This study investigates the effect of electric discharge machining (EDM) process parameters [curr... more This study investigates the effect of electric discharge machining (EDM) process parameters [current, pulse-on time (Ton), pulse-off time (Toff) and electrode material] on material removal rate (MRR), electrode wear rate (EWR) and surface roughness (SR) during machining of aluminum boron carbide (Al–B4C) composite. This article also summarizes a brief literature review related to aluminum metal matrix composites (Al-MMCs) based on different process and response parameters, work and tool material along with their sizes, dielectric fluid and different optimization techniques used. The MMC used in the present work is stir casted using 5% (wt) B4C particles of 50 micron size in Al 6061 metal matrix. Taguchi technique is used for the design of experiments (L9-orthogonal array), while the experimental results are analyzed using analysis of variance (ANOVA). Response table for average value of MRR, EWR and SR shows that current is the most significant factor for MRR and SR, while electrode material is most important for EWR. ANOVA also confirms similar results. It is also observed that the optimum level of process parameters for maximum MRR is A3B1C3D3, for minimum EWR is A1B2C3D1, and for SR is A1B3C3D3.
In the present work, vehicular traffic noise prediction models have been developed for Patiala ci... more In the present work, vehicular traffic noise prediction models have been developed for Patiala city (Punjab) using GA and regression approach. The various terminologies related to GA and acoustics analysis are discussed. The models predict equivalent continuous sound level (Leq) as the function of vehicle volume (Log Q) and percentage of heavy vehicles (P %). A large number of data have recorded at different dates/timings to account variability. Three commonly used GA selection operators (uniform, roulette wheel, and tournament) are used to analyze the accuracy of GA models. The GA model performs better as compared to regression model. The average mean square error (MSE) using GA model is 0.59 as compared to 0.76 for regression model. Among all GA selection operators, tournament selection shows better result.
The focus of the present study is to investigate the scuffing phenomenon of EN 31(Pin) against EN... more The focus of the present study is to investigate the scuffing phenomenon of EN 31(Pin) against EN 19(Disc) steel under dry running condition using pin-on-disc machine. Coefficient of friction (COF) and pin wear are experimentally measured for different combination of loads (10N-70N) and rotational speeds (200 rpm-2000 rpm). The speed and load at which transition to scuffing occur are also examined. It is observed that the wear resistance increases with sliding velocity for lower load, while at higher load it increases initially and then becomes stable at higher speed. The COF increases with sliding velocity at lower loads and then becomes steady, while at higher loads, the COF first increases with increase in speed and then decreases considerably. This abrupt decrease in friction coefficient is due to intense heat generation between the disc and the pin and the experiment is stopped due to higher noise and vibration. Archard’s wear model is also used to validate the wear of pin at different loads and speeds. Experimentally measured pin wear shows higher value probably due to decreases in material hardness with increase in interface temperature.
This paper presents an experimental study of pressure distribution on hydrodynamic journal bearin... more This paper presents an experimental study of pressure distribution on hydrodynamic journal bearing with SAE 10W30 multi grade oil. Hydrodynamic Journal bearing test rig (HJBTR) is used to test the 40 mm diameter and 40 mm long bearing (l/d = 1) made of Bronze. Test bearing is located between two antifriction bearings and loaded mechanically. The space between the shaft and the bearing is filled with SAE 10W30. A constant load of 800 N is applied at various journal rotational speeds of 1000, 1500, 2000 rpm. Various parameters like frictional torque, oil temperature and pressure at 10 different sensors along circumferential direction were recorded from Hydrodynamic Journal Bearing Test Rig (HJBTR). These results were used for experimental calculations and theoretical verification using Raimondi and Boyd charts for practical design. The experimental plot of pressure ratio vs sommerfeld number indicates that the working conditions are in the stable hydrodynamic regime. Also experimental results were following the same trend as MCKEE‟s investigation curve.
In India, the transportation sector is growing rapidly and the number of vehicles on Indian
roads... more In India, the transportation sector is growing rapidly and the number of vehicles on Indian roads is increasing at a very fast rate leading to overcrowded roads and noise pollution. The traffic scenario is typically different from other countries due to predominance of a variety of two-wheelers which has doubled in the last decade and forms a major chunk of heterogeneous volume of vehicles. Also tendency of not following the traffic norms and poor maintenance adds to the noise generation. In the present study, Multilayer feed forward back propagation (BP) neural network has been trained by Levenberg–Marquardt (L–M) algorithm to develop an Artificial Neural Network (ANN) model for predicting highway traffic noise. The developed ANN model is used to predict 10 Percentile exceeded sound level (L10) and Equivalent continuous sound level (Leq) in dB (A). The model input parameters are total vehicle volume/hour, percentage of heavy vehicles and average vehicle speed. The predicted highway noise descriptors, Leq and L10 from ANN approach and regression analysis have also been compared with the field measurement. The results show that the percentage difference is much less using ANN approach as compared to regression analysis. Further goodness-of-fit of the models against field data has been checked by statistical t-test at 5% significance level and proved the Artificial Neural Network (ANN) approach as a powerful technique for traffic noise modeling.
The major contribution of the traffic noise, towards overall noise pollution scenario, is a well ... more The major contribution of the traffic noise, towards overall noise pollution scenario, is a well known established fact. Traffic noise from highways creates problems for surrounding areas, especially when there are high traffic volumes and high speeds.
This study investigates the effect of electric discharge machining (EDM) process parameters [curr... more This study investigates the effect of electric discharge machining (EDM) process parameters [current, pulse-on time (Ton), pulse-off time (Toff) and electrode material] on material removal rate (MRR), electrode wear rate (EWR) and surface roughness (SR) during machining of aluminum boron carbide (Al–B4C) composite. This article also summarizes a brief literature review related to aluminum metal matrix composites (Al-MMCs) based on different process and response parameters, work and tool material along with their sizes, dielectric fluid and different optimization techniques used. The MMC used in the present work is stir casted using 5% (wt) B4C particles of 50 micron size in Al 6061 metal matrix. Taguchi technique is used for the design of experiments (L9-orthogonal array), while the experimental results are analyzed using analysis of variance (ANOVA). Response table for average value of MRR, EWR and SR shows that current is the most significant factor for MRR and SR, while electrode material is most important for EWR. ANOVA also confirms similar results. It is also observed that the optimum level of process parameters for maximum MRR is A3B1C3D3, for minimum EWR is A1B2C3D1, and for SR is A1B3C3D3.
In the present work, vehicular traffic noise prediction models have been developed for Patiala ci... more In the present work, vehicular traffic noise prediction models have been developed for Patiala city (Punjab) using GA and regression approach. The various terminologies related to GA and acoustics analysis are discussed. The models predict equivalent continuous sound level (Leq) as the function of vehicle volume (Log Q) and percentage of heavy vehicles (P %). A large number of data have recorded at different dates/timings to account variability. Three commonly used GA selection operators (uniform, roulette wheel, and tournament) are used to analyze the accuracy of GA models. The GA model performs better as compared to regression model. The average mean square error (MSE) using GA model is 0.59 as compared to 0.76 for regression model. Among all GA selection operators, tournament selection shows better result.
The focus of the present study is to investigate the scuffing phenomenon of EN 31(Pin) against EN... more The focus of the present study is to investigate the scuffing phenomenon of EN 31(Pin) against EN 19(Disc) steel under dry running condition using pin-on-disc machine. Coefficient of friction (COF) and pin wear are experimentally measured for different combination of loads (10N-70N) and rotational speeds (200 rpm-2000 rpm). The speed and load at which transition to scuffing occur are also examined. It is observed that the wear resistance increases with sliding velocity for lower load, while at higher load it increases initially and then becomes stable at higher speed. The COF increases with sliding velocity at lower loads and then becomes steady, while at higher loads, the COF first increases with increase in speed and then decreases considerably. This abrupt decrease in friction coefficient is due to intense heat generation between the disc and the pin and the experiment is stopped due to higher noise and vibration. Archard’s wear model is also used to validate the wear of pin at different loads and speeds. Experimentally measured pin wear shows higher value probably due to decreases in material hardness with increase in interface temperature.
This paper presents an experimental study of pressure distribution on hydrodynamic journal bearin... more This paper presents an experimental study of pressure distribution on hydrodynamic journal bearing with SAE 10W30 multi grade oil. Hydrodynamic Journal bearing test rig (HJBTR) is used to test the 40 mm diameter and 40 mm long bearing (l/d = 1) made of Bronze. Test bearing is located between two antifriction bearings and loaded mechanically. The space between the shaft and the bearing is filled with SAE 10W30. A constant load of 800 N is applied at various journal rotational speeds of 1000, 1500, 2000 rpm. Various parameters like frictional torque, oil temperature and pressure at 10 different sensors along circumferential direction were recorded from Hydrodynamic Journal Bearing Test Rig (HJBTR). These results were used for experimental calculations and theoretical verification using Raimondi and Boyd charts for practical design. The experimental plot of pressure ratio vs sommerfeld number indicates that the working conditions are in the stable hydrodynamic regime. Also experimental results were following the same trend as MCKEE‟s investigation curve.
In India, the transportation sector is growing rapidly and the number of vehicles on Indian
roads... more In India, the transportation sector is growing rapidly and the number of vehicles on Indian roads is increasing at a very fast rate leading to overcrowded roads and noise pollution. The traffic scenario is typically different from other countries due to predominance of a variety of two-wheelers which has doubled in the last decade and forms a major chunk of heterogeneous volume of vehicles. Also tendency of not following the traffic norms and poor maintenance adds to the noise generation. In the present study, Multilayer feed forward back propagation (BP) neural network has been trained by Levenberg–Marquardt (L–M) algorithm to develop an Artificial Neural Network (ANN) model for predicting highway traffic noise. The developed ANN model is used to predict 10 Percentile exceeded sound level (L10) and Equivalent continuous sound level (Leq) in dB (A). The model input parameters are total vehicle volume/hour, percentage of heavy vehicles and average vehicle speed. The predicted highway noise descriptors, Leq and L10 from ANN approach and regression analysis have also been compared with the field measurement. The results show that the percentage difference is much less using ANN approach as compared to regression analysis. Further goodness-of-fit of the models against field data has been checked by statistical t-test at 5% significance level and proved the Artificial Neural Network (ANN) approach as a powerful technique for traffic noise modeling.
The major contribution of the traffic noise, towards overall noise pollution scenario, is a well ... more The major contribution of the traffic noise, towards overall noise pollution scenario, is a well known established fact. Traffic noise from highways creates problems for surrounding areas, especially when there are high traffic volumes and high speeds.
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Papers by Paras Kumar
selection operators (uniform, roulette wheel, and tournament) are used to analyze the accuracy of GA models. The GA model performs better as compared to regression model. The average mean square error (MSE) using GA model is 0.59 as
compared to 0.76 for regression model. Among all GA selection operators, tournament selection shows better result.
running condition using pin-on-disc machine. Coefficient of friction (COF) and pin wear are experimentally measured for
different combination of loads (10N-70N) and rotational speeds (200 rpm-2000 rpm). The speed and load at which transition to
scuffing occur are also examined. It is observed that the wear resistance increases with sliding velocity for lower load, while at
higher load it increases initially and then becomes stable at higher speed. The COF increases with sliding velocity at lower loads and then becomes steady, while at higher loads, the COF first increases with increase in speed and then decreases considerably. This abrupt decrease in friction coefficient is due to intense heat generation between the disc and the pin and the experiment is stopped due to higher noise and vibration. Archard’s wear model is also used to validate the wear of pin at different loads and speeds. Experimentally measured pin wear shows higher value probably due to decreases in material hardness with increase in interface temperature.
roads is increasing at a very fast rate leading to overcrowded roads and noise pollution. The
traffic scenario is typically different from other countries due to predominance of a variety
of two-wheelers which has doubled in the last decade and forms a major chunk of heterogeneous
volume of vehicles. Also tendency of not following the traffic norms and poor
maintenance adds to the noise generation.
In the present study, Multilayer feed forward back propagation (BP) neural network has
been trained by Levenberg–Marquardt (L–M) algorithm to develop an Artificial Neural Network
(ANN) model for predicting highway traffic noise. The developed ANN model is used
to predict 10 Percentile exceeded sound level (L10) and Equivalent continuous sound level
(Leq) in dB (A). The model input parameters are total vehicle volume/hour, percentage of
heavy vehicles and average vehicle speed. The predicted highway noise descriptors, Leq
and L10 from ANN approach and regression analysis have also been compared with the field
measurement. The results show that the percentage difference is much less using ANN
approach as compared to regression analysis. Further goodness-of-fit of the models against
field data has been checked by statistical t-test at 5% significance level and proved the Artificial
Neural Network (ANN) approach as a powerful technique for traffic noise modeling.
selection operators (uniform, roulette wheel, and tournament) are used to analyze the accuracy of GA models. The GA model performs better as compared to regression model. The average mean square error (MSE) using GA model is 0.59 as
compared to 0.76 for regression model. Among all GA selection operators, tournament selection shows better result.
running condition using pin-on-disc machine. Coefficient of friction (COF) and pin wear are experimentally measured for
different combination of loads (10N-70N) and rotational speeds (200 rpm-2000 rpm). The speed and load at which transition to
scuffing occur are also examined. It is observed that the wear resistance increases with sliding velocity for lower load, while at
higher load it increases initially and then becomes stable at higher speed. The COF increases with sliding velocity at lower loads and then becomes steady, while at higher loads, the COF first increases with increase in speed and then decreases considerably. This abrupt decrease in friction coefficient is due to intense heat generation between the disc and the pin and the experiment is stopped due to higher noise and vibration. Archard’s wear model is also used to validate the wear of pin at different loads and speeds. Experimentally measured pin wear shows higher value probably due to decreases in material hardness with increase in interface temperature.
roads is increasing at a very fast rate leading to overcrowded roads and noise pollution. The
traffic scenario is typically different from other countries due to predominance of a variety
of two-wheelers which has doubled in the last decade and forms a major chunk of heterogeneous
volume of vehicles. Also tendency of not following the traffic norms and poor
maintenance adds to the noise generation.
In the present study, Multilayer feed forward back propagation (BP) neural network has
been trained by Levenberg–Marquardt (L–M) algorithm to develop an Artificial Neural Network
(ANN) model for predicting highway traffic noise. The developed ANN model is used
to predict 10 Percentile exceeded sound level (L10) and Equivalent continuous sound level
(Leq) in dB (A). The model input parameters are total vehicle volume/hour, percentage of
heavy vehicles and average vehicle speed. The predicted highway noise descriptors, Leq
and L10 from ANN approach and regression analysis have also been compared with the field
measurement. The results show that the percentage difference is much less using ANN
approach as compared to regression analysis. Further goodness-of-fit of the models against
field data has been checked by statistical t-test at 5% significance level and proved the Artificial
Neural Network (ANN) approach as a powerful technique for traffic noise modeling.