Study of High Temperature Coatings Using Various Substrate Materials Santosh Kulkarni, Somvir Singh Nain, Jeetendra Tiwari, B. Srinivasa Varma, Ravi Kumar Panthangi, Anuj Kumar Sehgal Iop Conference Series Earth and Environmental Science, 2023 Study the mechanical properties for materials to enhance the high strength and durability, wear & tear resistance and cost efficient. The coating can be carried out on various materials done by using different methods of coating based on requirements. Properties will be improved by different coating alloys to improve mechanical properties of materials. Due to their distinctive short-range ordered and long-range disordered atomic organisation; amorphous elements offer higher elastic elasticity and superior corrosion and wear resistance compared to conventional materials. The issues that arise during high temperature coating preparation methods, such as laser cladding and thermal spraying, are first briefly discussed in this overview. Metals are undercooled frozen liquids in the form of bulk metallic glasses.
Evaluation of tree regression analysis for estimation of river basin discharge Parveen Sihag, Ahmed Mohammed Sami Al-Janabi, Nashwan K. Alomari, Aminuddin Ab Ghani, Somvir Singh Nain Modeling Earth Systems and Environment, 2021 River discharge links the hydrologic and geologic cycles in addition to climate components; therefore, it forms an important source of hydraulic and hydrologic quantity. The ability to quantify river discharge accurately is very important for estimating water availability and distribution for better water resources management. In this study, the performance of ARIMA, random forest (RF), the M5P and Bagged M5P (BM5P) methods, for modeling the daily discharge of the Baitarani Riverwere compared and evaluated against measured values. Fifteen different input combinations under two groups (i.e., discharge and rainfall) were considered, and a suitable modeling approach with appropriate model input combination is proposed on the basis of various goodness fit parameters. Four statistical assessment methods implemented to determine the best performing models include the correlation coefficient (CC), Mean square error (MSE), Root mean square error (RMSE) and Scattering Index (SI).The outcomes of this study indicated that the Bagged M5P modeling approach is outperforming than ARIMA, RF and M5P. This model recorded up to 0.8676, 10.7279, 39.836 m 3 /s and 0.9599 for (CC), (MAE), (RMSE) and (SI), respectively, for testing data set.
Estimation of models for cumulative infiltration of soil using machine learning methods Anastasia Angelaki, Somvir Singh Nain, Varun Singh, Parveen Sihag Ish Journal of Hydraulic Engineering, 2021 Knowledge of cumulative infiltration of soil is necessary for irrigation, surface flow, groundwater recharge and many other hydrological processes. In the present study, the Support Vector Machine (SVM), artificial neural network (ANN) and adaptive Neuro-fuzzy inference system (ANFIS) were employed to estimate the cumulative infiltration of soil. For this study, a data set containing 106 experimental observations were analyzed. Out of 106, 70 % of data was selected for preparing different algorithms whereas rest 30% data was selected to test the models. The models accuracy was depended upon the two performance evaluation parameter which is correlation coefficient (CC) and root mean square error (RMSE). Results of performance evaluation parameters suggest that triangular membership function-based ANFIS model works well than SVM and ANN models. While SVM and ANN models also give a good estimation performance. Sensitivity analysis concludes that the parameter, time (t) is the most influencing parameter for the modeling of cumulative infiltration of soil for this data set.
Upgrading of surface properties of ef6 cast iron using thermal barrier coating Journal of Green Engineering, 2020
Machine learning application for pulsating flow through aluminum block Somvir Singh Nain, Rajeev Rathi, B. Srinivasa Varma, Ravi Kumar Panthangi, Amit Kumar Lecture Notes in Mechanical Engineering, 2020 Trials had been performed for heated square workpiece in a rectangular channel to observe heat exchange in pulsating flow. A square workpiece consisting of aluminum is utilized amid the examinations. The impact of oscillating frequency and Reynolds number of the stream on heat exchange enhancement and Nusselt number is investigated. The trials are done in the range of 0–60 Hz signal frequency. The support vector machine algorithm based on distinct kernels is used to evaluate the workpiece temperature. The support vector machine algorithm using PUK kernel presents the best results for workpiece temperature. Different diagrams are plotted to demonstrate the impact of RE number and recurrence frequency on the heat exchange enhancement. With an increment in the value of RE number, the increase in heat exchange takes place at all recurrence frequencies. Up to some point, heat exchange additionally improved with an increment in recurrence frequency and after that starts to decline.
Modelling and analysis for the machinability evaluation of Udimet-L605 in wire-cut electric discharge machining Somvir Singh Nain, Dixit Garg, Sanjeev Kumar International Journal of Process Management and Benchmarking, 2019 The intention of this research is to examine and analyse the machinability of Udimet-L605 in wire-cut electric discharge machining. The pulse-on time, pulse-off time, peak current, wire tension, spark gap voltage, wire feed have been considered as input variables and two response variables as material removal rate and surface roughness. The four models, such as support vector machine using two different kernels, nonlinear regression and multi-linear regression have been proposed to check the variation among the experimental and predicted consequences. In addition to this, the sensitivity analyses test has been performed on prominent model to examine the influence of input variables on the material removal rate and surface roughness in wire electric discharge machining and epitomised that pulse-on time is the momentous parameter for both material removal rate and surface roughness. The support vector regression based on the polynomial kernel model has conferred enhanced resulting in a contest with other models.
