Formability of Materials with Small Tools in Incremental Forming

Single point incremental forming(SPIF)is an innovative sheet forming process with a high economic pay-off.The formability in this process can be maximized by executing forming with a tool of specific small radius,regarded as threshold critical radius.Its value has been reported as 2.2 mm for 1 mm thick sheet materials.However,with a change in the forming conditions specifically in the sheet thickness and step size,the critical radius is likely to alter due to a change in the bending condition.The main aim of the present study is to undertake this point into account and develop a relatively generic condition.The study is composed of experimental and numerical investigations.The maximum wall angle(θmax)without sheet fracturing is regarded as sheet formability.A number of sheet materials are formed to fracture and the trends correlating formability with normalized radius(i.e.,R/To where R is the tool-radius and To is the sheet thickness)are drawn.These trends confirm that there is a critical tool-radius(Rc)that maximizes the formability in SPIF.Furthermore,it is found that the critical radius is not fixed rather it shows dependence on the sheet thickness such that Rc=βTo,whereβvaries from 2.2 to 3.3 as the thickness increases from 1 mm to 3 mm.The critical radius,however,remains insensitive to variation in step size ranging from 0.3 mm to 0.7 mm.This is also observed that the selection of tool with R<Rc narrows down the formability window not only on the higher side but also on the lower side.The higher limit,as revealed by the experimental and FEA results,diminishes due to excessive shearing because of in-plane biaxial compression,and the lower limit reduces due to pillowing in the bottom of part.The new tool-radius condition proposed herein study would be helpful in maximizing the formability of materials in SPIF without performing experimental trials.

Addressing Economic Dispatch Problem with Multiple Fuels Using Oscillatory Particle Swarm Optimization

Economic dispatch has a significant effect on optimal economical operation in the power systems in industrial revolution 4.0 in terms of considerable savings in revenue.Various non-linearity are added to make the fossil fuel-based power systems more practical.In order to achieve an accurate economical schedule,valve point loading effect,ramp rate constraints,and prohibited operating zones are being considered for realistic scenarios.In this paper,an improved,and modified version of conventional particle swarm optimization(PSO),called Oscillatory PSO(OPSO),is devised to provide a cheaper schedule with optimum cost.The conventional PSO is improved by deriving a mechanism enabling the particle towards the trajectories of oscillatory motion to acquire the entire search space.A set of differential equations is implemented to expose the condition for trajectory motion in oscillation.Using adaptive inertia weights,this OPSO method provides an optimized cost of generation as compared to the conventional particle swarm optimization and other new meta-heuristic approaches.

RSS-Based Selective Clustering Technique Using Master Node for WSN

Wireless sensor networks(WSN)are designed to monitor the physical properties of the target area.The received signal strength(RSS)plays a significant role in reducing sensor node power consumption during data transmission.Proper utilization of RSS values with clustering is required to harvest the energy of each network node to prolong the network life span.This paper introduces the RSS-based energy-efficient selective clustering technique using a master node(RESCM)to improve energy utilization using a master node.The master node positioned at the center of the network area and base station(BS)is placed outside the network area.During cluster head(CH)selection,the node with a high RSS value is more likely to become CH.The network is divided into segments according to the distance from the master node.All nodes near BS or master node transmit their data using direct transmission without the clustering process.The simulation results showed that the RESCM method improves the total network lifespan effectively.

Classification and Categorization of COVID-19 Outbreak in Pakistan

Coronavirus is a potentially fatal disease that normally occurs in mammals and birds.Generally,in humans,the virus spreads through aerial droplets of any type of fluid secreted from the body of an infected person.Coronavirus is a family of viruses that is more lethal than other unpremeditated viruses.In December 2019,a new variant,i.e.,a novel coronavirus(COVID-19)developed in Wuhan province,China.Since January 23,2020,the number of infected individuals has increased rapidly,affecting the health and economies of many countries,including Pakistan.The objective of this research is to provide a system to classify and categorize the COVID-19 outbreak in Pakistan based on the data collected every day from different regions of Pakistan.This research also compares the performance of machine learning classifiers(i.e.,Decision Tree(DT),Naive Bayes(NB),Support Vector Machine,and Logistic Regression)on the COVID-19 dataset collected in Pakistan.According to the experimental results,DT and NB classifiers outperformed the other classifiers.In addition,the classified data is categorized by implementing a Bayesian Regularization Artificial Neural Network(BRANN)classifier.The results demonstrate that the BRANN classifier outperforms state-of-the-art classifiers.

Deep Neural Networks Based Approach for Battery Life Prediction

The Internet of Things(IoT)and related applications have witnessed enormous growth since its inception.The diversity of connecting devices and relevant applications have enabled the use of IoT devices in every domain.Although the applicability of these applications are predominant,battery life remains to be a major challenge for IoT devices,wherein unreliability and shortened life would make an IoT application completely useless.In this work,an optimized deep neural networks based model is used to predict the battery life of the IoT systems.The present study uses the Chicago Park Beach dataset collected from the publicly available data repository for the experimentation of the proposed methodology.The dataset is pre-processed using the attribute mean technique eliminating the missing values and then One-Hot encoding technique is implemented to convert it to numerical format.This processed data is normalized using the Standard Scaler technique.Moth Flame Optimization(MFO)Algorithm is then implemented for selecting the optimal features in the dataset.These optimal features are finally fed into the DNN model and the results generated are evaluated against the stateof-the-art models,which justify the superiority of the proposed MFO-DNN model.