Optimization of weld bead geometry for stainless steel claddings deposited by FCAW

The quality of cladded components depends on the weld bead geometry, coefficients of shape of welds and dilution, which have to be controlled. Optimum range of bead parameters and dilution are required for better economy and to ensure the desired mechanical and corrosion resistant properties of the overlay. The above objectives can easily be achieved by developing mathematical equations to predict the weld bead geometry. This paper presents the development of such equations using the data obtained by conducting three factor five level factorial experiments. The experiments were conducted by depositing Type AISI 317L flux cored stainless steel wire onto IS: 2062 structural steel base plate. The results of the confirmation experiments showed that the models developed are able to predict the bead geometries and dilution with reasonable accuracy. The studies have indicated that both main and interaction effects of the process variables play a major role in determining the bead dimensions and dilution, and the effect of interaction between the process variables cannot be neglected. The process parameters were also optimized using response surface methodology (RSM) which will help the plant engineers to select and control the process variables effectively, to achieve the desired clad qualities.     

Visibility improvement of underwater turbid image using hybrid restoration network with weighted filter

Due to the attenuation of light passes through water, the captured underwater images suffer from low-contrast, halo artifacts, etc. To address this issue, the hybrid network with a weighted filter is proposed to improve the visibility of the obscured (turbid) images. In the captured image, the brighter pixels (near-to-source) are called foreground regions and the darker pixels (far-from-source) are called background regions. In order to ensure the adaptability of the proposed algorithm, the considered datasets are collected on different atmospheric light such as pond, lake, and fisheries tank. The foreground area of an image can be enhanced using the thresholding and masking technique. The background hazy region can be recovered by a hybrid Dehazenet called Generative Adversarial Network and Convolutional Neural Network. With this, the transmission map with high accuracy and color deviation can be addressed. Then both the regions are blended and the Amended Unsharp Mask filter is used to toughen the distorted edges. Finally, the blended restored image is weighted with a contrast factor to obtain the visibility improved image. The subjective and objective evaluation is done on considering the standard non-reference metric called Underwater Image Quality Measure comprises measures of color, sharpness, and contrast for a variety of water types with different atmospheric light. It is observed that the proposed technique showed a metric improvement of 57% compared to other existing techniques in an average manner. Overall, it is inferred that the proposed technique produces better results in both subjective and objective evaluation, thus it outperforms other state-of-the-art techniques.

     

Decision Tree-Based Entries Reduction scheme using multi-match attributes to prevent flow table overflow in SDN environment

The software-defined networking is used extensively in data centers that provide centralized control for the widely deployed networking resources. The traffic is shaped by rules created by the controller dynamically without modifying the individual switch. The key component that stores rules which are used to process the flows is the flow table which resides in the ternary content addressable memory. The current commercial OpenFlow appliances accommodate limited entries up to 8000 due to its high cost and high power consumption. There are two issues to be considered, where (1) flow table's inability to provide rules during flow table overflow leads to dropping of incoming packets and (2) the significant amount of rule replacement occurs when the traffic in data centers increases which creates massive route requests to controller creating overhead. The proposed scheme prevents flow table overflow using the robust machine learning algorithm called decision tree (Iterative Dichotomiser 3) that allows the flow table to learn its high prioritized fine-grained entries by means of multiple matching attributes. The entries are classified, and the usual eviction process is replaced by pushing the low important entries into counting bloom filter which acts as a cache to prevent flow entry miss. The simulations were carried out using real-time network traffic datasets, and the comparisons with the various existing schemes prove that the proposed approach reduces 99.99% of the controller's overhead and the entries are minimized to 99% providing extra space for new flows.     

QoS Aware Trust Based Routing Algorithm for Wireless Sensor Networks

Wireless Personal Communications - In Wireless Sensor Network (WSN), the lifetime optimization based on minimal energy consumption and security are the crucial issues for the effective design of...     

Additive Number Theory via Automata Theory

Theory of Computing Systems - We show how some problems in additive number theory can be attacked in a novel way, using techniques from the theory of finite automata. We start by recalling the...     

Modeling and simulation of wire feed rate for steady current and pulsed current gas metal arc welding using 317L flux cored wire

Wire feed rate plays a vital role in determining the weld characteristics in gas metal arc welding (GMAW). The wire feed rate is affected by any change in welding current in the case of steady current GMA welding and by any change in frequency, peak current, base current and duration of peak and base currents in the case of pulsed GMA welding. To predict the wire feed rate for any set of these parameters, a mathematical model was developed from the results obtained by conducting experiments. Electrode resistance heating constant and arc resistance heating constant were also determined by fitting a regression model. The above parametric constants have been used to simulate the wire feed rates for pulsed GMA welding for different pulse parameters using MATLAB. The effects of pulse parameters on the burnoff factor and burnoff rates were also analysed. The investigation was carried out using AWS 5.22–95 filler wire of size 1.2 mm diameter and the base metal used was IS:2062 structural steel plate of 20 mm thickness. An argon and 5% CO₂ gas mixture at a flow rate of 16 l/min was used for shielding throughout the welding.     

Optimization of hybrid laser–TIG welding of 316LN stainless steel using genetic algorithm

Determination of optimum hybrid laser–TIG welding process variables for achieving the maximum depth of penetration (DOP) in type 316LN stainless steel has been carried out using a genetic algorithm (GA). Nd:YAG pulsed laser and the TIG heat source were coupled at the weld pool to carry out hybrid welding. Design of experiments approach was used to generate the experimental design matrix. Bead-on-plate welds were carried out based on the design matrix. The input variables considered were laser power, pulse frequency, pulse duration, and TIG current. The response variable considered was the DOP. Multiple-regression model was developed correlating the process variables with the DOP using the generated data. The regression model was used for evaluating the objective function in GA. GA-based model was developed and it produced a set of solutions. Tournament and roulette wheel selection methods were used during the execution of GA. It was found that both the selection methods identified similar welding process parameters for achieving the maximum DOP. Excellent agreement was observed between the target DOP and the DOP values obtained in the validation experiments during hybrid laser–TIG welding.     

A robust similarity based deep siamese convolutional neural network for gait recognition across views

Gait recognition has been considered as the emerging biometric technology for identifying the walking behaviors of humans. The major challenges addressed in this article is significant variation caused by covariate factors such as clothing, carrying conditions and view angle variations will undesirably affect the recognition performance of gait. In recent years, deep learning technique has produced a phenomenal performance accuracy on various challenging problems based on classification. Due to an enormous amount of data in the real world, convolutional neural network will approximate complex nonlinear functions in models to develop a generalized deep convolutional neural network (DCNN) architecture for gait recognition. DCNN can handle relatively large multiview datasets with or without using any data augmentation and fine-tuning techniques. This article proposes a color-mapped contour gait image as gait feature for addressing the variations caused by the cofactors and gait recognition across views. We have also compared the various edge detection algorithms for gait template generation and chosen the best from among them. The databases considered for our work includes the most widely used CASIA-B dataset and OULP database. Our experiments show significant improvement in the gait recognition for fixed-view, crossview, and multiview compared with the recent methodologies.     

High Velocity Oxy Fuel Spraying of Cold Work Tool Steels‐ A Novel Approach to Thick Coatings for Wear Protection Applications

Within this work, HVOF sprayed coatings based on X220CrVMo13‐4 cold work steel were applied to a S235JR construction steel substrate. The investigations focus on the influence of particle size and spray parameters on the coating microstructure, analyzed by means of optical microscope (OM) and scanning electron microscopy (SEM). Additional XRD measurements and micro hardness plots across the interface between substrate material and coating were carried out. Furthermore, the influence of particle size on the detected phases and coating porosity was studied. The results were compared with an X220CrMoV13‐4 reference sample produced by HIP.