Dr. Phate Mangesh Ravindra
@aissmscoe.com
Professor Mechanical
AISSMSCOE,PUNE,MAHARASHTRA, INDIA
EDUCATION
BE ( Mech),M Tech (Prod), MBA,PhD(Mech)
RESEARCH INTERESTS
Machining of composite material and ergonomics
Scopus Publications
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 R. Phate, Pratik P. Gaikwad, and Shraddha B. Toney
Springer Science and Business Media LLC
Mangesh Phate, Shraddha Toney, Vikas Phate, and Vivek Tatwawadi
Springer Science and Business Media LLC
G.G. Biswas and M.R. Phate
MAFTREE
In this new era, noise pollution is very high. One of the reasons for this noise pollution is sound which is generated by the exhaust system of the motorbike. So, it is necessary to reduce the noise coming from the exhaust of the motorbike. In this paper, original silencer reverse engineering has been done, then three modification models of the silencer with its analysis have been done and from those results, one of the modified silencers was selected and it was sent for manufacturing. After fabrication of the silencer, two tests were conducted. First, the test was conducted with original silencer, then it was conducted with modified silencer. From that data, transmission loss of original and modified silencers was calculated and then compared with each other and from that, noise was reduced to 5db. Also, flow velocity of exhaust gas in original silencer and modified silencer were also measured. It concluded that modified flow was increased by 11% to original silencer and also PUC of modified silencer was checked and it was in limit as per the PUC norms.
Dinesh Y. Dhande, Mangesh R. Phate, and Nazaruddin Sinaga
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 .
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.
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...
Mangesh R. Phate and Shraddha B. Toney
Elsevier BV
Abstract In the present work, The CNC wire electrical discharge machining (WEDM) of Al 2124 SiCp (0,15,20) Metal Matrix Composite (MMC) is analysed by using dimensional analysis approach (DA) & artificial neural network (ANN). The models are formulated to correlate the independent parameters such as pulse on time, pulse off time, wire feed rate, current, voltage, thermal conductivity of the work piece material, coefficient of thermal expansion, density and the wire tension with the dependent parameters surface roughness and the material removal rate through design of experiments (DOE) plan. From the experimental findings, it has been observed that the pulse on time, thermal conductivity, coefficient of thermal expansion, wire feed rate and the wire tension are the most influencing parameters. In order to find out the accuracy of the formulated DA and ANN models, correlation coefficient (R2) was calculated. From the R2 values, it was clear that both DA and ANN approaches are competent to predict the surface roughness and the material removal rate. In addition, the models formulated by using ANN approach were found to be more reliable than the DA approach. The higher values of R2 (99.9910%) and lower value of various error based parameters shows the adequacy and reliability of the DA and ANN models. Comparative study of DA and ANN models disclosed the accuracy of ANN models hence recommended.
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%.
Sunil Dambhare, Samir Deshmukh, Atul Borade, Abhijeet Digalwar, and Mangesh Phate
Elsevier BV
Abstract Manufacturing industries are crucial in a country's economy. However, they accounts for huge resource consumption of and waste excretion. The objective of this study was to investigate sustainability issues pertinent to turning process in a Indian machining industry. Parameters such as surface roughness, material removal rate and energy consumption were considered as sustainability factors. The effect of process parameters (speed/feed/depth of cut), the machining environment (dry/MQL/wet) and the type of cutting tool on the response was observed. Analysis of Variance (ANOVA) was applied to test the data. The process was analysed using response surface methodology (RSM). The results of the study helped to understand the effect of the cutting parameters on surface finish, energy consumption, and material removal rate. The process was optimized from power consumption point of view. Extended form of the model could be useful to predict the environmental impact of machining process which will bring environmental concern into conventional machining.