Dr. T. Mohanraj

In this work, a milling dynamometer based on strain gauge with an octagonal and square ring was designed and tested. Strain gauges were attached with the mechanical rings to detect the deformation, during the machining process. Wheatstone bridge circuit was equipped with gauges to acquire the strain as voltage owing to the deformation of mechanical rings when machining takes place. The finite element analysis (FEA) was used to identify the location of maximum deformation and stress. The direction of rings and location of gauges were decided to increase the sensitivity and decrease the cross-sensitivity. Then, the cutting force was acquired through NI 6221 M series data acquisition (DAQ) card. The dynamometer had undergone a cycle of tests to verify its static and dynamic characteristics. The metrological characterization was performed according to the calibration procedure based on ISO 376 – 2011 standard. The cutting force was measured with both the dynamometers through milling experiments based on Taguchi’s L9 orthogonal array and the results were recorded. The measured cutting force varied from 300 N to 550 N. The obtained results depicted that low-cost milling dynamometer was reliable to measure the three component machining force. Overall, the square ring based dynamometer provides the better static and dynamic characteristics in terms of linearity, cross-sensitivity (4%), uncertainty (0.054%), and natural frequency (362.41 rev/s).

amor500 steel plates are used as a ballistic material due to their greater hardness and strength properties. This steel can be produced with a 2-30mm thickness sheet which may attain 505-590HV hard-ness. In the present work, Ramor500 steel pairs are joined using asubmerged arc welding (SAW) process and taking various parameters into consideration. An austenitic additional wire is used for the welding process which contains Cr, Ni, and Mn. The source model prototype was developed using ANSYS software and considering a time-dependent three dimensional thermal model with source cooling. The highest tensile stress voltage value was determined in the sample applying a constant current of 250A, 25V and 30cm×min-1 welding speed. It was observed that the welding seam width increases as welding tension grows and that welding height and depth increase and decrease more or less in tandem. A ANSYS thermal cooling analysis revealed that welding tension grows with heat transfer which increases 15mm from the maincenter of the welding area

Multi-objective optimization technique has become an essential step in the selection of cutting parameters. The intension of this research study is to analyze the performance characteristics of coated carbide inserts on their measured output responses during machining AISI 1015 steel. This paper targets to optimize the machining parameters such as speed, cutting depth, feed rate, cutting fluid flow rate, and coating material when multiple responses like surface roughness and flank wear were considered at the same time during turning. This research study also intends to examine scientifically the effect of machining parameters on quality measures during machining structural AISI 1015 steel. Cathodic arc evaporation–coated titanium aluminum nitride (TiAlN), titanium aluminum nitride/tungsten carbide-carbon (TiAlN/WC-C), and uncoated CNC inserts were used for the study. SEM and energy-dispersive X-ray analysis were performed to confirm the presence of coated elements. Micro-hardness was measured for coated, pure inserts, and TiAlN/WC-C-coated tool exhibited a higher hardness of 22.11 GPa compared with pure and coated tools. Five process parameters were used for this study, each at three stages. The experimental design was laid based on Taguchi’s L27 orthogonal array. In this research study, a multi-objective hybrid optimization technique comprising grey relation and fuzzy logic conjugated with the Taguchi design of experiments was used. The process parameters were optimized by grey relation analysis followed by fuzzification using Mamdani fuzzy engine and then optimized through Taguchi analysis. The parameter combination of speed 500 rpm, depth of cut of 1 mm, a feed rate of 0.05 mm/rev, cutting fluid flow rate at high level, and TiAlN/WC-C coating was found to be the optimum input parameters. The confirmatory test was also performed to validate the hybrid optimization approach.

The most important improvement in metal the cutting industry is the continuous utilization of cutting tools and tool condition monitoring system. In the metal cutting process, the tool condition has to be administered either by operators or by online condition monitoring systems to prevent damage to both machine tools and workpiece. Online tool condition monitoring system is highly essential in modern manufacturing industries for the rising requirements of cost reduction and quality improvement. This paper summaries various monitoring methods for tool condition monitoring in the milling process that have been practiced and described in the literature.

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

This paper describes an on-line tool wear monitoring system for milling operation by optimizing the input parameters while machining 7075T6 aluminium composite material. The input parameters considered are Spindle speed, feed rate and depth of cut. Coolant is the major factor that affects the tool wear to greater extent. So the type of coolant (different types of vegetable oils) is also taken as an input parameter for optimization. The experiments are carried out with different spindle speed, feed rate, depth of cut and coolant and the vibration produced in X, Y & Z directions were measured. Taguchi mixed level design (L18) is taken for optimization process using S/N ratio and ANOVA (Analysis Of Variance) analysis. The results show that the coolant has the most significance while measuring the vibration. © BEIESP.

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 condi-tion. This present research work is focused on analyzing tool condition by adopting a vibration signature during the ma-chining 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.

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.