RSM and ANN Modeling of Micro Wire Electrical Discharge Machining of AL 2024 T351

This paper presents modeling and analysis of machining characteristics of Micro Wire Electro Discharge Machining (Micro-WEDM) process on Aluminium alloy (AL 2024 T351) using the Response Surface Methodology (RSM) and Artificial Neural Network (ANN). The input variables of Micro-WEDM process were voltage, capacitance and feed rate. The surface roughness and material removal rate are considered as a response variables. Experiments were carried out on Aluminium alloy using Central Composite Design (CCD). The RSM and ANN models have been developed based on experimental designs. Analysis of variance (ANOVA) has been employed to test the significance of RSM model. It has been found out that all the three process parameters are significant and their interaction effects are also significant on the surface roughness and material removal rate. Finally predicted values were compared with ANN

Thermo-mechanical behavior of aluminum matrix nano-composite automobile disc brake rotor using finite element method

Analysis of mechanical and thermal behaviors during braking has become an increasingly important issue in many transport sectors for different modes of transportation. Brake failure generated during braking is a complex phenomenon confronting automobile manufacturers and designers. During braking, kinetic energy is transferred to thermal energy, resulting in the intense heating of disc brake rotors that increases proportionally with vehicle speed, mass, and braking frequency. It is essential to look into and improve strategies to make versatile, thermally resistant, lightweight, high-performance discs. As a result, this study uses the finite element method to conduct a thermo-mechanical analysis of aluminum alloy and aluminum matrix nano-composite disc brake rotors to address the abovementioned issues. The FEA method is used for the thermo-mechanical analysis of AMNCs for vented disc brake rotor during emergency braking at 70 km/h. From the results obtained, aluminum base metal matrix nano-composites have an excellent strength-to-weight ratio when used as disc brake rotor materials, significantly improving the discs' thermal and mechanical performance. From the result of transient thermal analysis, the maximum value of heat flux obtained for aluminum alloy disc is about 8 W/mm(2), whereas for AMNCs, the value is increased to 16.28 W/mm(2). The result from static analysis shows that the maximum deformation observed is 0.19 mm for aluminum alloy disc and 0.05 mm for AMNCs disc. In addition, the maximum von Mises stress value of AMNC disc is about 184 MPa. The maximum von Mises stress value of aluminum alloy disc is about 180 MPa. Therefore, according to the results, the proposed aluminum base metal matrix nano-composites are valid for replacing existing materials for disc brake rotor applications.Web of Science1517art. no. 607

A faster RCNN based diabetic retinopathy detection method using fused features from retina images

Early identification of diabetic retinopathy (DR) is critical as it shows few symptoms at the primary stages due to the nature of its gradual and slow growth. DR must be detected at the early stage to receive appropriate treatment, which can prevent the condition from escalating to severe vision loss problems. The current study proposes an automatic and intelligent system to classify DR or normal condition from retina fundus images (FI). Firstly, the relevant FIs were pre-processed, followed by extracting discriminating features using histograms of oriented gradient (HOG), Shearlet transform, and Region-Based Convolutional Neural Network (RCNN) from FIs and merging them as one fused feature vector. By using the fused features, a machine learning (ML) based faster RCNN classifier was employed to identify the DR condition and DR lesions. An extended experiment was carried out by employing binary classification (normal and DR) from three publicly available datasets. With a testing accuracy of 98.58%, specificity of 97.12%, and sensitivity of 95.72%, this proposed faster RCNN deep learning technique with feature fusion ensured a satisfactory performance in identifying the DR compared to the relevant state-of-the-art works. By using a generalization validation strategy, this fusion-based method achieved a competitive performance with a detection accuracy of 95.75%

Designing Highly Sensitive Surface Plasmon Resonance Sensor With Dual Analyte Channels

The ease of controlling waveguide properties through unparalleled design flexibility has made the photonic crystal fiber (PCF) an attractive platform for plasmonic structures. In this work, a dual analyte channel’s highly sensitive PCF bio-sensor is proposed based on surface plasmon resonance (SPR). In the proposed design, surface plasmons (SPs) are excited in the inner flat portion of two rectangular analyte channels where gold (Au) strip is deposited. Thus, the surface roughness that might be generated during metal deposition on circular surface could be effectively reduced. Considering the refractive index (RI) change in the analyte channels, the proposed sensor is designed and fully characterized by the finite element method based COMSOL Multiphysics software. Improved sensing characteristics including wavelength sensitivity (WS) of 186,000 nm/RIU and amplitude sensitivity (AS) of 2,792.97 RIU −1 in the wide RI range of 1.30 to 1.43 is obtained. In addition, the proposed sensor exhibits excellent resolution of 5.38×10−7 , signal to noise ration (SNR) of 13.44 dB, figure of merits (FOM) of 2188.23, detection limit (DL) of 101.05 nm, and detection accuracy (DA) of 0.0204 nm −1 . Outcomes of the analysis indicate that the proposed sensor could be suited for accurate detection of organic chemicals, bio-molecules, and biological analytes

Experimental investigation into the influence of the process parameters of wire electric discharge machining using Nimonic-263 superalloy

Nimonic alloy is difficult to machine using traditional metal cutting techniques because of the high cutting forces required, poor surface integrity, and tool wear. Wire electrical discharge machining (WEDM) is used in a number of sectors to precisely machine complex forms of nickel-based alloy in order to attempt to overcome these challenges and provide high-quality products. The Taguchi-based design of experiments is utilized in this study to conduct the tests and analyses. The gap voltage (GV), pulse-on time (Ton), pulse-off time (Toff), and wire feed (WF), are considered as the variable process factors. GRA is used for the WEDM process optimization for the Nimonic-263 superalloy, which has multiple performance qualities including the material removal rate (MRR), surface roughness (SR), and kerf width (KW). ANOVA analysis was conducted to determine the factors' importance and influence on the output variables. Multi objective optimization techniques were employed for assessing the machining performances of WEDM using GRA. The ideal input parameter combinations were determined to be a gap voltage (GV) of 40 V, a pulse-on time (Ton) of 8 & mu;s, a pulse-off time (Toff) of 16 & mu;s, and a wire feed (WF) of 4 m/min. A material removal rate of 8.238 mm(3)/min, surface roughness of 2.83 & mu;m, and kerf width of 0.343 mm were obtained. The validation experiments conducted also demonstrated that the predicted and experimental values could accurately forecast the responses.Web of Science1615art. no. 544

Modelling and Simulation of Machining Attributes in dry Turning of Aircraft Materials Nimonic C263 using CBN

In the current scenario, machinability of the super alloys is of greater importance in an aircraft turbine engine and land-based turbine applications owing to its superior properties. However, the machinability of these alloys is found to be poor owing to its inherent properties. Hence, a predictive model has been developed based on DEFORM 3D to forecast the machining attributes such as cutting force and insert's cutting edge temperature in turning of Nimonic C263 super alloy. The dry turning trials on Nimonic C263 material were carried out based on L27 orthogonal array using CBN insert. Linear regression models were developed to predict the machining attributes. Further, multi response optimization was carried out based on desirability approach for optimizing the machining attributes. The validation test was carried out for optimal parameter values such as cutting speed: 117 m/min, feed rate: 0.055 mm/rev and depth of cut: 0.25 mm. The minimum cutting force of 304N and insert's cutting edge temperature of 468 °C were obtained at optimum level of parameters.The predicted values by FEA and linear regression model were compared with experimental results and found to be closer with minimum percentage error.The minimum percentage error obtained by FEA and linear regression model for the machining attributes (cutting force, temperature) as compared with experimental values were (0.32%, 0.23%) and (2.34%, 1.63%) respectively

Optimization of a photovoltaic/wind/battery energy-based microgrid in distribution network using machine learning and fuzzy multi-objective improved Kepler optimizer algorithms

Abstract In this study, a fuzzy multi-objective framework is performed for optimization of a hybrid microgrid (HMG) including photovoltaic (PV) and wind energy sources linked with battery energy storage (PV/WT/BES) in a 33-bus distribution network to minimize the cost of energy losses, minimizing the voltage oscillations as well as power purchased minimization from the HMG incorporated forecasted data. The variables are microgrid optimal location and capacity of the HMG components in the network which are determined through a multi-objective improved Kepler optimization algorithm (MOIKOA) modeled by Kepler’s laws of planetary motion, piecewise linear chaotic map and using the FDMT. In this study, a machine learning approach using a multilayer perceptron artificial neural network (MLP-ANN) has been used to forecast solar radiation, wind speed, temperature, and load data. The optimization problem is implemented in three optimization scenarios based on real and forecasted data as well as the investigation of the battery's depth of discharge in the HMG optimization in the distribution network and its effects on the different objectives. The results including energy losses, voltage deviations, and purchased power from the HMG have been presented. Also, the MOIKOA superior capability is validated in comparison with the multi-objective conventional Kepler optimization algorithm, multi-objective particle swarm optimization, and multi-objective genetic algorithm in problem-solving. The findings are cleared that microgrid multi-objective optimization in the distribution network considering forecasted data based on the MLP-ANN causes an increase of 3.50%, 2.33%, and 1.98%, respectively, in annual energy losses, voltage deviation, and the purchased power cost from the HMG compared to the real data-based optimization. Also, the outcomes proved that increasing the battery depth of discharge causes the BES to have more participation in the HMG effectiveness on the distribution network objectives and affects the network energy losses and voltage deviation reduction

Optimization of machining parameters in drilling of LM6/B

Metal matrix composites (MMCs), innovative replacements for traditional materials, are currently achieving a growing trend in engineering and research for operations like aviation, nuclear power, and automotive. Machining of MMCs makes it challenging to get good dimensional accuracy, surface finish and lower tool wear. Drilling is a necessary and immensely useful tool for component assembly in the manufacturing sector. As a result, optimization of drilling process variables is unavoidable. The fundamental purpose of this study is to use the stir casting technique to manufacture LM6/B4C/Fly ash composites with 3, 6 and 9 wt.% of second phase materials. Taguchi's design of experiments strategy was used to drill with three levels of feed rate (F), spindle speed (S), drill material (D) and percentage of reinforcement (R) as input process parameters. Optimization of drilling variables for attaining lower surface roughness (SR) and burr height (BH) using single objective approach. The optimum process variable achieved for surface roughness is F1S3D3R2, i.e., 50 mm/min, 3000 rpm, TiN-coated drill bit and 6 wt.% of reinforcements (B4C and Fly-ash) and for burr height is F1S3D3R3, i.e., 50 mm/min, 3000 rpm, TiN-coated drill bit and 9 wt.% of reinforcements

Vibration Studies on Fiber Reinforced Composites – a Review

Fiber reinforced composites materials have been much more attention in the materials system by the researchers since their unique performance in various structural applications. The addition of fiber and various chemical treatments produces the best mechanical properties of the composite material. However, several authors have described the working mechanism in mechanical composites. Recently researchers have started to investigate the free vibrational behavior of the composites and to find the suitable applications in industry. It is indeed important to represent the vibrational response of the fiber reinforced composites (FRC) toward the anti-vibration applications. Thus, an attempt was made to review the vibration behaviors on natural fiber, synthetic fiber, and filler incorporated natural/synthetic fiber composites. Discussions on the comparison of the natural frequency and damping ratio of various composites bring comprehensive knowledge on vibrational behaviors of various fiber reinforced composites