bluuebunny/crossref_metadata_2025_split huggingface dataset:
Numerical Analysis of the Tread Grooves’ Acoustic Resonances for the Investigation of Tire Noise
Authors: Rapino Luca , Ripamonti Francesco , Baro Simone , Corradi Roberto 
Published: August 2024

Abstract
Tire/road noise is one of the major sources of noise pollution in urban areas. This phenomenon can be described as the combined effect of several generation and amplification mechanisms. Acoustic resonances due to the tread grooves at the footprint region, also known as pipe resonances, are particularly important since they can generate high noise levels if they combine with harmonic generation mechanisms. This paper deals with a numerical investigation of this phenomenon and describes modeling approaches that might be used to estimate the frequencies and the mode shapes of these resonances.
The Effect of Laser Re-Melting on Surface Quality and Mechanical Properties of LPBF Martensitic Stainless Steel: Preliminary Results
Authors: Ali Majed , Almotari Abdalmageed , Algamal Anwar , Qattawi Ala 
Published: August 2024

Abstract
Metal Additive Manufacturing (AM) has made significant strides in improving the mechanical characteristics and microstructures of fabricated materials. AM components often require post-processing operations, such as heat treatment to induce material homogenization and improve materials properties. The martensitic stainless-steel family is widely recognized for its excellent corrosion resistance, stiffness, and high tensile strength. It can be found in extensive applications such as aerospace, automotive, chemical, and food processing industries. However, given the inherent surface roughness of AM components, additional surface treatment processes are often required. The incorporation of laser remelting as a cutting-edge method in laser-based AM is geared toward reducing porosity and refining surface roughness. Yet, there is a significant gap in understanding how laser remelting influences mechanical properties and microstructure. Laser Powder Bed Fusion (LPBF) presents an opportunity for laser re-melting after each layer is deposited, thereby enhancing both surface quality and mechanical properties. The laser path can be programmed to traverse each fused layer with varying laser power and speed. This approach results in remelting and improved sintering, ensuring that all powder particles are effectively melted and enhancing the overall integrity of the fabricated materials. This study focuses on optimizing the process parameters for laser remelting to improve surface roughness and hardness. Surface roughness measurements are performed using digital microscopy, while Vickers hardness tests are conducted. Twenty-seven cubes were fabricated with three processing parameters factors and three levels. The factors are laser power, laser speed, and number of laser remelting passes, and their levels are 150W, 175W, and 200W; 900mm/s, 1200mm/s, 1500mm/s; one pass, two passes, and three passes, respectively. The findings reveal that laser re-melting significantly enhances hardness, and the outcome is contingent upon laser power and laser speed settings. The combination of a laser power of 150W, a laser speed of 1200 mm/s, and a single pass for laser re-melting results in the highest hardness, achieving 401HV, compared to the initial as-built value of 368HV. The main effect plot revealed that the number of laser remelting passes was not significant in terms of hardness but was significant in terms of surface roughness. The laser remelting process also leads to a substantial improvement in surface roughness when contrasted with the as-built sample. The most favorable surface roughness results are achieved using a laser power of 175W, a laser speed of 900 mm/s, and three passes of laser re-melting, yielding a surface roughness of 7.1 μm, as opposed to the initial as-built value of 45 μm.
Brief Paper: Deciphering the Effect of Part Thermal History on Microstructure and Mechanical Properties in Laser Powder Bed Fusion of SS316L
Authors: Deshmukh Kaustubh , Riensche Alexander , Lane Ryan J. , Snyder Kyle , Williams Christopher B. , Mirzaeifar Reza , Rao Prahalada 
Published: August 2024

Abstract
The main research goal of this study is to decipher the intercorrelation between process-induced thermal-structure-property relationships of Stainless Steel 316L fabricated by laser powder bed fusion. The objective therein is achieved by explaining and quantifying the effect of processing parameters and part-scale thermal history on microstructure evolution and mechanical properties of these parts. Multiple previous works have correlated the effect of process parameters on flaw formation, microstructural features evolved and functional properties; however, a lack of understanding remains in the underlying effect of the thermal history on part microstructure and mechanical properties. The thermal distribution, or thermal history, of the part as it is being built layer-by-layer is influenced by the processing parameters, material properties and shape of the part. The thermal history influences the microstructure by changing the grain structure evolution, which affects the part properties. Therefore, the novelty of this paper lies in illuminating the process-thermal history-microstructure-property relationship in laser powder bed fusion.
Characterization of tensile specimens processed at a variety of conditions reveal a direct influence of the choice of process parameters on the dendritic structure and the grain orientations. A high energy density leads to &lt;100&gt; textured columnar dendritic grains and low energy density leads to randomly oriented equiaxed grains as a result of the shifting heat influx. The tensile properties are correlated with the inherent microstructure. Through future work involving fracture surface analysis, the texture, grain size and porosity is expected to influence the inherent fracture mechanism.
This work demonstrates that an understanding of thermal distribution within a printed part can inform the choice of processing conditions to generate the final microstructure as per the specified functional requirements. Thus, this paper lays the foundation for future prediction and control of microstructure and functional properties in laser powder bed fusion by identifying the root fundamental thermal phenomena that influences the microstructure evolution and part properties.
Polypropylene and Glass Fiber Composite Extrusion for Additive Biofabrication of Bone Tissue Scaffolds With Complex Microstructures
Authors: Al-Qawasmi Hamzeh , Risch Sebastian , Salary Roozbeh “Ross” 
Published: August 2024

Abstract
Osseous fractures account for 16% of all musculoskeletal injuries in the U.S. annually. Various tissue engineering methods have emerged for bone repair, including additive biomanufacturing techniques like extrusion-based bioprinting. Despite technological and scientific advances in bone tissue engineering, it has remained unknown how the complex rheological dynamics of composite material deposition affect the functional properties of fabricated bone scaffolds.
The goal of this work is to fabricate mechanically robust, dimensionally accurate, and biocompatible tissue scaffolds for treatment of bone fractures. The objectives of the work are to investigate the influence of (i) single-screw filament extrusion temperature and (ii) internal scaffold microstructures, on the physical and mechanical properties of bone scaffolds, fabricated using fused deposition modeling (FDM).
Uniform monofilaments of polypropylene (PP) and glass fibers (GF) were extruded at temperatures of 185 °C, 210 °C, and 235 °C, then used to fabricate porous bone scaffolds via FDM. Also, four scaffolds with bone-like microstructures were designed, based on novel mathematical formulations of triply periodic minimal surfaces (TPMS). The physical and mechanical properties of these scaffolds were characterized to identify optimal fabrication and design parameters.
Among the four TPMS designs constructed, Design #2 exhibited the highest compression modulus, attributed to its compact microstructure. Besides, extrusion temperatures of 210 °C and 235 °C had similar effects on scaffold properties compared to 185 °C. These findings contribute to the development of clinically viable bone scaffolds and future advancements in regenerative medicine.
Evaluation of Single Wall Builds Using Wire Arc Additive Manufacturing
Authors: Wall Emily S. , Sharma Gehendra , Freeman Elton , Jarrell Walker , Austin Joel 
Published: August 2024

Abstract
Wire Arc Additive Manufacturing (Wire Arc AM) offers advantages in producing large-scale components at lower costs, making it a promising technology. It holds the promise of revolutionizing the manufacturing industry, offering a versatile approach for producing intricate and large-scale metal components. In this paper, we present initial findings from our ongoing research project that is centered on investigating the potential of using ER 120S-G steel as a printing material for part designs featuring diverse geometric features in order to assess the manufacturability of part designs using Wire Arc AM. The selection of ER 120S-G steel as the material for this study was deliberate. This steel, known for its weldability and high strength, was chosen because of its history of application in welding high-strength steel structures. This paper focuses on a multi-faceted approach involving the analysis of four distinct builds. There are many aspects that effect the overall quality of a part that is built using Wire Arc AM that include Process Parameters, Machine Setup, Process, Build Quality, and Process Capability. At first, we systematically evaluate the impact of machine parameters, gas compositions, wire feed speed, and trigger modes on single layer bead quality. We used the knowledge from the single layer bead study to print four single wall builds. Each build tested a different machine or process parameter, while also striving to mitigate quality defects. Build 3, aiming for a final height of 95mm, marked a substantial improvement in build quality, showcasing the smoothest walls and overall top-level smoothness. Although each build presented unique challenges, they collectively contributed to the ongoing refinement of the machine and process parameters needed to achieve the desired geometry. Finally, we present the results from the four builds highlighting the importance of precise machine setup and parameter selection. This research provides valuable insights to optimize Wire Arc AM parameters for good quality of geometric features and represents a significant step forward in our understanding of Wire Arc AM with ER 120S-G steel. The insights gained from the systematic evaluation of the four single wall builds, machine setup, material selection, parameter optimization, and analysis of builds will serve as a valuable foundation for future research in the field. This project sets the stage for the development of design rules and best practices tailored for Wire Arc AM, with the future work focused on establishing manufacturability design rules, creating a formalized assessment for future designs to determine overall manufacturability risks for using Wire Arc AM, and enhancing Wire Arc AM capabilities.
Energy Prediction Model for the Sustainability of Continuous Fiber Reinforced Polymers Made by Fused Deposition Modeling
Authors: Price Garrett , Wen Zhuangru , Tang Yunlong , Dong Guoying 
Published: August 2024

Abstract
Fused deposition modeling (FDM) is a popular Additive Manufacturing (AM) technique thanks to its low cost and high availability. However, conventionally, this process has been limited to fabricating prototypes because of the poor mechanical properties of the materials used. To address this issue, composite materials such as Short Fiber Reinforced Polymers (SFRP) and Continuous Fiber Reinforced Polymers (CFRP) have been introduced in the FDM process. Although the mechanical properties, including the elastic modulus and tensile strength, can be significantly improved by adding fibers, especially continuous fibers, it is unclear how the addition of fiber will affect the sustainability of the part over its life cycle. During the printing of continuous fiber, the feed rate of the nozzle is only one-fifteenth of the feed rate of the nozzle when printing plastics. The printing time of a CFRP part will be much longer than the printing time of a pure plastic part, which increases the energy consumption of printing a CFRP part. Therefore, before understanding the sustainability of CFRP made by FDM in its life cycle, it is indispensable to calculate the energy consumption during the fabrication process. The energy consumption can be calculated by measuring the power of the machine during the printing process. However, this method takes a lot of time and effort, which makes it inconvenient to evaluate the energy consumption for a new part without printing it. In this study, an energy prediction model is developed that can estimate the energy consumption for CFRP made by the FDM process. First, three parts are made by the FDM machine with different fiber designs. The real-time power is measured using a Kasa Smart Wi-Fi Plug Slim along with a Raspberry Pi to store and export the data. The G-codes that are used to fabricate the parts and the power log are benchmarks to develop the energy prediction model. Then, a G-code analyzer is developed using Python to set a timeline for the G-code, which can indicate the time used by each line of the G-code to print the part. After that, the power log and the G-code are matched together by the time. The power level at different stages of the printing process is analyzed. Finally, the energy prediction model is developed to estimate the power history of the fabrication process of CFRP made by FDM. It can directly read the G-code, plot the power history curve, and calculate the total energy consumption. With this model, there is no need to print the part and measure the power history to calculate the energy consumption. This model can be used in the life cycle inventory analysis of CFRP made by FDM, which can help to understand their sustainability.
Entrapped Powder Removal Using a Graph-Based Orientation Sequence Optimization for Powder Bed Fusion Additive Manufacturing Processes
Authors: Venugopal Vysakh , Farahmand Parisa , Satoh Gen , Anand Sam 
Published: August 2024

Abstract
Depowdering is a vital post-processing step in Powder Bed Fusion Additive Manufacturing (PBFAM). Traditionally, depowdering is carried out manually using high-velocity air jets in a vacuum-insulated setting. Also, the part is induced with vibration forces to agitate powder particles compactly packed in tight crevices. Apart from the obvious risks in handling metal powder particles, entrapped powder particles tend to negatively affect the part quality as they interact with the as-built part during the PBFAM heat treatment post-processing stage. In summary, the effective and complete removal of entrapped powder is critical to safely handling parts and achieving superior part quality. This paper aims to automate the entrapped powder removal process using computational geometry and graph search algorithms; the objective is to find the optimum sequence of orientations that results in the complete depowdering of a part manufactured using the PBFAM process. Initially, an automatic method to detect the internal channels in a given Computer Aided Design (CAD) part file is provided. The internal channels can be detected and separated using the Boolean union and subtraction applied to the CAD body and its bounding box. The detected powder-entrapped region is converted into a triangulated mesh geometry, which is voxelized and skeletonized to form a graph data structure. The mesh skeleton, thus developed, represents the path with which the powder particles would flow along the internal channel. A ray-casting algorithm estimates the radius of the maximum inscribed sphere that would fit inside the internal channel at a particular skeletal graph location. These spheres, whose radii are embedded as node attributes to the skeletal graph, physically represent the approximate quantity of entrapped powder at that location. Subsequently, a powder flow model based on the graph structure is proposed to effectively track a powder particle’s movement inside a channel. Based on the powder flow graph model, the next best orientation to navigate the powder toward the part’s exit is identified, and the skeletal graph and its attributes are recursively modified at each intermediate orientation until all the entrapped powder particles are virtually removed from the part. Two non-trivial test cases with complex internal channels are presented to test the robustness of the proposed algorithm. The main impact of the proposed research is that this framework enables the designers to consider depowdering challenges in PBFAM during the early design stage of a part. By modifying the geometric features based on the number of orientation setups required for complete depowdering, the post-processing phase of the PBFAM process can be planned in a cost and time-efficient manner.
A Study on Digital Twin for Autonomous Robotic Chip Removal Deep Learning Framework on CNC Machine
Authors: Han Changheon , Lee Hojun , Lee Jiho , Jun Martin Byung-Guk , Yun Huitaek 
Published: August 2024

Abstract
While a Computer Numerical Control (CNC) machine automates most of the machining processes, the pre- and post-processes are still manually and inefficiently done by a human operator. Specifically, failure to eliminate chips from a worktable completely can adversely affect the machining process, leading to incorrect clamping and cutting of a workpiece and internal dimension measurements. An operator blows high-pressure air or coolant at various angles and positions in a random manner to remove the debris on a worktable, however, chips often disperse in unintended directions. Furthermore, blind spots such as corners or areas covered by other parts hinder full inspection and cleaning processes. Thus, optimizing air-blowing direction depending on the feature of chips and devising a vision system inspecting the inside of a CNC machine with diverse angles and locations is essential for an autonomous and robust chip removal algorithm. However, simulating diverse conditions in a physical CNC machine for optimizing air-blowing directions consumes many resources and may cause damage to a machine. To tackle this, this preliminary study developed a DT environment to train an autonomous chip removal deep learning model for a collaborative robot (cobot). In a DT environment including a transplanted virtual CNC machine, a chip cluster localization deep learning model was trained using a data set synthetically generated by scattering chip models in the virtual CNC model. Annotating a data set, Gray Level Co-occurrence Matrix (GLCM) energy, a texture analysis method, was implemented since it presented different values on the region with and without a chip cluster. The YOLOv8 algorithm was used to build a deep learning model of localizing chip clusters. The deep learning model predicted the coordinates of chip clusters in new cases in the DT environment correctly, and the vector from the center of the worktable to the predicted location of a chip cluster was calculated to estimate an air-blowing direction. Next, coordinate conversion from an image to a cyber space was performed for the visualization of an estimated air-blowing direction. Lastly, a virtual reality (VR) interface was utilized to record human skills cleaning chips from a worktable.
Brief Paper: Phase-Field Modeling of Grain Evolution and Recrystallization in Friction Stir Processing
Authors: Li Zixun , Gan Zhengtao 
Published: August 2024

Abstract
In this study, we develop a three-dimensional (3D) phase-field model to simulate and understand the mesoscopic grain evolution during extreme grain deformation and recrystallization in friction stir processing (FSP). FSP is a promising solid-state manufacturing technology. It uses a specially designed rotating tool that is plunged into the surface of a workpiece to generate heat and severe plastic deformation in the material without reaching its melting point. The rotating tool stirs and mixes the material along its path, leading to various microstructural changes and property enhancements. Compared to other manufacturing techniques, such as laser powder bed fusion (L-PBF), FSP produces parts characterized by improved grain refinement and reduced occurrence of defects, such as pores and hot cracks.
A characteristic feature of FSP is that the velocity field introduced by the rotating tool displaces and deforms the grains. Our model captures this dynamic process through introducing the advection equation. The result is a comprehensive picture of how grains evolve and rearrange as the FSP tool traverses the material, effectively modeling the material’s microstructure. Another feature of FSP is nucleation and dynamic recrystallization (DRX) due to large grain deformation. These phenomena are simulated in our model, where the evolution of dislocation density is modeled by the Laasraoui Jonas (LJ) model, nucleation is determined by the magnitude of the dislocation energy and DRX grain growth is involved in the phase-field framework. The synergy between these models offers valuable insights into the material’s behavior and microstructural changes. In an endeavor to make these simulations more efficient and practical, the calculation is based on the JAX GPU computing platform. It significantly enhances the computational capacity, enabling a more accurate and detailed representation of grain evolution during FSP. Furthermore, this computational approach extends to the modeling of grain evolution in another manufacturing process, L-PBF, thereby allowing for a comparative analysis of grain morphology and the underlying physical mechanisms in both FSP and L-PBF. By conducting this comparative analysis, the study uncovers the distinguishing features and performance of FSP and L-PBF. It elucidates how the grain morphology differs between the two processes. This knowledge is invaluable for the manufacturing industry, as it enables engineers and scientists to make informed decisions about which process to employ for specific applications based on their unique requirements and desired material properties.
Embedded Fused Deposition Modeling of PCL Orbital Implants With Enhanced Performance
Authors: Mitchell Kellen , Shackleford Aidan , Bandala Erick , Chai Guangrui , Jin Yifei 
Published: August 2024

Abstract
Orbital implants are necessary for reconstructing fractured orbital walls and are traditionally fabricated using titanium or polyethylene, but these materials result in medical complications such as increased risk of implant migration and hemorrhaging. Therefore, orbital implants constructed from biocompatible and biodegradable polymers have been recently researched to mitigate these risks. Material extrusion three-dimensional (3D) printing techniques, especially fused deposition modeling (FDM), can be applied to produce patient-specific orbital implants. However, current structures fabricated by FDM usually possess poor mechanical properties and high surface roughness. In this work, an embedded FDM method is designed and implemented to fabricate polycaprolactone (PCL) orbital implants with increased mechanical properties and surface morphology through the development and utilization of a temperature-stable yield-stress suspension comprised of fumed silica particles and a sunflower oil solvent. The rheological properties of the suspension were measured and tuned to produce a viable support bath material above the melting temperature of PCL. Filaments, single-layer sheets, and tensile test samples were printed to optimize the printing parameters, verify the surface morphology, and validate the mechanical properties, respectively. After that, a numerical simulation was performed to determine the mechanical robustness of the designed orbital implant model. Finally, the orbital implant was printed, measured, and implanted into a mock-up orbital socket to verify the viability of the proposed embedded FDM method.