Dr. T. Mohanraj

Rapid industrialization and growth in population in urban regions augment the pollution levels from transportation sectors, especially from diesel fleets. A wide array of research activities were carried out to satisfy the energy needs as well as reduce the emission levels, which poses a big challenge to the research community. In this situation, biomass-derived fuels provide a ray of hope to the research community to address the emission problem by adapting closed carbon cycle at low cost. This chapter gives an overview to the readers about the present energy scenario, biomass-based fuel, upgradation techniques for biomass fuel, and engine adaptability of biomass-based fuels. This chapter provides a clear glimpse of biomass energy, one of the potential energy resources in the near future.

In this work, the flank wear of the cutting tool is predicted using artificial neural network based on the responses of cutting force and surface roughness. EN8 steel is chosen as a work piece material and turning test is conducted with various levels of speed, feed and depth of cut. Cutting force and surface roughness are measured for both the fresh and dull tool under dry cutting conditions. The tool insert used is CNMG 120408 grade, TiN coated cemented carbide tool. The experiments are conducted based on the response surface methodology face central composite design of experiments. The feed rate (14.52%), depth of cut (27.72%) and the interaction of feed rate and depth of cut (50.39%) influence the cutting force. The feed rate (21.33%) and the interaction of cutting speed and depth of cut (26.67%) influence the flank wear. The feed rate (61.63%) has the significant influence on surface roughness. The feed forward back propagation neural network of 5-n-1 architecture is trained using the algorithms like Levenberg Marquardt, BFGS quasi-Newton, and Gradient Descent with Momentum and Gradient descent with adaptive learning rate. The network performance has been assessed based on their mean square error and computation time. From this analysis, the BFGS quasi-Newton back propagation algorithm produced the least mean squared error value with minimum computation time

Surface roughness and taper angle of an abrasive waterjet machined surface of 7075 Aluminum metal matrix composite were deliberately studied. Response surface methodology design of experiments and analysis of variance were used to design the experiments and to identify the effect of process parameters on surface roughness and taper angle. The jet traverse speed and jet pressure were the most significant process parameters which influence the surface roughness and taper angle, respectively. Increasing the pressure and jet traverse speed results in increasing the surface roughness and taper angle. At the same time, decreasing the standoff distance and jet traverse speed possibly enhances both the responses. The optimal process parameters of 1mm as standoff distance, 192MPa as water pressure and 30mmmin−1 as jet traverse speed were identified to obtain the minimum value of surface roughness and taper angle. Based on the optimal parameters, the confirmation test was conducted. The mathematical equation was obtained from the experimental data using regression analysis; it was observed that the error was less than 5% of the experimentally measured values.

The major difficulty faced in a machining process is predicting the failure of cutting tools and analyzing the stipulated time for tool replacement. The former and latter can be achieved through a monitoring system that surveys the effective condition. This present research work is focused on analyzing tool condition by adopting a vibration signature during the machining of a hybrid aluminum alloy composite using various coolants. The experiments were conducted employing various tools under optimum process parameters utilizing vegetable based cutting oil as a coolant. During the machining process, a vibration signature from the workpiece was acquired using an NI 6221 M series DAQ card allowing for various time domain features to be extracted. The arithmetic mean and skewness significantly increased for dull tools. Based on the extracted features, a decision making algorithm for tool condition monitoring system has been proposed. The result shows that the features extracted increased consecutively with an increase in flank wear.

An efficient method based on Taguchi's design of experiment coupled with the grey relational analysis was studied, concentrating on the optimization of process parameters over surface roughness, cutting force and tool wear rate in milling of mild steel. This study consists of three stages: experimental work, single response optimization using Taguchi's S/N value and multi-response optimization using grey relational analysis. In the first stage, the experimental work was carried out using Taguchi's design of experiments. The effects of process parameters (spindle speed, feed rate and depth of cut) on surface roughness, cutting force and tool wear rate were investigated using analysis of variance. In the second stage, Taguchi's signal-to-noise ratio was used to optimize the responses. Finally, multi-response optimization was carried out using grey relational analysis. Additionally, the analysis of variance (ANOVA) was applied to determine the most significant factor for the optimal response for milling of mild steel. From the ANOVA table, the most significant factor is the spindle speed. This proposed method can be an effective approach to enhance the multi-response optimization for milling process.

This paper presents a comparison of experimental results and a fuzzy rule based system model for calculating the cutting force in the turning operation. A full bridge dynamometer was used to measure the cutting forces over the mild steel work piece and Cemented Carbide Insert tool for different combinations of cutting velocity, feed rate and depth of cut. The rake angle, approach angle and nose radius of the cutting tool insert is kept constant throughout the experiment. This fuzzy model consists of 27 rules and Mamdani Max-min inference mechanism was used. The Taguchi designs of experiments were used to determine the number of experiments. Also, an attempt had been made to analyze the influence of the parameters using the regression analysis which yields a maximum error of 3.214% at the time of prediction which was smaller. The experiments are planned based on Taguchi's design and the measured cutting forces were compared with the predicted forces in order to validate the feasibility of the proposed design. The percentage contribution of each process parameter had been analyzed using Analysis of Variance (ANOVA). Experimental results were compared with the regression analysis and predicted fuzzy model. The difference between experimental and predicted results was obtained as around 98.84%.

In the current scenario most of the countries do not have sufficient skilled manpower specifically in agricultural sector and it affects the growth of developing countries. So it’s a time to automate the sector to overcome this problem. An innovative idea of our project is to automate the process of sowing crops such as sunflower, baby corn, groundnut and vegetables like beans, lady’s finger, pumpkin and pulses like black gram, green gram etc to reduce the human effort and increase the yield. The plantations of seeds are automatically done by using DC motor. The distance between the two seeds are controlled and varied by using Microcontroller. It is also possible to cultivate different kinds of seeds with different distance. When the Robo reaches the end of the field we can change the direction with the help of remote switches. The whole process is controlled by Microcontroller.