11 research outputs found
Optimization of Cutting Parameters in Drilling of AISI 304 Stainless Steel Using Taguchi and ANOVA
No full textAbstractThe present work deals with the effect of cutting parameters namely cutting speed, feed rate and helix angle on the tool life. The experiments were performed on drilling of AISI304 steel with carbide drill bits. Design of experiments was prepared according to Taguchi orthogonal array of L8 and experiments were performed with two levels of the cutting parameters. The effects of cutting parameters were analyzed by evaluating the amplitude of drill bit vibration and surface roughness. A Laser Doppler Vibrometer (LDV) was used for online data acquisition of drillbit vibration and a high-speed Fast Fourier Transform analyzer is used to process the acousto optic emission (AOE) signals. Taguchi and Analysis of Variance methods were used to identify significant cutting parameters affecting the drill bit vibrations and surface roughness. From the experimental results vibration of drill bit is found to be increased with the progression of the tool wear. Optimum levels of cutting parameters for surface roughness are obtained as 25 degrees of helix angle, 12mm/min of feed rate and 800rpm spindle speed. Optimum levels of cutting parameters for acceleration of vibration are obtained as 25 degrees of helix angle, 10mm/min of feed rate and 600rpm spindle speed
A Reliable Kidney Stone Detection Method Using Inductive Transfer-Based Ensemble Deep Neural Networks
No full textChronic kidney disease is a significant global health concern, with kidney stones being a major contributing factor to kidneydysfunction. Early and accurate detection is essential to prevent severe complications. This study proposes an efficient approachusing inductive transfer-based ensemble deep neural networks for kidney stone detection. A combination of classification models,including DarkNet19, InceptionV3, ResNet101, and others, along with detection algorithms from the YOLO family, enhancesdiagnostic accuracy. Feature extraction techniques such as ReliefF and validation methods like KNN and KFold improve modelperformance. The integration of the Xception model further refines classification accuracy, while a user-friendly Flask-based frontend facilitates real-time testing with secure authentication. The proposed approach improves early diagnosis, reduces physicianworkload, and enhances patient care
Bayesian Optimisation in Deep Learning for Electric Vehicle SOC Prediction
No full textSOC estimate is essential for electric car lithium-ion battery efficiency. Traditional deep learning models optimized using Grid Search often fail to achieve optimal hyperparameters, leading to reduced prediction accuracy. To address this, we employed Bayesian Optimization, which efficiently selects the best hyperparameters by leveraging past evaluations. Our study evaluated multiple DL models, including GRU, LSTM and BI-LSTM, optimized with 70 neurons, where BI-LSTM achieved the lowest RMSE and Max Error. As an extension, we implemented the CNN2D algorithm, known for its superior feature selection and optimization capabilities using convolutional layers and MaxPooling2D. CNN2D outperformed previous models with reduced RMSE and Max Error. Additionally, we developed a Flask-based application enabling users to predict SOC by uploading CSV data, enhancing accessibility and usability
ENHANCING THE CLASSIFICATION EFFICACY OF ASPHALT CRACKS POST-EARTHQUAKE VIA AN INNOVATIVE FEATURE SELECTION ALGORITHM
No full textNovel imaging and AI approach, DeepCurvMRI, enhances AD diagnosis. Over 50 million individuals globally are impacted by AD, and that number is projected to increase. It is absolutely critical to detect the disease early and accurately. De-stigmatizing Alzheimer's disease diagnosis, DeepCurvMRI employs Curvelet Transform to extract characteristics from MRI scans. The basic study showed that DeepCurvMRI, which employs a CNN architecture specifically built for AD detection, attained an accuracy rate of 98%. This research takes a look into Xception and DenseNet deep learning models, together with Decision Trees and Voting Classifier. Preliminary research suggests a 99%+ accuracy rate. Numerous implications stem from this research. Doctors are able to intervene earlier when diagnostics are more precise, which benefits patients and their families. Improvements in AD diagnosis also help society with optimal resource allocation and reduced healthcare costs
Optimization of drilling parameters for drilling of TI-6Al-4V based on surface roughness, flank wear and drill vibration
No full textMachining of titanium alloys is difficult due to their low elasticity, high formability and tendency of breakage. In drilling of TI -6Al- 4V alloy, drill bits are subjected to chatter vibration
and it causes poor surface finish and tool failure. In this study, effect of drilling parameters such as spindle speed, helix angle and feed rate on surface roughness, flank wear and acceleration of drill vibration velocity was investigated using Response Surface Methodology. A Laser Doppler Vibrometer (LDV) was used to measure vibration of drill bit in the form of Acousto Optic Emission (AOE) signal. And these signals were transformed in to time domain with different time frequency zones using a high speed fast Fourier transformer. Experimental data were
analyzed using Response Surface Methodology (RSM) to identify significant parameters on surface roughness, flank wear and acceleration of drill vibration velocity. A multi response
optimization was performed to optimize drilling parameters for minimum surface roughness, flank wear and acceleration of drill vibration velocity. Optimum cutting parameters were found
as 26.16 degrees of helix angle, 10.0 mm/min of feed rate and 600rpm of spindle speed
Prediction of cutting tool wear, surface roughness and vibration of work piece in boring of AISI 316 steel with artificial neural network
No full textMachining of stainless steel is difficult due to their hardening tendency. In boring of stainless steels, tool wear and surface roughness are affected by vibration of boring bar. In this
paper, tool wear, surface roughness and vibration of work piece were studied in boring of AISI 316 steel with cemented carbide tool inserts. A Laser Doppler Vibrometer was used for
online data acquisition of work piece vibration and a high-speed Fast Fourier Transform analyzer was used to process the acousto optic emission signals for the work piece vibration. Experimental data was collected and imported to artificial neural network techniques. A multilayer perceptron model was used with back-propagation algorithm using the input parameters of nose radius, cutting speed, feed and volume of material removed. The artificial neural network was used to predict surface roughness, tool wear and amplitude of work piece vibration. The predicted values were compared with the collected experimental data and percentage error was compute
College of Nursing, Spring 2021
No full textFEATURES [Page] 2 University honors Abuatiq [Page] 4 Mehlhaff adds author to titles [Page] 5 West River Health Sciences Center gets established COLLEGE NEWS [Page] 7 RN to B.S.N. program changes [Page] 8 Program returns nurses to field [Page] 10 Impact of NANEC [Page] 11 New role for Arends [Page] 12 Carson, Burdette retire [Page] 14 Winterboer, Soholt honored RESEARCH [Page] 16 Mollman recognized for efforts STUDENT NEWS [Page] 20 Mother, daughter earn diplomas [Page] 22 Sigma Theta Tau anniversary [Page] 24 Engineering and nursing [Page] 26 Benefits of scholarships SDSU FOUNDATION [Page] 28 Sustaining scholarshipshttps://openprairie.sdstate.edu/con_mag/1012/thumbnail.jp
Cutting tool condition monitoring by analyzing surface roughness, work piece vibration and volume of metal removed for AISI 1040 steel in boring
No full textThe vibration is one of the intensive problems in boring process. Machining and tool wear are affected more by vibration of tool due to length of boring bar. The present work is to
estimate the effect of cutting parameters on work piece vibration, roughness on machined surface and volume of metal removed in boring of steel (AISI1040). A laser Doppler vibrometer (LDV) was used for online data acquisition and a high-speed FFT analyzer used to process the AOE signals for work piece vibration. A design of experiments was prepared with
eight experiments with two levels of cutting parameters such as spindle rotational speed, feed rate and tool nose radius. Taguchi method has been used to optimize the cutting parameters and a multiple regression analysis is done to obtain the empirical relation of Tool life with roughness of machined surface, volume of metal removed and amplitude of work piece vibration
ICT in Education: A Study of Public Health Education
No full textModern technologies such as Information Communication Technologies have helped many of the development sectors. One of the sectors it has lot of scope to develop is the Education. It is also evident from the experience that the benefits of these technologies have contributed much in the area of healthcare. However, these benefits come with few limitations. A technology is useful only if (a) the systems are designed keeping the user perspective mind, (b) if the users are trained on those systems, (c) users recognize the need for a system and (d) users feel there is a need for such system. Developing a system for an application does not necessarily lead to usage. Many developments ended without giving any benefit to society. For the better usage and the benefits, one has to have a commitment to promote the system among the appropriate users by demonstrating the benefits of such systems. This further discouraged by the restrictions imposed by the IPR regime. There is some relief now due to the popularization of the free software movements. This paper is an effort to highlight the benefits of such systems in public health education with special reference to the open source online tools. Author is a faculty of a Public Health school teaching health management course to the students of public health. The paper addresses the importance of ICT systems in training the public health professionals. It also discusses the benefits and limitations of such system. The present system is a complementary teaching method to the existing classroom teaching.ICT Education, Online Tools, Learning Management System
The impact of Caputo-Fabrizio fractional derivative and the dynamics of noise on worm propagation in wireless IoT networks
No full textThe main objective of this article on Wireless sensor network of the Internet of Things (IoT). The wireless network, B bluetooth network, infrared network, and other networks are the key components of the Internet of Things (IoT). The major emphasis of this work was on the impact of Caputo-Fabrizio fractional derivative on worm propagation in heterogeneous susceptible-exposed-infected-recovered wireless IoT devices. We first determined the equilibrium points and fundamental reproduction number for the Caputo-Fabrizio HSEIR system, and then we discussed the stability of the system at the worm propagation equilibrium point. Using the Picard-Lindeof method, we determine the existence and unique solution for the fractional CF system of the heterogeneous SEIR model. Next, we use fixed point theory to judge the stability of the iterative method. We investigate the impact of the derivative order on the behaviour of the resultant functions and acquired numerical values by computing the model's findings for various fractional orders. In addition, we compute the integer-order model's results and contrast them with the results of the fractional-order model. We develop a periodically intermittent controller driven by white noise with the amazing benefits of reduced cost and more adaptable control technique to restrict the spread of worms in wireless IoT networks. To clearly define the conditions for stability in probability one, we employ the stochastic analysis technique. Our results show that the nonlinear worm propagation system may be stabilised by intermittent stochastic perturbation under the parameters of intermittent time linked to stochastic perturbation strength. Our theoretical conclusions may be used to analyse the observable processes of the worm, design countermeasures to prevent its spread, and evaluate the consequences of various system parameters
