bluuebunny/crossref_metadata_2025_split huggingface dataset:
Energy Consumption and Carbon Emissions of Additive Manufacturing Using Smart Materials: A Supply Chain Perspective
Authors: Han Muyue , Zhao Jing , Li Lin 
Published: September 2023

Abstract
The use of stimuli-responsive materials in additive manufacturing offers new opportunities for fabricating smart structures with time-evolving properties, which is termed as 4D printing. 4D-printed structures can change their shapes over time when triggered by external stimuli, such as heat, moisture, and light, which leads to promising applications in robotics, prosthetic devices, self-folding products, etc. In the current literature, material discovery and characterization have been extensively reported for 4D printing processes, along with advanced modeling and simulation from the structure-to-functionality perspectives. However, the potential impacts of 4D printing technology on supply chain sustainability, particularly concerning energy use and carbon emissions, remain largely unexplored. In this study, mathematical models are established to quantitively analyze how the adoption of 4D printing technology affects energy consumption and carbon emissions during different stages of supply chains. An exploratory comparison is performed using a protective enclosure for microcontroller board under two scenarios, i.e., the components fabricated using stereolithography with conventional resin needing manual assembly upon use, and a 4D-printed monolithic case with heat-activated self-folding on demand. Case study results indicate that the use of 4D printing contributes to more energy-efficient operations in the supply chain with an approximately 10% reduction in carbon emissions.
Surface Morphology of API 5L X65 Pipeline Steel Processed by Ultrasonic Impact Peening
Authors: Haque M. Merajul , Yeh Li-Hsin , Zhang Xing , Li Beiwen , Liao Yiliang 
Published: September 2023

Abstract
Ultrasonic impact peening (UIP) is one of the most promising surface engineering techniques to enhance the durability (fatigue life, stress corrosion cracking resistance, wear resistance, etc.) of metallic materials by imparting surface hardening and compressive residual stresses. Besides these two beneficial surface modifications, the surface morphology after UIP plays another key role in determining the material’s durability. In literature, the effect of UIP on surface morphology was studied with a focus on average surface roughness (Sa). However, the surface integrity and quality after peening are determined by not only Sa, but also surface skewness (Ssk) and surface kurtosis (Sku). In the present study, surface morphological modifications of pipeline steel processed by UIP are investigated, with emphasize on Sa, Ssk and Sku. Surface morphology was characterized using a structured light scanning system. The effects of processing parameters (energy levels and peening time) are investigated. It is found that Sa increases with the increase of energy levels and peening times, whereas Ssk value remains below zero for all peening conditions, indicating the predominance of the valleys rather than peaks on the surface. In addition, the Sku value remains below three for all measurements, which ensures the absence of extreme surface peaks, indicating the removal of stress concentration points. The in-depth hardness is also tested to observe the surface strengthening effect.
Real-Time Temperature Prediction of a Moving Heat Source Problem Using Machine Learning
Authors: Heydari Mahtab , Kung Pei-Ching , Tai Bruce L. , Tsou Nien-Ti 
Published: September 2023

Abstract
Moving heat source problem is commonly seen in many manufacturing applications, such as machining, laser cutting, welding, and additive manufacturing processes, while numerical modeling often takes time to analyze. This paper presents a neural network (NN) and linear-time invariant (LTI) system-based framework, aiming at real-time temperature prediction both spatially and temporally. Training data are generated from finite element analysis (FEA) and processed with convolution neural network (CNN) to form a surrogate model for location-dependent thermal response. LTI is used to superimpose thermal responses based on the heat source’s path and magnitude. The suitability of this framework is evaluated for materials of both low and high thermal diffusivities as well as adiabatic and nonadiabatic cases. In the training of the model, the low thermal diffusivity and high thermal diffusivity cases both showed training and testing correlations of over 99%. Overall, all validation studies show good agreement between the predicted temperature and the ground truth. More errors are seen when the material has a high thermal diffusivity (&lt; 21.7 %), and the heat is applied adjacent to the boundaries (&lt; 23.6 %).
Grinding Force Prediction Model by Discretizing Stochastic Grains
Authors: Zhang Xin , Li Xin , Zhang Xueping , Yao Zhenqiang 
Published: September 2023

Abstract
Material removal process during grinding is the accumulative effect of numerous stochastic grains’ asperity interactions with workpiece, leading to extreme difficulty to predict grinding force. The existing analytical grinding force prediction models primarily focus on representing the stochastic interactions in contact zone using average effective grains number and undeformed chip thickness or assuming ideal distribution law for the stochastic characteristics of grains, which can result in relatively large error in predicting grinding force, whereas the experimentally empirical methods are usually time-consuming and laborious. To overcome these drawbacks, the research proposed a hybrid methodology to predict grinding force by means of discretizing stochastic grains, matrix calculation and two-dimension (2D) micro-grinding simulations. Consequently, grains’ stochastic geometry, size, position distribution, and corresponding undeformed chip size in contact region were considered to guarantee the authenticity and credibility of the predicted grinding forces. Firstly, the stochastic interaction between a random grain and workpiece was converted equivalently to a series of plane-cutting processes between micro-edges with different rake angles and micro-chip layers with corresponding thicknesses. Then, the rake angle matrix of micro edges and the corresponding cutting depth matrix in contact zone were identified through matrix calculation method based on single grain discretization analysis. Followed, both matrixes were incorporated into the plane-cutting force interpolant function derived from the finite element analysis (FEA) to achieve the grinding force matrix, and further the resultant grinding force along normal and tangential directions. The cross validation with experimental grinding results in literature showed the prediction error falls within 20% to verify the authenticity of proposed model. The model considers both influences of the stochastic grain distribution in grinding tool and their interactions with workpiece. Therefore, it bears significance in improving the prediction accuracy of grinding force, and promoting a better understanding of inherent logic from micro-level abrasive-workpiece interactions to macro-level grinding force prediction during the abrasive-based machining including grinding, honing, and polishing.
Continuous Fiber Path Optimization in Composite Additive Manufacturing via a Finite Element Model With B-Spline Fiber Parameterization
Authors: Duan Molong , He Shuaiyin 
Published: September 2023

Abstract
This paper presents a finite element model of the additive manufactured part with continuous fiber reinforcement. The proposed model exploits the B-spline parameterization of the fiber path while approximating the fiber path within a triangular element to be a straight line. This simplification enables the analytical and computational-efficient calculation of the mechanical properties of the printed parts. The elements in the model are differentiated into three categories to account for the location of fiber: matrix elements, main fiber-reinforced elements, and secondary fiber-reinforced elements. Analytical formulas to calculate the geometry-dependent stiffness matrix are established. With the established finite element model with B-spline fiber parameterization, the continuous fiber path is optimized by manipulating the control points of the B-spline to reduce fiber usage while increasing the stiffness of the part. The parts with the reference and optimized carbon fiber paths are 3D printed with a continuous-fiber fused deposition method printer, with polylactic acid as the matrix material. The estimated mechanical properties of the printed parts have been verified via experimental tensile tests.
Impact of Process Parameters on Laser Beam Machining of Ceramic Material
Authors: T. Geethapriyan , C. Kailaash Pandiyan , Palani I. A. 
Published: September 2023

Abstract
Laser Beam Machining (LBM) is a procedure of directing the laser beam to generate heat energy to vaporize the interacting material on the surface of the workpiece. Ceramics are used in several applications, like hybrid circuits in the automotive industry, environment-friendly LEDs, and other electrical components. The basis of the study is to analyze the machining of alumina ceramic by LBM to enhance process parameters such as Material Removal Rate (MRR), Conicity (CO), and Overcut (OV) by inspecting machining parameters like Power (P), Transverse Speed (TS), Standoff Distance (SOD) and Gas Pressure (GP) precisely. From multiple assessments of varying SOD, high MRR, finer overcut, and conicity are achieved. The deterministic parameters for machining on alumina ceramic are the standoff distance and transverse speed. The SOD between the workpiece surface and nozzle, influences the intensity and divergence of the beam to get better overcut and conicity. At the same time, transverse speed affects the cutting time of the workpiece since increased cutting speed reduces cutting time on the workpiece resulting in high MRR.
Controlling Friction of Rods With Entrained Particles
Authors: Khalil Md Ibrahim , Tong Dezhong , Jawed Mohammad Khalid , Khoda Bashir 
Published: September 2023

Abstract
In this paper, we demonstrate a novel process of controlling the friction of a continuous rod by adhering inorganic micro-particles. A standardized continuous dip-coating protocol has been developed in our laboratory for depositing particles from a liquid carrier system to the cylindrical elastic rod substrate. Polymeric binder is used to deliver the particles as asperities over the rod substrate and by controlling their size and distribution, the coefficient of friction of the rod is determined. Tabletop experiments are designed and performed to measure the friction coefficient following the capstan equation. The friction coefficient of the coated rod increases with the increase of the particle volume fraction in the suspension. The force-extension curve of these experiments shows that the proposed process can successfully tune the friction between rods.
Process Signature Characterization and Anomaly Detection With Personalized PCA in Laser-Based Metal Additive Manufacturing
Authors: Shi Naichen , Al Kontar Raed , Guo Shenghan 
Published: September 2023

Abstract
During Laser-Based Metal Additive Manufacturing (LB-MAM), the interaction between the energy source (i.e., the laser beam) and the material generates spatter tracks along the laser scan path. The spattering behavior is the “process signature” reflecting the process stability and anomalies that potentially lead to defects in the printing results. In-situ thermography captures the spatter patterns in real-time, which are crucial evidence for process monitoring and anomaly detection in LBMAM. A critical aspect of anomaly detection in LBMAM is identifying the unique spatter pattern in a thermal image that differs from the generic spattering behavior in the current layer being deposited (i.e., “outliers”). It is equivalent to separating the “local features” and layer-wise “shared features” from the same thermal image and recognizing the local anomaly, i.e., recognizing the outlier “local features.” In state-of-the-art, thermal-image-based anomaly detection in LBMAM relies on conventional feature extraction procedures, which limit the “view” of information to each thermal image and cannot separate the layer-wise spatter pattern governing these thermal images. Consequently, the features extracted are less interpretable, and the anomaly detection method cannot distinguish the abnormality in the current process signature from the layer-to-layer evolution of spattering dynamics. Targeting the limitation in state-of-the-art literature, this study adopts personalized Principal Component Analysis (PerPCA) as a novel means for process signature characterization and anomaly detection in LBMAM. Designed for heterogeneous data with shared features, PerPCA simultaneously extracts the “local features” and “shared features” from the same image. An outlier detection strategy can be designed upon PerPCA that removes the shared features from a process signature and examines the local features at the current printing time for abnormality. The proposed method is demonstrated with selected data from the 2018 AM Benchmark Test Series from the National Institute of Standards and Technology (NIST). The results show good potential for extending the method to complex geometry built with LBMAM.
Physics-Informed Machine Learning Model for In-Process Estimation of Cutter Runout Parameters in End Milling
Authors: Vaishnav Shubham , Desai K. A. 
Published: September 2023

Abstract
Cutter runout is inevitable in machining operations involving rotary cutting tools, such as end milling. The runout parameters are usually measured offline using a dial indicator or gap measurement sensor. Alternatively, measured cutting force data can also be used to determine these parameters using a non-linear optimization technique. It is an iterative approach sensitive to measured force data and could not determine parameters online. This work proposes utilizing a machine learning algorithm to extract cutter runout parameters from real-time cutting force data. Developing a machine learning model requires a large amount of experimental data over a wide range of conditions which is costly, time-consuming, and impractical. The present work generates synthetic datasets through a physics-based Mechanistic force model instead of performing milling experiments for the model training. The synthetic training datasets generated using the Mechanistic model minimize the effects of process disturbances and measurement outliers on the prediction abilities. A set of end milling experiments are carried out to compare the performance of the proposed approach with experimentally measured parameters and the non-linear optimization model presented in the literature. It has been shown that the proposed approach has robust prediction abilities and can be effectively employed for the online estimation of cutter runout parameters.
Influence of Modified Heat Treatments and Build Orientations on the Microstructure of Additively Manufactured IN718
Authors: Almotari Abdalmageed , Alafaghani Ala’aldin , Ali Majed , Al Gamal Anwar , Abedi Hossein , Qattawi Ala 
Published: September 2023

Abstract
Additive manufacturing of superalloys has significant merits as it produces components with higher complexity and less post-machining. Inconel 718 (IN718) is a well-known high-strength nickel alloy that can be manufactured using laser powder bed fusion (LPBF). During the LPBF process, the thermal gradient and solidification rates vary, resulting in various solidification conditions and microstructures. This study aims to investigate the microstructural characteristics of IN718 manufactured by LPBF, including grain size and orientation. The presented work developed post heat treatment procedures to enhance the mechanical properties of fabricated IN718 and minimize anisotropy associated with the as-built conditions. Three different heat treatments with varying times and temperature ranges were investigated, followed by standard aging in IN718 samples fabricated in the X and Z orientations to homogenize the microstructure. Digital light microscope, X-Ray Diffraction (XRD), and hardness tests were used to characterize the microstructure of additively fabricated IN718. Results indicated that the heat treatment at 980°C improved the hardness. An additional increase in heat treatment temperature increased grain size and undesirable precipitates.
A Human Robot Collaboration Framework for Assembly Tasks in High Mix Manufacturing Applications
Authors: Dhanaraj Neel , Ganesh Niraj , Gurav Rohit , Jeon Minseok , Manyar Omey M. , Narayan Santosh , Park Jaehyun , Yu Zhao , Gupta Satyandra K. 
Published: September 2023

Abstract
Robots are mainly used in mass production applications. Recent advances in collaborative robots make it possible for humans and robots to collaborate on high-mix, low-volume applications. This paper presents a framework for human-robot collaboration in high-mix applications. Tasks that require delicate or complex operations are assigned to humans. Tasks that are straightforward are assigned to robots. Simulation is utilized to design the assembly cell by placing the robots and sensors in the cell. Computer vision is used to localize parts in the cell, and automated motion planning is used to generate robot trajectories to perform assembly tasks. The cell also tracks humans to ensure safe operations. A unique feature of the cell is its ability to detect contingency situations and alert human operators to resolve them. Humans can use a gesture-based interface to communicate with the cell. We use a satellite assembly case study to illustrate the capabilities of the cell and demonstrate the feasibility of performing complex assembly operations using human-robot collaboration.
A Versatile Point Cloud Registration Method for Quantifying Geometric Deviation in Additive Manufacturing
Authors: He Chuan , Okwudire Chinedum 
Published: September 2023

Abstract
One of the advantages additive manufacturing (AM) has over traditional manufacturing techniques is the complexity of the parts it can produce. However, this complexity creates a challenge in quantifying the geometric deviation between the desired and printed shapes due to the lack of clear reference features on the printed part. Existing methods for quantifying shape deviation in parts made via AM make simplifying assumptions about reference features, thus limiting their versatility. To address this shortcoming, in this work, the iterative closest point (ICP) algorithm is adopted to achieve accurate registration between desired and printed shapes. To enhance its versatility, the proposed method makes no assumptions about the reference features. Instead, it determines the best reference features automatically by performing ICP using a weighted sliding window. To validate the proposed method, two simulated case studies are used. In both cases, the proposed method is able to automatically determine reference features, hence accurately represent geometric deviation, without need for simplifying assumptions.
Digital Twin Simulation and Optimization of Manufacturing Process Flows
Authors: Lee Hankang , Yang Hui 
Published: September 2023

Abstract
The new wave of Industry 4.0 is transforming manufacturing factories into data-rich environments. This provides an unprecedented opportunity to feed large amount of sensing data collected from the physical factory into the construction of digital twin (DT) in cyberspace. However, little has been done to fully utilize the DT technology to improve the smartness and autonomous levels of small and medium-sized manufacturing factories. Indeed, only a small fraction of small and medium-sized manufacturers (SMMs) has considered implementing DT technology. There is an urgent need to exploit the full potential of data analytics and simulation-enabled DTs for advanced manufacturing. Hence, this paper presents the design and development of DT models for simulation optimization of manufacturing process flows. First, we develop a multi-agent simulation model that describes nonlinear and stochastic dynamics among a network of interactive manufacturing things, including customers, machines, automated guided vehicles (AGVs), queues, and jobs. Second, we propose a statistical metamodeling approach to design sequential computer experiments to optimize the utilization of AGV under uncertainty. Third, we construct two new graph models — job flow graph and AGV traveling graph — to track and monitor the real-time performance of manufacturing jobshops. The proposed simulation-enabled DT approach is evaluated and validated with experimental studies for the representation of a real-world manufacturing factory. Experimental results show that the proposed methodology effectively transforms a manufacturing jobshop into a new generation of DT-enabled smart factories. The sequential design of experiments effectively reduces the computation overhead of expensive simulations while optimally scheduling the AGV to achieve production throughputs in a cost-effective way. This research is strongly promised to help SMMs fully utilize big data and DT technologies for gaining competitive advantages in the global market.
Understanding Mechanical Response of Porous Polymeric Stamps During Large-Area Metal-Assisted Chemical Imprinting of Silicon
Authors: Dasinor Emmanuel , Sharstniou Aliaksandr , Ding Yifu , Azeredo Bruno 
Published: September 2023

Abstract
The patterning of semiconductors with complex 3D hierarchical structures having sub-100 nm resolution is propelling a new generation of optical metasurfaces for applications in optoelectronic interconnects and hyperspectral imaging. These complex structures are now manufacturable with the advent of Metal-assisted chemical imprinting (Mac-Imprint). This technique involves bringing a 3D patterned catalytic stamp into contact with a semiconductor substrate in the presence of an etching solution. Recently, a novel class of catalytic stamps made of Au-coated porous polymeric membranes was developed in order to allow for etching solution storage and mass-transport of the reactants, all of which are essential for fast and accurate Mac-Imprint. Further, the flexibility and stretchability of these porous membranes allows for the use of pressure-based actuation to bring both planar and non-planar substrates in conformal contact with the stamp, replicating the set-up of modern nanoimprinting lithography equipment. Due to the contact-based nature of the Mac-Imprint process, it is vital to understand the mechanical behavior of the stamp. Thus, this work attempts to computationally elucidate the mechanical behavior of the stamp during conformal Mac-Imprint. In particular, the elastic behavior of a stamps laminate made of commercial porous polyvinylidene fluoride (PVDF) with nominal pore sizes of 100 nm and solid polyimide (PI) is modelled using finite element analysis (FEA) in a commercial ANSYS software with material constants, extracted via uniaxial tensile test. The FEA prediction using 2D axisymmetric model is able to capture the induced biaxial stress, strain, deflection and yield of the porous PVDF. To validate simulation results, a deflection test was experimentally performed as a function of air pressure and the obtained results matched with the FEA simulation.