Dr. Vikas R. Phate
@gpamravati.ac.in
Senior Lecturer in Electronics and Telecommunication Engineering
Government Polytechnic, Amravati (MS)
EDUCATION
Ph.D. (ECE)
RESEARCH INTERESTS
Computer Vision, Soft Computing, Machine Learning, Mathematical Modelling.
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Mangesh Phate, Shraddha Toney, and Vikas Phate
Inderscience Publishers
Mangesh Phate, Shraddha Toney, and Vikas Phate
Inderscience Publishers
Mangesh Phate, Shraddha Toney, and Vikas Phate
Informa UK Limited
This paper presents an investigation of wire electrical discharge machining (WEDM) of new fabricated aluminium base composite with 5% graphite by weight, i.e., Al/Gr/Cp5. The new fabricated metal m...
Mangesh Phate, Shraddha Toney, Vikas Phate, and Vivek Tatwawadi
Springer Science and Business Media LLC
Mangesh R. Phate, Shraddha B. Toney, and Vikas R. Phate
Springer Science and Business Media LLC
Wire electrical discharge machining (WEDM) process optimization is essential when any novel material is discovered. The WEDM process has numerous variables which affect multiple responses. Therefore, multi-response optimization needs to be performed with the help of advanced optimization techniques. Multi-parametric optimization of the WEDM for processing aluminium silicate composite with 15–20% silicate (designated as Al/SiCp) was examined in the present work. A composite principal component was calculated using principal component analysis for multi-parametric optimization. The artificial neural network was employed for enhancing the performance of the process. Analysis of variance was performed to realize the influence of WEDM process parameters on the overall WEDM effectiveness. The WEDM response characteristics such as finish part roughness (Ra), material removal rate and kerf width were considered for this work. From the experimental findings, it is observed that the parameters, viz. the % composition of silicate, the pulse off time (POFF) and current (IP), are the most critical process parameters. The parameters obtained through the present analysis were silicate composition 15%, pulse on time 112 μs, POFF 56 μs, IP-3 A, wire feed rate 4 m/min, wire tension 10 kg and fluid pressure 13 kg/cm 3 .
Vikas R. Phate, R. Malmathanraj, and P. Palanisamy
Informa UK Limited
In the past few decades, both academicians and industries have shown interest toward the agricultural post-harvest operation aiming to reduce the post-harvest losses. In order to assist farmers in ...
Mangesh Phate, Aditya Bendale, Shraddha Toney, and Vikas Phate
Elsevier BV
Aluminum (Al)-copper (Cu)-nickel (Ni) alloy is a versatile material with lightweight and excellent strength. It also possesses properties such as superior corrosion resistance, fatigue strength. These alloys are essential in sectors viz. automobile, aerospace, defense, aerospace, etc. In this research work, the authors have presented the prediction and analysis of tool wear rate (TWR). The impact of electrical discharge machining (EDM) on process parameters viz. input current (IP), pulse on time (TON), pulse off time (TOFF)/for Al/Cu/Ni alloy with the composition 91/4/5 and 87/8/5 (weight %) is analyzed. Taguchi's L18 (21∗33) mixed plan is employed to plan the experimentation. A mathematical model develops to correlate these process parameters. A soft computing technique known as an adaptive neuro-fuzzy inference system (ANFIS) utilizes to predict TWR. Taguchi analysis reveals that input current is the most influencing parameter followed by pulse on time. TWR decreases with a decrease in the amount of Aluminium. It increases in the amount of copper in the alloy. TWR firstly decreases with an increase in pulse on time and then starts to grow after the median value of 25 micro-sec. The confirmation experiments have conducted using optimum process parameters to validate the obtained results. The experimental finding shows the superior capability of ANFIS to predict the TWR with acceptable accuracy. The optimized TWR obtained was 0.1238 mm3/min based on the optimal settings of input parameters.
Vikas R. Phate, R. Malmathanraj, and P. Palanisamy
IEEE
A fast and indirect method of weighing the sweet lime fruit developed based on the computer vision coupled with machine learning algorithm is investigated in this research work. The developed computer vision system (CVS) has been used to analyze the sweet lime image database. The images have been processed using the developed algorithm to extract seven geometrical attributes. The support vector machine regression (SVMR) modelling technique has been utilized to develop the model for estimating the weight of fruit samples under consideration. Eight different SVMR models have been developed in two SVM type for different kernel type. Relevant statistical analysis and comparison of the developed model is also presented. Finally, the type 2 SVMR model with RBF kernel has been recommended as the model with best performance during training ($R^{2}=$ 0.9867, RMSE = 5.26) and testing ($R^{2} =$ 0.9866, RMSE = 6.435) too. Thus, the presented work provides an indirect way for measuring sweet lime fruit size to estimate its weight. This will be helpful in the design and development of most of the post-harvest equipment.
M. Phate, S. Toney and V. Phate
In the present work, a model based on Dimensional Analysis (DA) coupled with the Taguchi method to analyze the impact of silicon carbide (SiC) was presented. The Wire Cut Electrical Discharge Machining (WEDM) performance of Aluminium Silicon Carbide (AlSiC) Metal Matrix Composite (MMC) was critically examined. To formulate DA-based models, a total of 18 experiments were conducted using Taguchi’s L18 mixed plan of experimentation. The input data used in the DA models include a pulse on time, pulse off time, wire feed rate, % SiC, wire tension, flushing pressure, etc. According to these process parameters, DA models for the surface roughness and the material removal rate were predicted. The formulated DA models showed a strong correlation with the experimental data. The analysis of variance (ANOVA) was applied to determine the impact of individual parameters on response parameters.
Mangesh Phate, Shraddha Toney, and Vikas Phate
Informa UK Limited
The paper presents the prediction of surface quality during the electro-discharge machining (EDM) of aluminium-based alloy. The composition consists of Aluminium, copper, nickel (Al/Cu/Ni) alloy. T...
Vikas R. Phate, Ramanathan Malmathanraj, and Ponnusamy Palanisamy
Wiley
Vikas R. Phate, R. Malmathanraj, and P. Palanisamy
Springer Science and Business Media LLC
Weight is widely used as an important measure to study the physiology and agronomy for monitoring the fruit growth, grading, and packaging. The development of a computer vision system to measure the sweet lime fruit weight by relating the weight with its physical attributes is economically efficient than the mechanical online load cell used in the fruit sorting machines. In the present work, firstly a classification tree is developed using classification and regression tree algorithm to classify the fruits based on size. The average accuracy, sensitivity, specificity, and F score achieved are 98.16%, 94.01%, 98.51%, and 94.85% respectively. Secondly, parametric and non- parametric models are developed for predicting the weight of these classified fruits. A non-parametric model is developed using feed forward artificial neural network (FFANN) with error back propagation. The best topology is found among the fifty different FFANN configurations formed by varying the count of neurons in the hidden layer. Two parametric models are also developed using an approach of dimensional analysis (DA), and normal regression (NR). If the volume and the weight of the fruit have high correlation; then the bulk density of the fruit is fairly constant. This is the hypothesis used for developing the DA model. A lower value of mean square relative error and the remarkable value of Nash–Sutcliffe coefficient of efficiency indicate the superiority and the robustness of the proposed NR model in estimating the weight of the sweet lime fruits. Furthermore, an estimation uncertainty Theil_UII value which demonstrates the effectiveness and the credibility of the model’s estimation ability is used for performance evaluation.
Mangesh R. Phate, Shraddha B. Toney, and Vikas R. Phate
Hindawi Limited
Aluminium silicate metal matrix composite (AlSiC MMC) is satisfying the requirement of material with good mechanical, thermal properties, and good wear resistance. But the difficulties during the machining are the main hurdles to its replacement for other materials. Wire electric discharge machining (WEDM) is a very effective process used for this type of difficult-to-cut material. So an effort has been taken to find out the most favourable level of input parameters for WEDM of AlSiC (20%) composite using a Taguchi-based hybrid grey-fuzzy grade (GFG) approach. The plan for experimentation is designed using Taguchi’s L9 (23) array. The various process parameters considered for the investigation are pulse on time (TON), pulse off time (TOFF), wire feed rate (WFR), and peak current (IP). Surface integrity such as surface roughness measured during the different types of cutting (along straight, inclined, and curvature directions) is considered in the present work. Grey relational analysis (GRA) pooled with the fuzzy logic is effectively used to find out the grey-fuzzy reasoning grade (GFRG). The Taguchi approach is coupled with the GFRG to obtain the optimum set of process parameters. From the experimental findings, it has been observed that the most economical process parameters for WEDM of AlSiCp20 were the pulse on time is 108 microsec, pulse off time is 56 microsec, wire feed rate (WFR) is 4 m/min, and peak current (IP) is 11 amp. From the analysis of variance (ANOVA), it is observed that the pulse on time is the foremost influencing parameters that contribute towards GFRG by 52.61%, followed by the wire feed rate (WFR) 38.32% and the current by 5.45%.