bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fundamental Process Mechanics Common to Machining and Grinding Operations
Authors: Lortz Wolfgang , Pavel Radu 
Published: January 2021

Abstract
All different production processes have one thing in common: in each case a workpiece with characteristic material behavior, stress, strain, self-hardening and temperature will be produced by a tool with special geometry and individual kinematic conditions, with a wide range of energy in a designed machine tool which is working along programmed lines. For the workpiece material, it is not important from which machine the energy is coming. To be able to predict more accurate values of the production process, it will be necessary to focus more on the complex and difficult process mechanics. The result must have a strong physical base and be in good agreement with practical results To solve these problems, we have to uncover all previous simplification assumptions for the existing models. This leads in a first step to a new fundament in process mechanics, which is only based on mathematics, physics and material behavior with friction conditions, and resulting temperatures during metal plastic flow. The new mathematical equations developed for yield shear stress and strain rate will be presented and discussed in this paper. The plastic deformation is the only parameter that will not disappear after completing the operation. Therefore, this will be the base to compare the developed theoretical deformation with the experimental results for two operations: cutting and grinding. In addition, it could be shown that yield shear stress and corresponding strain rate versus temperatures have an interdependent relationship, which creates the opportunity to determine the temperatures during metal plastic flow.
Intense Pulse Light Annealing for Perovskite Photovoltaics
Authors: Ghahremani Amir H. , Druffel Thad 
Published: January 2021

Abstract
Rapid advancements within photovoltaics realm necessitates swift fabrication of the modules using cheap materials through cost effective manufacturing processes to achieve short cost payback time. Photovoltaics manufacturing includes chemical processing of the materials followed by thermal annealing. Yet, long-term annealing of the materials using high temperature furnaces have remained the prevalent post-processing approach in industry which necessitates alternative methods to achieve high performance modules through rapid and economical processes. Intense pulse light (IPL) has been successfully applied as a promising rapid post-process annealing for various thin film photovoltaics, particularly to process the organic-inorganic perovskite solar cell (PSC) layers. In this paper, several results pertinent to the application of IPL on perovskite and SnO2 electron transport thin films are presented and the role of IPL on rapid thermal annealing (RTA) is explained. We show that swift fabrication of PSCs through IPL can result in efficiencies exceeding 16% when the Perovskite film is annealed with aid of CH2I2 alkyl halide additive in the ambient with 60% relative humidity. In addition, the synergy of IPL-alkyl halide interaction for other perovskite chemistries is introduced. We show that achieving to PSCs exceeding 12% efficiency was possible when the perovskite and SnO2 ETL was annealed sequentially through IPL.
Review of Surface Finishing of Additively Manufactured Metal Implants
Authors: Fashanu Felicia F. , Marcellin-Little Denis J. , Linke Barbara S. 
Published: January 2021

Abstract
Metal additive manufacturing (AM) technologies, commonly referred to as 3D printing, provide a good prospect for medical applications because complex geometries and customized parts can be fabricated to meet individual patient needs. Orthopedic implants are a group of medical parts with high relevance for AM. This paper discusses relevant AM technologies, several orthopedic applications, materials and material properties, mechanical surface finishing techniques, and measurement techniques from the literature. Today, most metal 3D printed implants are manufactured through metal powder bed fusion technology which includes direct metal laser sintering (DMLS), selective laser melting (SLM), and electron beam melting (EBM). Common materials include titanium alloys, cobalt chromium (CoCr) and stainless steel, chosen because of their biocompatibility and mechanical properties. Surface finishing is most often required for 3D printed implants due to the relatively poor surface quality to meet the desired surface texture for the application. Typically, postprocessing is done mechanically, including manual and automated grinding, sandblasting, polishing, or chemically, including electrochemical polishing. This review also covers an overview of surface quality characterization of AM metal implants which includes surface texture and topography. The surface parameters used to characterize the surface of the implants: surface roughness (Ra), differences between the peak and valley (Rz), waviness, and micro-finish.
3D Printing of Flexible and Stretchable Parts Using Multiwall Carbon Nanotubes/Polyester-Based Thermoplastic Polyurethane
Authors: Stan Felicia , Stanciu Nicoleta-Violeta , Constantinescu Adriana-Madalina , Fetecau Catalin 
Published: January 2021

Abstract
This paper reports on the 3D printing of flexible and stretchable parts based on multiwall carbon nanotubes (MWCNTs)/polyester-based thermoplastic polyurethane (TPU) nanocomposites. The rheological properties of the MWCNT/TPU nanocomposites with different wt.% of MWCNTs (0.1–3) were determined and used as guidance for the extrusion and 3D printing processes. MWCNT/TPU filaments were extruded and used for 3D printing of different flexible and stretchable parts. The mechanical, electrical, and piezoresistive response of the MWCNT/TPU nanocomposite filaments and 3D printed parts under static and monotonic loading was studied.
The experimental results show that with increasing temperature and shear rate, respectively, the shear viscosity of the MWCNT/TPU nanocomposite decreases, whereas the viscosity increases with increasing wt.% of MWCNTs. With the addition of MWCNTs, the elastic modulus and tensile strength of the feedstock filament all increase, enhancing the printability of TPU by increasing the buckling resistance and the stability of the 3D printed layer. The electrical conductivity of the 3D printed MWCNT/TPU nanocomposites increases with increasing wt.% of MWCNTs and exceeds the conductivity of the filaments. The 3D printed MWCNT/TPU nanocomposites with 3 wt.% show an electrical conductivity about 10 S/m, irrespective of the printing direction. Moreover, the 3D printed MWCNT/TPU nanocomposites exhibit good mechanical properties and high piezoresistive sensitivity with gauge factor (50–600) dependent on both strain and printing direction.
Mechanical and Corrosion Assessment of Friction Self-Piercing Rivet Joint of Carbon Fiber-Reinforced Polymer and Magnesium Alloy AZ31B
Authors: Lim Yong Chae , Chen Jian , Jun Jiheon , Leonard Donovan N. , Brady Michael P. , Warren Charles David , Feng Zhili 
Published: January 2021

Abstract
In the present work, thermoset carbon fiber–reinforced polymer (CFRP) was spot joined to magnesium alloy AZ31B by a friction self-piercing riveting (F-SPR) process. Lap shear tensile and cross-tension testing were used to evaluate the mechanical joint performance. An average lap shear tensile load of 5.18 kN was achieved, while an average of 2.81 kN was found from cross-tension testing. All F-SPR samples showed pull-out of AZ31B after mechanical testing, indicating good mechanical interlocking between the steel rivet and AZ31B. Corrosion potential was measured for each material to establish the galvanic corrosion characteristics. As expected, AZ31B was found to be the most active, while thermoset CFRP was the most noble. The steel rivet fell between the AZ31B (active) and the thermoset CFRP (noble). Salt fog corrosion testing (ASTM B-117) was performed to evaluate the corrosion performance of the uncoated F-SPR joint. With up to 200 h of exposure, the post-corroded F-SPR joint integrity retained 81.2% of the pre-exposure F-SPR joint strength with AZ31B pull-out failure mode. From cross-sectional analysis of the F-SPR joint, extensive corrosion of AZ31B was observed at the joint and other exposure areas. However, steel rivet was not significantly corroded potentially due to sacrificial anode effect by which AZ31B corroded first in the galvanic couple.
Localized Acoustic-Event Measurement Probe: Connector Confirmation Utilizing Acoustic Signatures
Authors: Skoglind Brian , Roberts Travis , Karmakar Sourabh , Turner Cameron , Mears Laine 
Published: January 2021

Abstract
Electrical connections in consumer products are typically made manually rather than through automated assembly systems due to the high variety of connector types and connector positions, and the soft flexible nature of their structures. Manual connections are prone to failure through missed or improper connections in the assembly process and can lead to unexpected downtime and expensive rework. Past approaches for registering connection success such as vision verification or Augmented Reality have shown limited ability to verify correct connection state. However, the feasibility of an acoustic-based verification system for electrical connector confirmation has not been extensively researched. One of the major problems preventing acoustic based verification in a manufacturing or assembly environment is the typically low signal to noise ratio (SNR) between the sound of an electrical connection and the diverse soundscape of the plant. In this study, a physical means of background noise mitigation and signature amplification are investigated in order to increase the SNR between the electrical connection and the plant soundscape in order to improve detection. The concept is that an increase in the SNR will lead to an improvement in the accuracy and robustness of an acoustic event detection and classification system. Digital filtering has been used in the past to deal with low SNRs, however, it runs the risk of filtering out potential important features for classification. A sensor platform is designed to filter out and reduce background noise from the plant without effecting the raw acoustic signal of the electrical connection, and an automated detection algorithm is presented. The solution is over 75% effective at detecting and classifying connections.
Structural Nonlinearity of Robotic Machining Systems
Authors: Mohammadi Yaser , Ahmadi Keivan 
Published: January 2021

Abstract
Excessive and unstable vibrations that are caused by the machining forces are among the most critical problems that limit the use of industrial robots instead of CNC machine tools. Reduction and control of robot’s vibrations during machining require accurate models of the robot’s vibration response to the dynamic forces exerted at the Tool Centre Point (TCP) where the cutting tool interacts with the workpiece material. The existing models of vibrations in robotic machining have been formed by assuming the linearity of the dynamic response at the TCP. In this paper, the accuracy of this assumption is investigated experimentally, and the results show that the dynamic response at the TCP is strongly nonlinear. An experimental procedure is presented to identify the nonlinearities by employing the first-order Frequency Response Functions (FRFs) measured using various input force excitations. Nonlinear Complex Mode Analysis is then used to extract the modal parameters of the system when its dynamics is linearized around a harmonic response with a constant amplitude. The extracted modal parameters strongly depend on the amplitude of the applied force and the resulting vibrations. This study highlights the need for considering the nonlinearities of the structural dynamics of industrial robots in modelling machining vibrations.
Deep Learning-Based Data Fusion Method for In-Situ Porosity Detection in Laser-Based Additive Manufacturing
Authors: Tian Qi , Guo Shenghan , Guo Weihong , Bian Linkan 
Published: January 2021

Abstract
Laser-Based Additive Manufacturing (LBAM) provides unprecedented possibilities to produce complicated parts with multiple functions for lots of engineering applications. Melt pool is one of the most important signatures in LBAM and is indicative of process anomalies and part defects. High-speed temperature images of the melt pool captured during LBAM make it possible for in-situ melt pool monitoring and porosity prediction. This paper aims to broaden current knowledge of the underlying relationship between process and porosity in LBAM and provide new possibilities for efficient and accurate porosity prediction. We present a deep learning-based data fusion method to predict porosity in LBAM parts by leveraging the measured melt pool thermal history and deep learning. A PyroNet, based on Convolutional Neural Networks, is developed to correlate in-process pyrometer images with layer-wise porosity; an IRNet, based on Long-term Recurrent Convolutional Networks, is developed to correlate sequential thermal images from infrared camera with layer-wise porosity. Predictions from PyroNet and IRNet are fused at the decision level to obtain a more accurate prediction of layer-wise porosity. The model fidelity is validated with LBAM Ti-6Al-4V thin-wall structure. Our method can achieve 100% accuracy with high efficiency, allowing the method to be applicable for in-situ porosity detection in LBAM.
A Novel Edge Computing Based Architecture for Intelligent Tool Condition Monitoring
Authors: Liu Rui 
Published: January 2021

Abstract
Edge computing has been recognized as a potential solution to enable intelligent manufacturing in the machining industry, especially for the small and medium-sized manufacturers. However, while various research studies have proposed their edge-based architectures for intelligent systems, there still exists a lack of practical and affordable technological plans that can be applied to complex machining process designs in actual production scenario. The objective of this research is to realize the tool condition monitoring (TCM) in machining by the edge computing based architecture for actual mass production. This study creatively proposes a calibration-based TCM system to monitor the cutting tool conditions in repetitive machining operations by comparing the characteristic signals generated by the reference cutting tools in the calibration procedure with the signal generated by the cutting tool in production through a concise similarity analysis, which can be easily integrated into typical cyber-psychical systems to realize the edge computing in a very efficient and flexible way. To validate the performance of the proposed architecture, a case study is demonstrated for tool wear monitoring of repetitive milling operations with a complex machining process design. Experimental validation has shown that the proposed edge-based TCM system can effectively monitor the tool wear progression which is in good agreement with actual wear measurements.
Heart rate during the first 24 hours in term-born infants
Authors: Tveiten Lars , Diep Lien My , Halvorsen Thomas , Markestad Trond 
Published: January 2021

ObjectiveHeart rate (HR) is an important clinical parameter in newborn infants, but normal ranges are poorly defined. Our aim was to establish normal reference ranges and individual variations in HR as obtained by auscultation in healthy term-born infants during the first 24 hours of life.DesignObservational study.SettingSingle hospital in Norway.MethodsHR was assessed by auscultation for 30 s at 2, 4, 8, 16 and 24 hours of age. Auscultation was validated against ECG recordings.SubjectsHealthy term-born infants who were asleep or awake in a quiet resting state.Main outcome measuresConstruction of percentile curves for resting HR.ResultsThe study included 953 infants. The 50th percentile was 126 beats per minute (bpm) at age 2 hours and thereafter 120–122 bpm. The respective 2nd and 98th percentiles were 102 (thereafter 96–100) bpm and 162 (thereafter 150–156) bpm. The mean HR was 5.6 bpm higher when awake than asleep, 4.9 bpm higher when on the mother’s chest than in the cot, 1.6 bpm higher in girls than in boys, and increased by 0.5 bpm per 0.1°C increase in rectal temperature. Mode of delivery, meconium staining, birth weight and maternal smoking during pregnancy were of no significance. For each infant, HR varied considerably during the first 24 hours (intraclass correlation 0.21 (95% CI 0.18 to 0.24), coefficient of variation 9.2%).ConclusionsThe HR percentiles allow for a scientifically based use of HR when assessing newborn infants born at term.
Randomised study of heart rate measurement in preterm newborns with ECG plus pulse oximetry versus oximetry alone
Authors: Murphy Madeleine C , Jenkinson Allan , Coveney John , McCarthy Lisa K , O Donnell Colm Patrick Finbarr 
Published: January 2021

AimTo determine whether the IntelliVue monitor (ECG plus Masimo pulse oximeter (PO)) displays heart rate (HR) at birth more quickly than Nellcor PO (PO alone) among infants of 29–35 weeks’ gestational age.MethodsUnmasked parallel group randomised (1:1) study.ResultsWe planned to enrol 100 infants; however, the study was terminated due to the COVID-19 pandemic when 39 infants had been enrolled (17 randomised to IntelliVue, 22 to Nellcor). We found no differences between the groups in the time to first HR display (median (IQR) IntelliVue ECG 49 (33, 71) vs Nellcor 47 (37, 86) s, p>0.999), in the proportion who had a face mask applied for breathing support, or in the time at which it was applied. Infants monitored with IntelliVue were handled more frequently and for longer.ConclusionIntelliVue ECG did not display HR more quickly than Nellcor PO in preterm infants. We found no differences in the rate of or time to intervention between groups. Our study was terminated early so these findings should be interpreted with caution.Trial registration numberISRCTN16473881
Patients presenting with metastases: stage IV uveal melanoma, an international study
Authors: Garg Gaurav , Finger Paul T , Kivelä Tero T , Simpson E Rand , Gallie Brenda L , Saakyan Svetlana , Amiryan Anush G , Valskiy Vladimir , Chin Kimberly J , Semenova Ekaterina , Seregard Stefan , Filì Maria , Wilson Matthew , Haik Barrett , Caminal Josep Maria , Catala-Mora Jaume , Gutiérrez Cristina , Pelayes David E , Folgar Anibal Martin , Jager Martine Johanna , Doğrusöz Mehmet , Luyten Gregorius P M , Singh Arun D , Suzuki Shigenobu 
Published: January 2021

ObjectiveTo analyse ocular and systemic findings of patients presenting with systemic metastasis.Methods and analysisIt is an international, multicentre, internet-enabled, registry-based retrospective data analysis. Patients were diagnosed between 2001 and 2011. Data included: primary tumour dimensions, extrascleral extension, ciliary body involvement, American Joint Committee on Cancer (AJCC)-tumour, node, metastasis staging, characteristics of metastases.ResultsOf 3610 patients with uveal melanoma, 69 (1.9%; 95% CI 1.5 to 2.4) presented with clinical metastasis (stage IV). These melanomas originated in the iris, ciliary body and choroid in 4%, 16% and 80% of eyes, respectively. Using eighth edition AJCC, 8 (11%), 20 (29%), 24 (35%), and 17 (25%) belonged to AJCC T-categories T1–T4. Risk of synchronous metastases increased from 0.7% (T1) to 1.5% (T2), 2.6% (T3) and 7.9% (T4). Regional lymph node metastases (N1a) were detected in 9 (13%) patients of whom 6 (67%) had extrascleral extension. Stage of systemic metastases (known for 40 (59%) stage IV patients) revealed 14 (35%), 25 (63%) and 1 (2%) had small (M1a), medium-sized (M1b) and large-sized (M1c) metastases, respectively. Location of metastases in stage IV patients were liver (91%), lung (16%), bone (9%), brain (6%), subcutaneous tissue (4%) and others (5%). Multiple sites of metastases were noted in 24%. Compared with the 98.1% of patients who did not present with metastases, those with synchronous metastases had larger intraocular tumours, more frequent extrascleral extension, ciliary body involvement and thus a higher AJCC T-category.ConclusionsThough higher AJCC T-stage was associated with risk for metastases at diagnosis, even small T1 tumours were stage IV at initial presentation. The liver was the most common site of metastases; however, frequent multiorgan involvement supports initial whole-body staging.
Do ‘pathologic’ cardiac murmurs in adolescents identify structural heart disease? An evaluation of 15 141 active adolescents for conditions that put them at risk of sudden cardiac death
Authors: Austin Ashley V , Owens David S , Prutkin Jordan M , Salerno Jack C , Ko Brian , Pelto Hank F , Rao Ashwin L , Siebert David M , Carrol Jennifer S , Harmon Kimberly G , Drezner Jonathan A 
Published: January 2021

ObjectivesWe assessed whether the presence and character of a cardiac murmur in adolescents were associated with structural heart disease that confers risk of sudden cardiac death (SCD).MethodsWe performed a retrospective analysis of 15 141 adolescents age 12–19 who underwent a heart screen with history, physical examination and ECG. Participants with any screening abnormality underwent an echocardiogram for the assessment of structural heart disease. Murmurs were classified as physiological or pathological according to standard clinical criteria, and participants with murmurs were compared with a comparison group without murmurs. The primary outcome was echocardiogram-detected structural heart disease associated with SCD.Results905 participants with a cardiac murmur (mean age 15.8; 58% male) and 4333 participants without a murmur (comparison group; mean age 15.8; 55% male) had an echocardiogram to detect structural heart disease. 743 (82%) murmurs were described as physiological and 162 (18%) as pathological. Twenty-five (2.8%) participants with murmurs and 61 (1.4%) participants without murmurs had structural heart disease. Three (0.3%) participants in the murmur group were diagnosed with hypertrophic cardiomyopathy (HCM) which was the only identified condition associated with SCD. Two participants with HCM had physiological murmurs, one had a pathological murmur, and all three had an abnormal ECG. The most common minor structural heart disease was bicuspid aortic valve in both the murmur (7; 0.8%) and comparison (20; 0.5%) groups. The positive predictive value of physiological versus pathological murmurs for identifying any structural heart disease was 2.4% versus 4.3% (p=0.21), respectively. The positive predictive value of having any murmur versus no murmur for identifying structural heart disease was 2.8% versus 1.4% (p=0.003), respectively.ConclusionsIn adolescents, the traditional classification of cardiac murmurs as ‘physiologic’ or ‘pathologic’ does not differentiate for structural heart disease that puts individuals at risk for SCD. We recommend ECG evaluation in all patients with a cardiac murmur found during preparticipation screening to increase detection of HCM.