in the welded area generates material bursts, spatter, and pores due to the very low vaporization temperature compared to the welded metal. Laser hairpin stripping removes the electrically insulating enamel layer and ensures a clean surface for the successive laser welding operation that produces the contacts. Several types of pulsed wave lasers can be operated producing different types of material removal mechanisms from surface evaporation to material expulsion. CO2 lasers have been long employed in the industry for processing polymeric materials thanks to the high optical absorptivity at IR region. High power CO2 lasers sources can be an appealing solution for the hairpin stripping process combining the high absorption by the polymeric enamel and the low absorptivity of underlying copper. New generation of CO2 lasers at lower wavelengths (i.e. 9.4 μm) can provide relatively smaller spot sizes and possibly enhanced processing. This work proposes a systematic analysis of the process development starting from single pulsed material removal towards areal scanning. The processed material was Polyester (Amide) (Imide) overcoated with Polyamideimide enamel material with 95 μm thickness on ETP Cu (electrolytic tough pitch copper) bars. A CO2 laser operating at 9.4 μm wavelength with 400 W maximum average power was employed. The laser produced μs-long pulses by regulating the pulse repetition rate in the kHz level along with the duty cycle. First the material removal was evaluated at maximum output power and duty cycle varying the pulse duration and the scan speed. The conditions were analyzed categorically assessing partial and complete material removal. Multiple pass scan strategies were developed to remove material in larger areas. The most productive solutions as well as the one providing the surface with the least amount of material remaining on the surface were chosen for further evaluation. Scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and focal variation microscopy (FVM) were employed to analyze the surface topography, chemistry, and roughness. Using the chosen conditions hairpin couples were stripped and laser welded using a high-power active fiber laser. The quality of the hairpin stripping operation was therefore evaluated also through the outcome of the welding operation in terms of bead geometry and its mechanical strength.