bluuebunny/crossref_metadata_2025_split huggingface dataset:
FPGA-Based Edge Computing Framework: Modeling of Computation Task Scheduling
Authors: Tan Jianfei , Yang Hao , Zhao Chun , Zhang Lin 
Published: September 2023

Abstract
Cloud manufacturing is a service-oriented networked manufacturing paradigm, which integrates manufacturing resources and capabilities, to enable the intelligence and digitization of the whole life cycle from design to scrap of products. The manufacturing process of complex products requires the collaboration of a large number of heterogeneous devices, the devices accessed to a manufacturing cloud can generate a bulk of data and computing requirements. Traditional cloud computing can not meet the demand for timely and efficient task processing anymore. Edge computing is a computing paradigm that enables tasks to be processed close to the edge, in order to reduce the load on the cloud, and enhances the overall responsiveness of cloud manufacturing systems. However, the computing performance and computing resources on edge nodes are limited, which cannot meet the complex computing tasks. In this paper, a modeling method of task scheduling for FPGA-based edge computing framework is proposed to load balance the edge computing network by dynamic task scheduling and algorithm hardware-based acceleration. This framework builds a task model to describe task information. The task offloading rules are decided by task information and edge nodes states, then task data is subsequently sent to the target edge node. FPGAs are introduced in the edge nodes to enhance the computing performance, which avoids the task can not be processed due to the insufficient processing capacity of a single node, in order to make the cloud manufacturing system load balance. Finally, a case is passed to verify that the proposed framework can handle the task in a timely and efficient manner.
Single Shot 3D Shape Measurement of Non-Volatile Data Storage Devices
Authors: Balasubramaniam Badrinath , Li Beiwen 
Published: September 2023

Abstract
The value of electronic waste at present is estimated to increase rapidly year after year, and with rapid advances in electronics, shows no signs of slowing down. Storage devices such as SATA Hard Disks and Solid State Devices are electronic devices with high value recyclable raw materials which often goes unrecovered. Most of the e-waste currently generated, including HDDs, is either managed by the informal recycling sector, or is improperly landfilled with the municipal solid waste, primarily due to insufficient recovery infrastructure and labor shortage in the recycling industry. This emphasizes the importance of developing modern advanced recycling technologies such as robotic disassembly. Performing smooth robotic disassembly operations of precision electronics necessitates fast and accurate geometric 3D profiling to provide a quick and precise location of key components. Fringe Projection Profilometry (FPP), as a variation of the well-known structured light technology, provides both the high speed and high accuracy needed to accomplish this. However, Using FPP for disassembly of high-precision electronics such as hard disks can be especially challenging, given that the hard disk platter is almost completely reflective. Furthermore, the metallic nature of its various components make it difficult to render an accurate 3D reconstruction. To address this challenge, We have developed a single-shot approach to predict the 3D point cloud of these devices using a combination of computer graphics, fringe projection, and deep learning. We calibrate a physical FPP-based 3D shape measurement system and set up its digital twin using computer graphics. We capture HDD and SSD CAD models at various orientations to generate virtual training datasets consisting of fringe images and their point cloud reconstructions. This is used to train the U-NET which is then found efficient to predict the depth of the parts to a high accuracy with only a single shot fringe image. This proposed technology has the potential to serve as a valuable fast 3D vision tool for robotic re-manufacturing and is a stepping stone for building a completely automated assembly system.
Comparison of Reinforcement Learning Methods for Production Control in Discrete Manufacturing Systems
Authors: Yun Lingxiang , Wang Jingwen , Xiao Minkun , Li Lin 
Published: September 2023

Abstract
The widespread deployment of smart meters and communication technologies brings opportunities to improve the adaptability and flexibility of future manufacturing systems under growing complexity and uncertainty. As a result, data-driven approaches, especially Reinforcement Learning (RL) methods, are gaining wide attention in recent years. Although in the current literature, RL approaches show superiority over traditional methods in many applications, a comprehensive review and comparison of different RL methods and their use in discrete manufacturing system control under changing environments have not yet been established. This paper first provides a literature review of RL algorithms for decision-making in discrete manufacturing systems, and then systematically discusses the underlying mechanisms of four most commonly used RL algorithms. In addition, the performance of these RL algorithms is compared by solving a production control problem, which aims to maximize the profit under time-varying production costs. The comparison results show that the single-agent RL methods with discrete action space can provide satisfactory results in small-scale systems, while multi-agent RL algorithms are more suitable to solve problems in complex, large-scale systems to remit the curse of dimensionality. This research sheds light on the impacts of RL method selection on the search for high-quality solutions in manufacturing systems, which is envisioned to drive future research on RL-supported manufacturing systems.
Inhouse Multi-Material Nozzle System Design and Fabrication for 3D Bioprinting Process: Next Step
Authors: Quigley Connor , Hurd Warren , Clark Scott , Sarah Rokeya , Habib MD Ahasan 
Published: September 2023

Abstract
Three-dimensional (3D) bio-printing is a rapidly growing field attempting to recreate functional tissues for medical and pharmaceutical purposes. The printability of multiple materials encapsulating various living cells can take this emerging effort closer to tissue regeneration. In our earlier research, we designed a Y-like nozzle connector system capable of switching materials between more than one filament with continuous deposition. The device had a fixed switching angle, was made from plastic, and was suitable for one-time use. This paper presents the extension of our previously proposed nozzle system. We considered 30°, 45°, 60°, and 90° angles (vertical and tilted) between the two materials and chose stainless steel as a material to fabricate those nozzle connectors. The overall material switching time was recorded and compared to analyze the effects of those various angles. Our previously developed hybrid hydrogel (4% Alginate and 4% Carboxymethyl Cellulose, CMC) was used as a test material to flow through the nozzle system. These in-house fabricated nozzle connectors are reusable, easy to clean, and sterile, allowing smooth material transition and flow.
Tracking and Visualization of Benchtop Assembly Components Using a RGBD Camera
Authors: Maddipatla Yashwanth , Li Jiaqiong , Zheng Yi , Li Beiwen 
Published: September 2023

Abstract
The workforce training for part assembly processes usually depends on paper-based instructions, which may not be intuitive and could take a long time for a worker to be fully proficient. This paper aims to improve the efficiency of the training for the part assembly process with the help of an RGBD camera like Kinect. Essentially, the RGBD camera tracks the assembly bench, and an associated computer graphics interface provides the assembly instructions through a virtual model of the real part on the bench. The model is displayed on a computer, and its orientation is manipulated based on its real counterpart, which is tracked by the Kinect system in real-time. A U-net-based full-flow bidirectional fusion network model was trained for real-time 6D pose estimation. The training data was primarily captured with Kinect and Apple iPad Pro’s LiDAR. The captured data was processed in Meshlab and Matlab. The training data was also synthetically enriched so the machine learning model could recognize and track the parts in various environmental settings. The virtual model visualization interface is built with the Unity gaming engine and uses the 3D models of the assembly parts. Once set up, the system works seamlessly and reduces the time an operator has to spend assembling parts containing many circuit boards and other smaller components. Our experimental testing showed high accuracy in recognizing and tracking the assembly parts with the trained model.
Composites 4.0: Enabling the Modernization of Legacy Manufacturing Assets in South Carolina
Authors: Deshpande Amit Makarand , Keskar Gautami Girish , Pradeep Sai Aditya , Farahani Saeed , Pilla Srikanth 
Published: September 2023

Abstract
Composites 4.0 is the implementation of Industry 4.0 concepts to plastics and composites manufacturing with the goal to overcome the complexities associated with these materials. Due to very complex process-structure-property relationships associated with plastics and composites, a wide range of process parameters need to be tracked and monitored. Furthermore, these parameters are often affected by the tool and machinery, human intervention, and variability. They should thus, be monitored by integrating intelligence and connectivity in manufacturing systems. Retrofitting of legacy manufacturing systems with modern sensing and control systems is emerging as a cost-effective approach as it circumvents the substantial investments needed to replace legacy equipment with expensive modern systems to enhance productivity.
The goal of the following study is to contribute to these retrofitting efforts by identifying the gap in the current state-of-the-art research, and the implementation level of Composites 4.0 capabilities in plastics and composites manufacturing. The study was conducted in two phases, first, a detailed review of the current state-of-the-art for Industry 4.0 in the manufacturing domain was conducted to understand the level of integration possible. It also helped gain insights into formulating the right questions for the composites manufacturing industry in South Carolina. Second, a survey of the plastics and composites manufacturing industries was performed based on these questions, which helped identify the needs of the industry and the gap in the implementation of Composites 4.0. The study focuses on three major composite manufacturing industries: injection molding, extrusion, and 3D printing of thermoplastic materials.
Through the survey, it was possible to identify areas and desired functionalities being targeted by the surveyed industries and concentrate research efforts to develop targeted solutions. After analyzing the survey responses, it was found that updating old protocols using manufacturer support and customized integration of cost-effective solutions like retrofit kits, edge gateways, and smart sensors were the best-suited solutions to modernize the equipment. Composites 4.0 is already being implemented for Preventive Maintenance (PM), Manufacturing Execution System (MES), and Enterprise Resource Planning (ERP) to some extent, and the focus is on process optimization and equipment downtime reduction. The inferences drawn from this study are being used to develop highly targeted, supplier-agnostic solutions to modernize legacy manufacturing assets.
Modeling and Characterizing a Novel Hybrid Infill Pattern for Additive Manufacturing
Authors: Roney Md Saidur Rahman , Ahsan AMM Nazmul 
Published: September 2023

Abstract
In this paper, a novel hybrid infill pattern is introduced denoted as Hexagonal Zigzag pattern and its mechanical properties are investigated through experiments. The novel pattern is island type with zigzag raster repeating in a regular periodic hexagonal pattern. The size of the hexagon and the infill density can be controlled by the quantity and spacing of the zigzag raster in each hexagon cell. A mathematical model on the relationship between these two raster parameters and their effect on infill density is established. This model allows the scope of manipulating the infill parameter values to customize the hexagon size and infill density. The mechanical properties of the proposed pattern are then compared with two existing infill patterns — honeycomb and zigzag — for 15.5% overall infill density through compression tests. The test results indicate that the proposed pattern has the highest absorbed energy density before densification compared to the two existing infill patterns. Besides, the compression strength of the proposed pattern gradually increases after 40% compression strain while the other patterns exhibit this phenomenon after 60% compression strain, which exhibits its improved resistance against crushing. Overall, the experimental investigation reveals that the proposed infill pattern yields comparable mechanical performance with improved energy absorption ability.
Cyber-Physical Trust Anchors in Additive Manufacturing: Secure, Low-Cost, and Educational
Authors: Maasberg Michele , Birch Brendan , Janes Daniel , Stor Kirsten , Ham Kyungmin , Butler Leslie G. 
Published: September 2023

Abstract
We report progress towards development of a cyber-physical trust anchor for additive manufacturing systems. The additive manufacturing commercial sector needs cyber-physical trust anchors to establish a secure supply chain, to detect counterfeiting and to ensure part provenance. However, the underlying technology of cyber-physical trust anchors requires optimization and spans several sectors ranging from mathematics, additive manufacturing, materials science, nondestructive evaluation, to cyber science. The fast and effective deployment of cyber-physical trust anchors requires an educational component. This project present a novel method for authenticating additively manufactured parts. Features are extracted using advanced X-ray imaging, transformed into unique identifiers, and bound with security features for cloud-based blockchain authentication. A plan for the low-cost and safe incorporation of cyber-physical trust anchor research in education is included. The anticipated outcome is an optimized trust anchor prototype and educational product suitable for interdisciplinary research and coursework to develop the workforce needed for cyber-secured physical supply chainsd.
A Numerical Investigation on Nanosecond Laser Shock Peening of Ti-6Al-4V
Authors: Matura Max , Aguilar Lesly , Ma Jianfeng , Jahan Muhammad 
Published: September 2023

Abstract
Ti-6Al-4V is the most commonly used titanium alloy in aerospace, automotive, and biomedical industries because of its excellent strength, low modulus of elasticity, high corrosion resistance, good weldability and heat treatability. In this study, numerical investigation is conducted to assess the effect of pulse energy level and operating temperature on residual stress induced on Ti-6Al-4V workpiece during nanosecond laser shock peening with 5 ns laser pulse width. The commercial FEM software package ABAQUS is employed to conduct FEM simulation of single shot laser peening of Ti-6Al-4V. Totally, eight laser pulse energy levels and six operating temperatures are selected. The FEM model for laser peening can provide insightful guidance for selecting optimal peening parameters and operating temperatures.
Real-Time Prediction of Pores and Their Lifespan in Laser Melting Using Transfer Learning
Authors: Valizadeh Mahsa , Clark Samuel , Fezzaa Kamel , Wolff Sarah 
Published: September 2023

Abstract
Additive manufacturing, a layer-by-layer method to build up parts, has gained serious attention lately; however, there are part qualification and certification bottlenecks due to various defects observed in printed parts. To overcome this issue, deep learning, a fast-growing technology, is leveraged to detect anomalies automatically. Due to the nature of deep learning algorithms, which require thousands of samples to be trained, transfer learning is offered. In this study, laser melting of 316L stainless steel to provide proof-of-concept data and training for transfer learning, with the goal to scale up the results of laser melting to proper metal laser-based additive manufacturing technologies like laser powder bed fusion and directed energy deposition. In this study, four pre-trained state-of-the-art algorithms on ImageNet are employed to be trained for additively manufactured part defect detection while having a limited dataset. The results demonstrated that pre-trained EfficientNetB7 and InceptionV3 algorithms showed promising results, successfully classifying the test data with an accuracy of more than 94%.
Real-Time Monitoring and Gaussian Process-Based Estimation of the Melt Pool Profile in Direct Energy Deposition
Authors: Lyu Jiaqi , Akhavan Javid , Mahmoud Youmna , Xu Ke , Vallabh Chaitanya Krishna Prasad , Manoochehri Souran 
Published: September 2023

Abstract
A comprehensive understanding of the melt pool behavior during directed energy deposition (DED) has become essential in identifying process anomalies and controlling the process quality. Previous studies focused on predicting the melt pool characteristics by solely using the process parameters, in this study we use real-time melt pool images to predict the melt pool characteristics. A CMOS camera is used to capture coaxial images of the melt pool during the deposition of single-track prints to improve the prediction model.
Multiple regression models are trained and compared to estimate the melt pool profile (width, depth, and height) as a function of process parameters (namely, the laser power, the powder feed rate, and the scanning speed) and features from the ellipse fitting of the real-time melt pool images. A novel image processing algorithm is proposed to extract the major axis, minor axis, and tilt. The sensitivity analysis demonstrated that combining process parameters and coaxial images can improve the prediction performance. The Gaussian Process regression showed the best performance among all the employed regression models.
Part Surface Inspection Through Semi-Supervised Learning to Overcome Limited Data Challenge
Authors: Zhou Qianyu , Tang Jiong 
Published: September 2023

Abstract
Surface inspection plays an important role in manufacturing of parts with complicated geometry. Specifically, the timely detection of minor defects, e.g., dent and scratches, in small-sized parts such as engine blades before acceptance is usually the final step of quality assurance. Current practice, however, mainly relies on human decision which is subjective and labor intense. In this research we propose an automated, image-based inspection system that utilizes robotic automation to acquire images of a part being inspected and employs machine learning to facilitate decision making. The data acquisition aims at high-resolution images that can thoroughly reflect the surface features of parts. While deep learning is leveraged, a series of advancements are designed to overcome the challenges of limited data and especially limited number of labels (i.e., parts with defects) during the initial training process. We synthesize a semi-supervised learning framework, building upon the residual neural network (ResNet) as well as the deep convolutional generative adversarial network (DCGAN), to extract features from the ground truth data and synthetic data. The performance boost of synthetic samples is investigated with different dataset sizes and validated.
Finite Element Dynamic Model and Vibration Signal Simulation of Rolling Bearing With Local Faults
Authors: Wang Tianhe , Chen Lei , Lu Hong , Wang Shaojun , Li Zhangjie , Zhang Wei , Mei Jiangnuo 
Published: September 2023

Abstract
The lack of fault samples is a key problem that restricts various fault diagnosis methods to engineering applications. However, the acquisition of measured fault samples requires the construction and commissioning of an experimental bench, which has high material and time costs. And the fault signals other than the fault components cannot be obtained. The reducer’s ball bearing 6008 serves as the object in this study. Based on the ANSYS/LS-DYNA software, this paper takes the cosine value of the simulated and measured vibration acceleration signal as the similarity measurement standard to modify the important parameters such as the radial load of the finite element model, and finally establishes the 3D explicit finite element dynamic model of the healthy bearing. After adding some fault geometry models, a variety of bearing finite element models with local faults are obtained, and the simulated fault vibration acceleration signal is obtained through the acceleration response calculation, so as to solve the problem of lack of fault samples. The fault characteristic frequency of the simulation signal can be precisely located via envelope spectrum analysis. And the simulation results can play an auxiliary role in bearing fault diagnosis in the absence of fault samples.
Complex Shaped Detection and Reconstruction Algorithm Considering Uncertain Factors
Authors: Zhang Jun , Lu Hong , Wu Zidong , Tan Yanglei , Liu Meng , Li Dingzhong , Niu Yuxi , Wang Shaojun 
Published: September 2023

Abstract
Uncertain factors such as environmental noise and light source fluctuation will cause the loss of important features of the detection object, which will interfere with the detection and reconstruction accuracy. Therefore, accurate extraction of groove information is the key to ensure the quality of subsequent processes when detection technology is applied to steel structure grooves in ships, buildings, transportation and other fields. This paper designs a complex shaped detection and reconstruction algorithm based on linear structured light. This algorithm performs the visually assisted localization of the ROI area of the welding groove in a complex environment, and completes the extraction of the characteristic area of the welding groove and the interpolation of missing parts based on the slope method. According to the obtained information, the digital modeling analysis of each groove section feature is carried out, and the 3D model is reconstructed based on the feature information fitting. The extraction of geometric characteristics of single V-groove and the reconstruction of the 3D model are realized, and the effectiveness of the extracted geometric characteristics is verified. On this basis, the subsequent welding is completed well, which verifies that the algorithm can effectively reduce the influence of the lack of groove characteristics caused by uncertain factors.