scholarly journals Structure and Performance Attributes Optimization and Ranking of Gamma Irradiated Polymer Hybrids for Industrial Application

Polymers ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 47
Author(s):  
Suhail H. Serbaya ◽  
Emad H. Abualsauod ◽  
Mohammed Salem Basingab ◽  
Hatim Bukhari ◽  
Ali Rizwan ◽  
...  
Keyword(s):  
Young Modulus ◽  
High Strength ◽  
Industrial Applications ◽  
Gamma Dose ◽  
Decision Matrix ◽  
Suitability Index ◽  
Proposed Model ◽  
Dump Trucks ◽  
And Performance ◽  
Gamma Irradiated

The selection of suitable composite material for high-strength industrial applications, from the list of available alternatives, is a tedious task as it requires an optimized structural performance-based solution. This study aimed to optimize the concentration of fillers, i.e., vinyl tri-ethoxy silane and absorbed gamma-dose, to enhance the properties of an industrial scale polymer, i.e., ultra-high molecular weight polyethylene (UHMWPE). The UHMWPE hybrids, in addition to silane, were treated with (30, 65, and 100 kGy) gamma dose and then tested for ten application-specific structural and performance attributes. The relative importance of attributes based on an 11-point fuzzy conversation was used for establishing the material assessment graph and corresponding adjacency matrix. Afterwards, the normalized values of attributes were used to establish the decision matrix for each alternative. The normalization was performed after the identification of high obligatory valued (HOV) and low obligatory valued (LOV) attributes. After this, suitability index values (SIVs) were calculated for ranking the hybrids that revealed hybrids 65 kGy irradiated the hybrid as the best choice and ranked as first among the existing alternatives. The major responsible factors were higher oxidation strength, a dense cross-linking network, and elongation at break. The values of the aforementioned factors for 65 kGy irradiated hybrids were 0.24, 91, and 360 MPa, respectively, as opposed to 0.54, 75, and 324 MPa for 100 kGy irradiated hybrids, thus placing the latter in second place regarding higher values of Yield Strength and Young Modulus. Finally, it is believed that the reported results and proposed model in this study will improve preoperative planning as far as considering these hybrids for high-strength industrial applications including total joint arthroplasty, textile-machinery pickers, dump trucks lining ships, and harbors bumpers and sliding, etc.

scholarly journals Capacitance Response of Concave Well Substrate MEMS Double Touch Mode Capacitive Pressure Sensor: Robust Design, Theoretical Modeling, Numerical Simulation and Performance Comparison

2021 ◽  
Author(s):  
Guru Aathavan Alagu Uthaya Kumar ◽  
Sumit Kumar Jindal ◽  
SREEKANTH P K
Keyword(s):  
Pressure Sensor ◽  
Industrial Condition ◽  
Industrial Applications ◽  
Performance Comparison ◽  
Capacitive Pressure Sensor ◽  
Pressure Sensing ◽  
Flat Substrate ◽  
Small Deflection ◽  
Proposed Model ◽  
And Performance

Abstract Touch Mode Capacitive Pressure Sensor (TMCPS) is very suitable for industrial applications where pressure sensing is necessary because of their linearity, mechanical robust nature and large overload protection from harsh industrial condition. This work proposes an introduction of a notch in the concave substrate for further improvement of the sensitivity of the sensor. Small deflection mode is utilized for the mathematical analysis of the design proposed and MATLAB is utilized for all the software simulations. The sensitivity of the proposed model is very high compared to other models with flat substrate. The analysis and simulation show significant increase in sensitivity in touch mode. The pressure at which the value of the capacitance saturates is also much higher than the designs stated in the literature. The analysis of concave substrate Double Touch Mode Capacitive Pressure Sensor (DTMCPS) will be helpful in designing new sensor for performance increase and to evaluate the behaviour of it.

The Influence of Cognitive and Affective Reactions to Feedback on Subsequent Goals

2010 ◽  
Vol 15 (2) ◽  
pp. 121-131 ◽  
Author(s):  
Remus Ilies ◽  
Timothy A. Judge ◽  
David T. Wagner
Keyword(s):  
Performance Feedback ◽  
Business School ◽  
Structural Equation ◽  
Emotional Reactions ◽  
Equation Model ◽  
Proposed Model ◽  
And Performance ◽  
Dispositional Measure ◽  
The Relationship ◽  
Reactions To Feedback

This paper focuses on explaining how individuals set goals on multiple performance episodes, in the context of performance feedback comparing their performance on each episode with their respective goal. The proposed model was tested through a longitudinal study of 493 university students’ actual goals and performance on business school exams. Results of a structural equation model supported the proposed conceptual model in which self-efficacy and emotional reactions to feedback mediate the relationship between feedback and subsequent goals. In addition, as expected, participants’ standing on a dispositional measure of behavioral inhibition influenced the strength of their emotional reactions to negative feedback.

An Analytical Transient Tire Model

2001 ◽  
Vol 29 (2) ◽  
pp. 108-132 ◽  
Author(s):  
A. Ghazi Zadeh ◽  
A. Fahim
Keyword(s):  
Transient Behavior ◽  
Stability Control ◽  
Vehicle Stability ◽  
Tire Model ◽  
Steering Angle ◽  
First Order ◽  
Vehicle Stability Control ◽  
Proposed Model ◽  
Depth Study ◽  
And Performance

Abstract The dynamics of a vehicle's tires is a major contributor to the vehicle stability, control, and performance. A better understanding of the handling performance and lateral stability of the vehicle can be achieved by an in-depth study of the transient behavior of the tire. In this article, the transient response of the tire to a steering angle input is examined and an analytical second order tire model is proposed. This model provides a means for a better understanding of the transient behavior of the tire. The proposed model is also applied to a vehicle model and its performance is compared with a first order tire model.

scholarly journals Manufacturing and development of a bolted GFRP flange joint for oil and gas applications

2021 ◽  
pp. 095440542198951
Author(s):  
Muhsin Aljuboury ◽  
Md Jahir Rizvi ◽  
Stephen Grove ◽  
Richard Cullen
Keyword(s):  
Pressure Vessel ◽  
Oil And Gas ◽  
Polyester Resin ◽  
High Strength ◽  
Adhesively Bonded ◽  
Acceptable Quality ◽  
Composite Pipe ◽  
And Performance ◽  
Composite Pipes

The goal of this experimental study is to manufacture a bolted GFRP flange connection for composite pipes with high strength and performance. A mould was designed and manufactured, which ensures the quality of the composite materials and controls its surface grade. Based on the ASME Boiler and Pressure Vessel Code, Section X, this GFRP flange was fabricated using biaxial glass fibre braid and polyester resin in a vacuum infusion process. In addition, many experiments were carried out using another mould made of glass to solve process-related issues. Moreover, an investigation was conducted to compare the drilling of the GFRP flange using two types of tools; an Erbauer diamond tile drill bit and a Brad & Spur K10 drill. Six GFRP flanges were manufactured to reach the final product with acceptable quality and performance. The flange was adhesively bonded to a composite pipe after chamfering the end of the pipe. Another type of commercially-available composite flange was used to close the other end of the pipe. Finally, blind flanges were used to close both ends, making the pressure vessel that will be tested under the range of the bolt load and internal pressure.

scholarly journals Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2648
Author(s):  
Muhammad Aamir ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Ahmad Shaf ◽  
Muhammad Zeeshan Azam ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Natural Disasters ◽  
Deep Convolutional Neural Network ◽  
Multispectral Images ◽  
Learning Techniques ◽  
Proposed Model ◽  
Disaster Intensity ◽  
And Performance

Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and wildfires. Many deep learning techniques have been applied by various researchers to detect and classify natural disasters to overcome losses in ecosystems, but detection of natural disasters still faces issues due to the complex and imbalanced structures of images. To tackle this problem, we propose a multilayered deep convolutional neural network. The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification of natural disaster intensity types with different filters and parameters. The model is tested on 4428 natural images and performance is calculated and expressed as different statistical values: sensitivity (SE), 97.54%; specificity (SP), 98.22%; accuracy rate (AR), 99.92%; precision (PRE), 97.79%; and F1-score (F1), 97.97%. The overall accuracy for the whole model is 99.92%, which is competitive and comparable with state-of-the-art algorithms.

scholarly journals Identification of abnormal tribological regimes using a microphone and semi-supervised machine-learning algorithm

Friction ◽  
2021 ◽  
Author(s):  
Vigneashwara Pandiyan ◽  
Josef Prost ◽  
Georg Vorlaufer ◽  
Markus Varga ◽  
Kilian Wasmer
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Industrial Applications ◽  
Supervised Machine Learning ◽  
Normal Operation ◽  
Promising Alternative ◽  
Wear And Tear ◽  
Validation Procedure ◽  
And Performance

AbstractFunctional surfaces in relative contact and motion are prone to wear and tear, resulting in loss of efficiency and performance of the workpieces/machines. Wear occurs in the form of adhesion, abrasion, scuffing, galling, and scoring between contacts. However, the rate of the wear phenomenon depends primarily on the physical properties and the surrounding environment. Monitoring the integrity of surfaces by offline inspections leads to significant wasted machine time. A potential alternate option to offline inspection currently practiced in industries is the analysis of sensors signatures capable of capturing the wear state and correlating it with the wear phenomenon, followed by in situ classification using a state-of-the-art machine learning (ML) algorithm. Though this technique is better than offline inspection, it possesses inherent disadvantages for training the ML models. Ideally, supervised training of ML models requires the datasets considered for the classification to be of equal weightage to avoid biasing. The collection of such a dataset is very cumbersome and expensive in practice, as in real industrial applications, the malfunction period is minimal compared to normal operation. Furthermore, classification models would not classify new wear phenomena from the normal regime if they are unfamiliar. As a promising alternative, in this work, we propose a methodology able to differentiate the abnormal regimes, i.e., wear phenomenon regimes, from the normal regime. This is carried out by familiarizing the ML algorithms only with the distribution of the acoustic emission (AE) signals captured using a microphone related to the normal regime. As a result, the ML algorithms would be able to detect whether some overlaps exist with the learnt distributions when a new, unseen signal arrives. To achieve this goal, a generative convolutional neural network (CNN) architecture based on variational auto encoder (VAE) is built and trained. During the validation procedure of the proposed CNN architectures, we were capable of identifying acoustics signals corresponding to the normal and abnormal wear regime with an accuracy of 97% and 80%. Hence, our approach shows very promising results for in situ and real-time condition monitoring or even wear prediction in tribological applications.

Leader–follower narcissism and subgroup formation in teams: a conceptual model

2019 ◽  
Vol 34 (6) ◽  
pp. 429-442 ◽  
Author(s):  
Manuel London
Keyword(s):  
Team Performance ◽  
Team Identification ◽  
Goal Congruence ◽  
Content Type ◽  
Team Outcomes ◽  
Performance Pressure ◽  
Proposed Model ◽  
Leaders And Followers ◽  
Trait Activation ◽  
And Performance

Purpose Drawing on existing theory, a model is developed to illustrate how the interaction between leaders and followers similarity in narcissism and goal congruence may influence subgroup formation in teams, and how this interaction influences team identification and team performance. Design/methodology/approach The proposed model draws on dominance complementary, similarity attraction, faultline formation and trait activation theories. Findings Leader–follower similarity in narcissism and goal congruence may stimulate subgroup formation, possibly resulting in conformers, conspirators, outsiders and victims, especially when performance pressure on a team is high. Followers who are low in narcissism and share goals with a leader who is narcissistic are likely to become conformers. Followers who are high in narcissism and share goals with a narcissistic leader are likely to become confederates. Followers who do not share goals with a narcissistic leader will be treated by the leader and other members as outsiders if they are high in narcissism, and victimized if they are low in narcissism. In addition, the emergence of these subgroups leads to reduced team identification and lower team performance. Practical implications Higher level managers, coaches and human resource professions can assess and, if necessary, counteract low team identification and performance resulting from the narcissistic personality characteristics of leaders and followers. Originality/value The model addresses how and under what conditions narcissistic leaders and followers may influence subgroup formation and team outcomes.

Crack Pattern and Ductility of Steel Reinforced Ultra High Strength Concrete Composite Joint Subjected to Reversal Cycle Load

2009 ◽  
Vol 417-418 ◽  
pp. 845-848 ◽  
Author(s):  
Chang Wang Yan ◽  
Jin Qing Jia ◽  
Ju Zhang
Keyword(s):  
High Strength Concrete ◽  
Plastic Hinge ◽  
Engineering Application ◽  
Load Ratio ◽  
High Strength ◽  
Strength Concrete ◽  
Weak Beam ◽  
Composite Joint ◽  
And Performance ◽  
Beam Plastic Hinge

In order to investigate the seismic damage and performance of steel reinforced ultra high strength concrete composite joint subjected to reversal cycle load, six interior strong-column-weak-beam joint specimens were tested with various axial load ratio and volumetric stirrup ratio. A discussion on the crack mode and ductility was presented. It was found that all joint specimens failed in bending with a beam plastic hinge in a ductile manner, with crack propagation different from the weak-column-strong-beam joint. The experimental results indicated that test parameters of the steel reinforced ultra high strength concrete composite joint with good seismic performance may be referred for engineering application.

scholarly journals Sequential Laser–Mechanical Drilling of Thick Carbon Fibre Reinforced Polymer Composites (CFRP) for Industrial Applications

Polymers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 2136
Author(s):  
Sharizal Ahmad Sobri ◽  
Robert Heinemann ◽  
David Whitehead
Keyword(s):  
Carbon Fibre ◽  
Polymer Composites ◽  
Weight Ratio ◽  
High Strength ◽  
Industrial Applications ◽  
Fibre Laser ◽  
Reinforced Polymer ◽  
Carbon Fibre Reinforced Polymer ◽  
Fibre Reinforced Polymer ◽  
Thrust And Torque

Carbon fibre reinforced polymer composites (CFRPs) can be costly to manufacture, but they are typically used anywhere a high strength-to-weight ratio and a high steadiness (rigidity) are needed in many industrial applications, particularly in aerospace. Drilling composites with a laser tends to be a feasible method since one of the composite phases is often in the form of a polymer, and polymers in general have a very high absorption coefficient for infrared radiation. The feasibility of sequential laser–mechanical drilling for a thick CFRP is discussed in this article. A 1 kW fibre laser was chosen as a pre-drilling instrument (or initial stage), and mechanical drilling was the final step. The sequential drilling method dropped the overall thrust and torque by an average of 61%, which greatly increased the productivity and reduced the mechanical stress on the cutting tool while also increasing the lifespan of the bit. The sequential drilling (i.e., laser 8 mm and mechanical 8 mm) for both drill bits (i.e., 2- and 3-flute uncoated tungsten carbide) and the laser pre-drilling techniques has demonstrated the highest delamination factor (SFDSR) ratios. A new laser–mechanical sequence drilling technique is thus established, assessed, and tested when thick CFRP composites are drilled.

Modeling and performance analysis of diesel engine considering the heating effect of blow-by on cylinder intake gas

2021 ◽  
pp. 146808742110692
Author(s):  
Zhenyu Shen ◽  
Yanjun Li ◽  
Nan Xu ◽  
Baozhi Sun ◽  
Yunpeng Fu ◽  
...  
Keyword(s):  
Diesel Engine ◽  
Engine Performance ◽  
Marine Diesel Engine ◽  
Heating Effect ◽  
Shipping Industry ◽  
Low Speed ◽  
Proposed Model ◽  
International Regulations ◽  
Marine Diesel ◽  
And Performance

Recently, the stringent international regulations on ship energy efficiency and NOx emissions from ocean-going ships make energy conservation and emission reduction be the theme of the shipping industry. Due to its fuel economy and reliability, most large commercial vessels are propelled by a low-speed two-stroke marine diesel engine, which consumes most of the fuel in the ship. In the present work, a zero-dimensional model is developed, which considers the blow-by, exhaust gas bypass, gas exchange, turbocharger, and heat transfer. Meanwhile, the model is improved by considering the heating effect of the blow-by gas on the intake gas. The proposed model is applied to a MAN B&W low-speed two-stroke marine diesel engine and validated with the engine shop test data. The simulation results are in good agreement with the experimental results. The accuracy of the model is greatly improved after considering the heating effect of blow-by gas. The model accuracy of most parameters has been improved from within 5% to within 2%, by considering the heating effect of blow-by gas. Finally, the influence of blow-by area change on engine performance is analyzed with considering and without considering the heating effect of blow-by.

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