bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Novel Welding Method for Repairing Surface Defects of Large-Type Rotary Machinery Based on Line Structured Light Detection
Authors: Fu Hao , Lu Hong , Zhang Yongquan , Wu Zidong , Huang He , Liu Shijie , Wang Shaojun 
Published: September 2022

Abstract
Large-type rotary machinery is the core components of national major projects which is widely used aviation, electric power, metallurgy, energy and construction machinery industries. Surface defects of Large-type rotary machinery such as cracks and pits are usually processed into groove with a certain shape first, and then the processed groove is repaired by manual welding. This manual welding repair method has a low level of automation, and the repair quality of the groove is difficult to guarantee. Therefore, this paper proposes a novel welding method for repairing surface defects of Large-type rotary machinery which uses the Kollmorgen Joint Modular Robot to complete the welding repair of the processed groove. Firstly, the groove point cloud data collected by Line structured light sensor is processed by the designed algorithm to obtain the contour characteristics of the groove. Then, the arrangement of welding pass is completed based on contour characteristics of the groove. Finally, the trajectory of the welding robot is determined by the position of welding pass. The planned trajectory verification is completed on the simulation experiment platform and the result shows the accuracy and reliability of the planned trajectory which has certain theoretical and practical significance for realizing the automation of on-site maintenance of Large-type rotary machinery.
Electric Vehicle Battery End-Of-Use Recovery Management: Degradation Prediction and Decision Making
Authors: Zhao Yixin , Behdad Sara 
Published: September 2022

Abstract
Electric vehicles (EVs) are spreading rapidly in the market due to their better responsiveness and environmental friendliness. An accurate diagnosis of EV battery status from operational data is necessary to ensure reliability, minimize maintenance costs, and improve sustainability. This paper presents a deep learning approach based on the long short-term memory network (LSTM) to estimate the state of health (SOH) and degradation of lithium-ion batteries for electric vehicles without prior knowledge of the complex degradation mechanisms. Our results are demonstrated on the open-source NASA Randomized Battery Usage Dataset with batteries aging under changing operating conditions. The randomized discharge data can better represent practical battery usage. The study provides additional end-of-use suggestions, including continued use, remanufacturing/repurposing, recycling, and disposal; for battery management dependent on the predicted battery status. The suggested replacement point is proposed to avoid a sharp degradation phase of the battery to prevent a significant loss of active material on the electrodes. This facilitates the remanufacturing/repurposing process for the replaced battery, thereby extending the battery’s life for secondary use at a lower cost. The prediction model provides a tool for customers and the battery second use industry to handle their EV battery properly to get the best economy and system reliability compromise.
Hydrogel and Graphene Embedded Piezoresistive Microcantilever Sensor for Solvent and Gas Flow Detection
Authors: Ghosh Dipannita , Rahman Md. Ashiqur , Ashraf Ali , Islam Nazmul 
Published: September 2022

Abstract
Piezoresistive microcantilever sensor is widely used in sensing applications including liquid and gas flow detection. Microcantilevers can function as an embedded system if they are coated with polymers or nanomaterials to improve sensing performance. In this paper, we investigated the performance of piezoresistive microcantilevers (PMC) with and without additional coating. We studied the sensitivity of the PMC sensor after coating it with a three-dimensional porous hydrogel and piezoresistive graphene oxide layer. Hydrogel embedded piezoresistive microcantilever (EPM) showed better results than PMC during solvent sensing application. The resistance change for hydrogel embedded PMC was higher compared to bare PMC by 430% (3.2% to 17%) while detecting isopropyl alcohol (IPA), by approximately 1.5 orders of magnitude (0.19% to 5.7%) while detecting the presence of deionized water. Graphene Oxide coated PMC showed a wider detection range by 30 milliliter/min and 24% better sensitivity than bare PMC during the gas detection experiment. Additionally, we compared the experiment result with COMSOL simulation to develop a model for our embedded PMC sensing. Simulation shows significantly higher deflection of the EPM compared to the bare PMC (66.67% higher while detecting IPA, consistent with the trend observed during the experiment). The facile drop casting-based embedded microcantilever fabrication technique can lead to improved performance in different sensing applications. Our future work will focus on detecting biomolecules by using our constructed embedded systems.
Operating System for Cyber-Physical Manufacturing (OSCM): A Flexible Event-Driven Shopfloor Information Platform for Advanced Manufacturing
Authors: Toro Santamaria Ricardo , Ferreira Placid M. 
Published: September 2022

Abstract
Factory technologies have evolved to incorporate a great deal of manufacturing flexibility. Programmable automation in the form of CNC and PLCs along with hardware innovations (quick-change tooling, for example) and various operator assist technologies enable a high level of shop-floor flexibility. Possibly, the most inflexible part of a factory is the manufacturing information system. Customized for manufacturers by system integrators, these systems are often large monolithic systems assembled around an ERP/MRP framework or a precariously integrated set of decision-support software tools with a patchwork of communications enabling information flow between them.
On the other hand, cloud-based information service platforms such as those encountered in social networks and service brokers have seen rapid and multiple cycles of evolution resulting in a meteoric rise in their ability to handle increasingly large data scales and rates, while still maintaining their elasticity and flexibility. This rapid evolution of cloud-based information services has ignited a new era in the manufacturing industry as evidenced by emerging manufacturing cyberphysical system technologies such as the Industrial Internet of Things (IIoT), and Cloud Manufacturing (CM). These technologies are part of the broader context of what is thought to be the unfolding fourth industrial revolution (Industry 4.0 or Digital Manufacturing). This revolution places at its core, connectivity, information, and machine-based intelligence to create a new paradigm for manufacturing that is highly flexible, scalable, responsive, and intelligent.
This paper describes how we leveraged the newest advances in CPS, IIoT, CM, and distributed systems to create a flexible manufacturing information system infrastructure that separates information collection and distribution for decision-making functions. The first part of the paper introduces the architecture for a novel full-stack manufacturing infrastructure that is envisioned to facilitate and track the interaction between a manufacturing job, physical resources, and the software services (or apps) around them. We call this platform the Operating System for Cyber-physical Manufacturing (OSCM). In the second part of the paper, we introduce an event-based architecture for OSCM so that resource or transaction related events/data can be flexibly distributed to different decision-making/manufacturing software tools through an event/message exchange/bus. Further, such an architecture allows modularization and incremental development of different manufacturing software tools and services as new needs are identified.
Unexpectedly High Yields in Chemical Vapor Deposition of Carbon Nanotubes Based on Reactor Wall Thermochemical History
Authors: Tomaraei Golnaz , Abdulhafez Moataz , Bedewy Mostafa 
Published: September 2022

Abstract
While reactor wall preconditioning was previously shown to influence the growth of carbon nanotubes (CNTs) by chemical vapor deposition (CVD), it was previously only limited to studying the accumulating carbon deposits over the history of a large number of growth runs. However, the effect of leaving the reactor walls for an extended period of time between growth runs was not previously systematically studied. Here, we combine experimental measurements with a mathematical model to investigate the effect of thermochemical history of reactor walls on growth yield of vertically aligned CNT forests. Importantly, we demonstrate unexpectedly high CNT yield, exceeding one-order-of-magnitude taller forests, by increasing the interim period between runs (IPBR). We explain the results based on previously unexplored process sensitivity to trace amounts of oxygen-containing species in the reactor. In particular, uncontrolled amounts of water vapor desorbing from reactor walls during growth are modelled in this work. Our modeling results show the effect of IPBR on the outgassing dynamics revealing the underlying mechanism of generating growth promoting molecules during growth. By installing a new humidity sensor in our multizone rapid thermal CVD reactor, we are able to uniquely correlate the amount of moisture within the reactor to real-time measurements of growth kinetics, as well as ex situ characterization of CNT alignment and atomic defects. Our findings enable a scientifically grounded approach toward both boosting growth yield and improving its consistency by reducing run-to-run variations. Accordingly, engineered growth recipes can be envisioned to leverage this effect for improving manufacturing process scalability and robustness.
Thin-Film Nitrate Sensor Performance Prediction Based on Image Analysis and Credibility Data to Enable a Certify As Built Framework
Authors: Wang Xihui , Saha Ajanta , Mi Ye , Shakouri Ali , Ashraful Alam Muhammad , Chiu George T. C. , Allebach Jan P. 
Published: September 2022

Abstract
In the modern industrial setting, there is an increasing demand for all types of sensors. The demand for both the quantity and quality of sensors is increasing annually. Our research focuses on thin-film nitrate sensors in particular, and it seeks to provide a robust method to monitor the quality of the sensors while reducing the cost of production.
We are researching an image-based machine learning method to allow for real-time quality assessment of every sensor in the manufacturing pipeline. It opens up the possibility of real-time production parameter adjustments to enhance sensor performance. This technology has the potential to significantly reduce the cost of quality control and improve sensor quality at the same time. Previous research has proven that the texture of the topical layer (ion-selective membrane (ISM) layer) of the sensor directly correlates with the performance of the sensor. Our method seeks to use the correlation so established to train a learning-based system to predict the performance of any given sensor from a still photo of the sensor active region, i.e. the ISM. This will allow for the real-time assessment of every sensor instead of sample testing. Random sample testing is both costly in time and labor, and therefore, it does not account for all of the individual sensors.
Sensor measurement is a crucial portion of the data collection process. To measure the performance of the sensors, the sensors are taken to a specialized lab to be measured for performance. During the measurement process, noise and error are unavoidable; therefore, we generated credibility data based on the performance data to show the reliability of each sensor performance signal at each sample time.
In this paper, we propose a machine learning based method to predict sensor performance using image features extracted from the non-contact sensor images guided by the credibility data. This will eliminate the need to test every sensor as it is manufactured, which is not practical in a high-speed roll-to-roll setting, thus truely enabling a certify as built framework.
Review of KEI Method-Based Dimensional Measurement, Positioning Control and Part Inspection
Authors: Wang Zhikun , Lee ChaBum 
Published: September 2022

Abstract
We have developed knife-edge interferometry (KEI) for various engineering applications, including displacement measuring sensors for dynamic system controls and edge quality of the parts, such as cutting tools, corrosive blades, and photomask patterns. This paper reviewed the modeling, design, and data analysis of KEI. With the expanding market of manufacturing industries, edge topography and instrumentation technology become more and more vital to industrial manufacturing-related applications such as cutting tool wear inspection, photomask edge roughness determination, and edge corrosion propagation monitoring. Due to the limitation of measurement requirements like non-contact (photomask inspection), in-situ (cutting tool inspection), and real-time (corrosion propagation monitoring), there are only a few methods available in the market above, and those methods are based on post-processing. The KEI is capable of on-machine measurements, especially for the nanopositioning systems, providing a large working range and positioning accuracy compared with the conventional displacement sensor. This review addresses the current and future KEI technology. Here, including the theoretical approaches to KEI, this review details the data analysis method to feature the edge topographical information.
Development and Evaluation of a Computer Vision System for Assembly Bolt Pattern Traceability and Poka-Yoke
Authors: Kozikowski Eric , Hartman Nathan W. , D. Camba Jorge 
Published: September 2022

Abstract
Decisions made based on manufacturing data can help identify key problem areas in an assembly line and mitigate any defects from progressing through to the next step in the assembly process. But what if the products’ “as manufactured” data was inaccurate or did not exist at all? This paper outlines the development and validation of a system for tracking and error-proofing the assembly of bolted joints that require specific torque patterns in an industrial environment. Using a machine vision system, our solution recognizes the type of fastener being used, traces the tool location relative to the mechanical fastener, and records the order in which the fasteners were torqued in. If an error is detected, the system does not allow the user to progress through the assembly process. We leverage open source machine learning algorithms to allow efficient object detection model training. Our solution was validated using a series of tests and evaluated using the STEP method. Results show that the proposed system provides an effective and intuitive human interface for cyber-physical systems and an approach toward the digitalization of human skills needed for product assembly and their utilization in manufacturing.
Analytical Study of Shear-Thinning Fluid Flow in Direct Ink Writing Process
Authors: Guo Zipeng , Fei Fan , Song Xuan , Zhou Chi 
Published: September 2022

Abstract
As a facile and versatile additive manufacturing technology, direct ink writing (DIW) has attracted considerable interest in academia and industry to fabricate three-dimensional structures with unique properties and functionalities. However, so far, the physical phenomena during the DIW process are not revealed in detail, leaving a research gap between the physical experiments and the underlying theories. Here, we presented a comprehensive simulation study of non-Newtonian ink flow during the DIW process. We used the computational fluid dynamics (CFD) method and revealed the shear-thinning behavior during the extrusion process. Different nozzle geometry models were adopted in the simulation. The advantages and drawbacks of each syringe-nozzle geometry were analyzed. In addition, the ink shear stress and velocity fields were investigated and compared in the case studies. Based on these investigations and analysis, we proposed an improved syringe-nozzle geometry towards high-resolution DIW. Consequently, the high-resolution and high shape fidelity DIW could enhance the DIW product performance. The results developed in this work offer valuable guidelines and could accelerate further advancement of DIW.
A Dynamic Cloud Bayes Network-Based Cleaning Method of Multi-Source Unstructured Data
Authors: Chao Yin , Xinian Liao , Xiaobin Li 
Published: September 2022

Abstract
Aiming at the problems of data redundancy and data abnormality of multi-source unstructured data such as video, picture, and text in the process of processing quality inspection and equipment status monitoring of discrete intelligent production line, a multi-source unstructured data cleaning method based on dynamic cloud Bayesian network is proposed. We analyze the characteristics of multi-source unstructured data in the processing operation of the discrete intelligent production line and construct a multi-source unstructured data description model. combine dynamic Bayesian network and cloud model theory to design a multi-source unstructured data cleaning framework and processing flow based on dynamic cloud Bayesian network. finally, the feasibility of the proposed method is demonstrated by simulation analysis of arithmetic cases.
Real-Time Structured Light Scanning Characterization of Surface Topography of Direct Energy Deposited 316L Stainless Steel
Authors: Shen Weijun , Zhang Xing , Liao Yiliang , Li Beiwen 
Published: September 2022

Abstract
Direct energy deposition (DED) has been widely used for additive manufacturing of metallic components toward a variety of applications. Surface characteristics of DED-fabricated components play key roles in determining the property and performance. Besides the average surface roughness which has been extensively investigated in literature, surface skewness and kurtosis are critical for surface integrity, particularly its durability due to stress concentration points. In this work, surface skewness and kurtosis of DED-fabricated 316L stainless steel as affected by processing parameters are investigated. In particular, the surface quality is measured using a microscopic structured light scanning (SLS) system, which is a relatively fast, low-cost, high-efficiency dimensional inspection metrology as compared to other methods. The results demonstrated the correlations between the printing parameters (laser power and scanning speed) and the surface topography of DED printed parts. It is found that the skewness and kurtosis of the surface are more sensitive to the change in scanning speed within a relatively low laser power range. Skewness is positively correlated with the scanning speed, while kurtosis shows a negative correlation with the scanning speed. Given a high scanning speed, Kurtosis and Skewness are more sensitive to the changes of scanning speed. Understanding the relationship between DED processing parameters and areal surface characteristics provides guidance and insights for process optimization and post-processing design towards additive manufacturing of high-performance metallic components.
CFRP Milling and Drilling Process Monitoring by Using a Spindle-Integrated Three Axis Force Sensor
Authors: Kim Jungsub , Chun Heebum , Lee ChaBum 
Published: September 2022

Abstract
This paper introduces a spindle-integrated 3-axis force sensor consisted of 2-axis curved-edge sensor (CES) for the radial direction and single axis capacitive sensor (CS) for the axial direction. The proposed single unit force sensor was used to monitor the carbon fiber-reinforced plastic (CFRP) milling and drilling processes and to understand their machining mechanics. Unlike a conventional cutting force sensor can non-contact measure the displacement of the spindle and estimate the cutting force by multiplying the spindle stiffness and the measured displacement. The force sensor unit was successfully integrated with the spindle and measured the cutting force while machining the CFRP plates with 0°, 45° and 90° fiber orientation. The conventional 3-axis force sensor was also used for a baseline comparison with the cutting force results measured by the proposed method. To monitor and characterize the delamination effect of CFRP, the time-frequency domain scalogram images analyzed form the cutting force data were used. Unique features were extracted according to the fact of existence and nonexistence of the delamination. Laser, the sensor measured capability, allowing for in-process CFRP machining process monitoring and CFRP defect mode prediction was discussed.
Assessing the Impacts of Real-Time Price Prediction Quality on Demand Response Management for Sustainable Smart Manufacturing
Authors: Yun Lingxiang , Li Lin 
Published: September 2022

Abstract
The emerging smart manufacturing technologies pave the way for flexible and autonomous monitoring and control of complex manufacturing systems, which facilitate the implementation of real-time price (RTP) based demand response management towards sustainability. The demand response management requires scheduling of smart manufacturing systems in advance, and thus the quality of RTP predictions directly impacts the performance of demand response. Although several prediction evaluation metrics are currently available, they are designed to show the similarities between prediction and actual RTP, which are not necessarily related to demand response performance. Therefore, in this study, the daily energy cost reductions obtained by solving a demand response management problem are adopted as an indicator of demand response performance. Six commonly used evaluation metrics are examined, and their correlations with energy cost reductions are investigated. In addition, a new metric called k-peak distance considering the characteristics of the demand response problem is proposed and compared with the other six metrics. The case studies show that the proposed metric has two to four times higher correlation with energy cost reductions and only about half of the standard error compared to other metrics. The results indicate that the proposed metric can better represent the prediction quality in the demand response problem.
Robust and Scalable Synthesis of High Surface Area Porous Copper Spheriodized Powders by Electroless Chemical Dealloying
Authors: Niauzorau Stanislau , Kublik Natalya , Hasib Amm , Azeredo Bruno 
Published: September 2022

Abstract
In the past decade, nanoporous metals have been a point of interest in the scientific community because they exhibit chemical, optical, and mechanical properties that are unique from their bulk counterparts. One of the most prominent method for its synthesis is chemical dealloying. While, under electrolytic conditions, dealloying can use process-inputs such as current density and electrical potential to control the ligament size during its synthesis with excellent reproducibility, electroless methods are plagued by the lack of local control of dealloying rates which introduces batch-to-batch variations in ligament size. Given that powder is a format incompatible with electrolysis, this study shows an approach to safely scale fabrication of spherical porous copper powders containing oxides from gas atomized Cu-Al powders. Additionally, the agglomeration that is commonly associated with porous powder fabrication was addressed by its functionalization with an anionic surfactant and powder washing in both deionized water (polar) and anhydrous ethanol (nonpolar). Additionally, hazards associated with its production scaling such as excessive hydrogen evolution, heat generation due to its high-reactivity and exothermic reaction and pyrophoricity are discussed and addressed. As a result of this study, a robust and scalable approach was developed to produce 100 of grams of porous metal powders.