bluuebunny/crossref_metadata_2025 huggingface dataset:
Sparse Convolution-Based 6D Pose Estimation for Robotic Bin-Picking With Point Clouds
Authors: Zhuang Chungang , Niu Wanhao , Wang Hesheng
Published: August 2024

Estimating the orientation and position of objects is a crucial step in robotic bin-picking tasks. The challenge lies in the fact that, in real-world scenarios, a diverse array of objects is often randomly stacked, resulting in significant occlusion. This study introduces an innovative approach aimed at predicting 6D poses by processing point clouds through a two-stage neural network. In the initial stage, a network for scenes with low-textured environments is designed. Its purpose is to perform instance segmentation and provide an initial pose estimation. Entering the second stage, a pose refinement network is suggested. This network is intended to enhance the precision of pose prediction, building upon the output from the first stage. To tackle the challenge of resource-intensive annotation, a simulation technique is employed to generate a synthetic dataset. Additionally, a dedicated software tool has been developed to annotate real point cloud datasets. In practical experiments, our method demonstrated superior performance compared to baseline methods such as PointGroup and Iterative Closest Point. This superiority is evident in both segmentation accuracy and pose refinement. Moreover, practical grasping experiments have underscored the method's efficacy in real-world industrial robot bin-picking applications. The results affirm its capability to successfully address the challenges produced by occluded and randomly stacked objects.
Modeling of Fiber Optic Acoustic Coupler for Ultrasonic Sensing
Authors: Kim Jee Myung , Peters Kara
Published: August 2024

Fiber Bragg grating sensors have been applied in the remote-bonding configuration for structural health monitoring, in which ultrasonic modes are propagated along the optical fiber to the sensor. Recently, coupling of the ultrasonic mode from optical fiber to optical fiber through an adhesive coupler has also been demonstrated. This paper develops a finite element (FE) model to describe the coupler behavior as a function of the fiber and coupler properties, for future optimization of couplers. The FE model is validated with previous experimental data. We also compare the FE model to three theoretical models (spring and damper frictional contact models and coupled mode theory). The results show that the FE model and spring well replicate the experimental results. Future work will use the resulting FE and spring models to derive solutions for the coupling coefficient as a function of the geometrical and material parameters of the coupler.
Technological Limit of Solar Concentration Technique Applied to Hybrid Photovoltaic-Thermal Solar Collector Equipped With Thermoelectric Generator Incorporating Ternary Nanofluid
Authors: Mouaici K. , Fersadou B. , Arslan K. , Kahalerras H. , Traiche M.
Published: August 2024

This paper aims to explore the technological limits of the synergy between the solar concentration technique, facilitated by a parabolic concentrator, and the thermoelectric effect induced by a thermoelectric generator within a hybrid photovoltaic-thermal collector, incorporating a ternary nanofluid Cu–Al2O3-MWCNT (multiwalled carbon nanotubes)/water. Each system component is subject to energy balance equations, and the Runge–Kutta fourth-order method is employed to solve the resultant mathematical model. The effects of the concentration ratio (CR), the mass flowrate ṁ, and the type of heat transfer fluid on the system's performance are scrutinized. The simulations are carried out under the meteorological conditions of Ain Salah City in southern Algeria for a moderate wind velocity. The results show better performance when operating ternary nanofluid than other heat transfer fluids. Moreover, the outcomes indicate that by using a 2% volume fraction of nanoparticles of ternary nanofluid, the thermal output, electrical yield, and thermoelectric production reach enhancements of 14.5%, 11.2%, and 22.6%, respectively. Incorporating the solar concentrator resulted in a 3.54 and 5.88 times increase in electrical and thermal powers, respectively. With the growths in ṁ, the temperature of the photovoltaic panel decreases by 53 °C, and the electrical efficiency improves by 34.5%. Correlations encompassing the concentration ratio and mass flowrate for various types of heat transfer fluids are established to predict the technological limits of solar concentration technique in photovoltaic-thermal-thermoelectric generator collectors under the meteorological conditions specific to Ain Salah.
The Antiwear and Friction-Reducing Performance of Magnesium Silicate Hydroxide/Graphite Nanocomposite as a Lubricant Additive
Authors: Wang Kai , Chang Qiuying , Gao Kai , Wang Bin , Gao Rongqin , Yan Qingqing
Published: August 2024

In this article, magnesium silicate hydroxide-graphite (MSH-C) and magnesium silicate hydroxide-graphene oxide (MSH-GO) composites were prepared by doping magnesium silicate hydroxide (MSH) with micron-sized few-layer graphene oxide (∼10 µm) and nanoscale graphite (∼300 nm), respectively, during the hydrothermal synthesis process. Octadecyltrimethoxysilane (ODTMS) was used as a surfactant to disperse these two types of additives into the fully formulated oil. Tribological tests demonstrated that MSH-C exhibited superior tribological properties compared to MSH-GO. The findings reveal that the oil sample containing 0.5 wt% MSH-C can effectively reduce wear volume by approximately 26.4% compared to fully formulated oil, with the friction coefficient decreased to ∼0.02. scanning electron microscopy (SEM), Raman, focused ion beam-transmission eletron microscope (FIB-TEM), and time of flight secondary ion mass spectrometry (TOF-SIMS) characterizations, along with molecular dynamics simulation, were used to investigate the tribological mechanism. A tribofilm composed of two layers of different compositions was formed on the worn surface of MSH-C, indicating the synergistic effect of MSH and graphite.
Optimization of Oil Bores on Connecting Rod Small End to Prevent Bushing Failures at Maximum Speed
Authors: Fu Hongyu , Zhang Hao , Niu Penghao , Fan Zhihan , Gao Zhonghao
Published: August 2024

The bushing of connecting rod small end is one of the most prone components to failure in diesel engines. According to the previous study, the oil flowrate and storage of small-end bearing with splash lubrication are both minimal at maximum speed. Thus, the optimization of oil bores was performed using a model based on smooth particle hydrodynamics. By adjusting the angle between the axes of two oil bores to 90 deg, the oil flowrate and storage increase from 0.036 mL/s and 0.04 mL to 0.094 mL/s and 0.045 mL, respectively. A semicolumn baffle above the oil bore away from the piston cooling nozzle further increases them to 3.564 mL/s and 0.8 mL. The optimization greatly enhances the cooling intensity and oil supply stability of small-end bearing, conducing to prevent bushing failures.
Probing the Synergy of Calcium Sulfonate Complex Soap and Polyurea Complex Thickener Toward Good Lubrication and Rheological Properties
Authors: Zhang Hong , Mo Yimin , Liu Changye , Wang Jun
Published: August 2024

Polyurea complex thickener was introduced into calcium sulfonate complex grease (CSCG) to prepare a new type of grease. The microstructure, rheological properties, and tribological properties of new greases were studied in detail. The experimental results show that the combination of calcium sulfonate complex soap and polyurea complex thickener can form a new thickener system, which combines the layered structure of calcium sulfonate complex soap and the rod-shaped structure of polyurea complex thickener. The thermal decomposition temperature of new greases is reduced, but their fluidity is improved and the friction coefficient is lower. Their synergistic effect is related to the special structural skeleton of the new thickener system, and calcium sulfonate and urea molecules share the same calcium ion, which may be an important reason for maintaining the stability of this special structure skeleton.
Piezoelectric Impedance-Based Structural Damage Identification Empowered With Tunable Circuitry Integration and Multi-Objective Optimization-Based Inverse Analysis
Authors: Zhang Yang , Dupont Joshua , Wang Ting , Tang Jiong
Published: August 2024

The piezoelectric impedance-based technique is increasingly recognized for its promise in structural health monitoring and damage identification. Built upon their self-sensing actuation capability, piezoelectric transducers can be integrated into host structures to acquire the system-level impedance information in a high-frequency range with a small wavelength. Furthermore, the frequency-sweeping harmonic excitations in impedance measurements lead to the potential for model-based inverse identification of damage location and severity. A major challenge in damage identification, however, is that the inverse analysis is generally underdetermined, as the measurement information may not be adequate to yield a unique solution. In this research, a new methodology of tunable sensing in conjunction with multi-objective optimization inverse analysis is established. Taking advantage of the two-way electromechanical coupling of piezoelectric transducers, tunable inductance is integrated into the measurement circuit. For the same damage scenario, by tuning the inductance to a series of values, a family of impedance measurements can be acquired. Meanwhile, the inverse analysis is cast into a multi-objective optimization problem, aiming at minimizing the difference between measurement and model prediction and achieving sparsity in damage index vector. A Q-learning-based multi-objective particle swarm optimization is synthesized to reach a small yet diverse solution set. We report the circuitry integration details as well as the algorithm enhancement with systematic case investigations. It is validated that the new methodology with enriched measurement can produce a smaller solution set encompassing the true damage scenario, thereby providing vital information for diagnoses and prognosis.
On Dynamic Analysis and Prevention of Transmission Squawk in Wet Clutches
Authors: Ali Junaid , Natarajan Bharat , Horen Jezrah , Evans John , Shaver Gregory , Bajaj Anil K.
Published: August 2024

The genesis of clutch noise, encompassing squeaks, chatter, shudders, or judders, predominantly arises from friction-induced vibrations. As time progresses, the degradation of the clutch friction lining manifests due to diverse factors, such as the smearing of surface irregularities, elevated transmission fluid temperatures, fluctuating axial pressure, or aggressive shifting. Consequently, the slope of the friction curves tends to assume a negative gradient, giving rise to adverse damping effects like self-excited oscillations or stick-slip oscillations within the clutch pack. A lesser-discussed phenomenon, known as squawking, occurs through analogous mechanisms discussed in this paper. This paper delves into investigating the occurrence of squawking noise observed in automatic transmission multi-disc clutches during low-speed up-shifts. The study discerns friction-induced vibrations as the primary contributor to squawk during the inertia phase of the clutch engagement cycle, a facet previously unidentified. During this phase, high-frequency weakly damped oscillating modes become self-excited due to the negative slope of the coefficient of friction versus slip speed curve. The coefficient of friction functions as negative damping during the inertia phase, where the energy dissipation from damping is insufficient to completely halt the oscillations, allowing them to persist approximately at the natural frequency until clutch lock-up. Experimental data validate the proposed model and hypothesis, with results closely aligning with numerical simulations. The paper concludes by offering practical suggestions to prevent and mitigate squawk in automatic transmission wet clutches, with the aim of enhancing overall performance and reducing undesirable noise.
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

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

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

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 <100> 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

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

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

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

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

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

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

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

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

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

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

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

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

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.