Prediction of optimum process parameters in turning of Ti6al4v alloy under various cooling strategies using soft computing tool M. Venkata Ramana, G. Krishna Mohan Rao, D. Hanumantha Rao, BVR Ravi Kumar Aip Conference Proceedings, 2021 Titanium alloys are treated as tough to machine materials. To remove the material from Titanium alloys inexpensively, the optimization of controllable machining parameters is very important. The experimental design methodology is used for the planning and conduct of experiments. Machining experiments are conducted with various cooling strategies like dry, Minimum Quantity Lubrication (MQL) and flooded machining with dissimilar tool materials such as uncoated, Physical Vapour Deposition (PVD) and Chemical Vapour Deposition (CVD) coated tools. In this study, experimental design with L27 orthogonal array of Taguchi’s is used to perform the experimental tests. The results obtained from these experiments are used to build up the second-order multiple regression form with the input process parameters. The same model is applied as a fitness utility for Simulated Annealing (SA) to optimize the turning controllable process parameters for minimization of tool wear. SA results are contrasted with Taguchi’s technique and validated with Regression Analysis. The optimum parameters obtained both in Simulated Annealing and Taguchi’s methodology are the depth of cut at a moderate level, feed rate at a high level, MQL machining and uncoated tool.
Optimization of controllable process parameters to decrease cutting forces in turning of Ti6Al4V alloy with various machining environments Journal of Manufacturing Technology Research, 2019
Experimental study on the effect of mechanical stirring in semisolid processing of aluminum alloys at thixo-temperatures M.V. Kishore, D. Hanumantha Rao, M. Manzoor Hussain Materials Science Forum, 2015 Semisolid metal processing (SSMP) is a relatively new technology for production of near net shaped components. This process is carried out at a temperature range between its liquidus and solidus temperature. At thixo-temperatures, the metal exists as a mixture of solid and liquid phases in the slurry form. Thixotropy is a time-dependent shear thinning property where the metal is thick at static conditions but will flow when subjected to shear stress. The present research work focuses to identify the process parameters in SSMP. A mechanical stirring process was employed to obtain the desired microstructure required for SSMP. Process parameters such as stirring time and stirring speed were considered. The effect of addition of grain refiner to the molten alloy on the microstructure was also studied.
Influence of silicon content on the volume deficit characteristic of cast Al-Si alloys Samavedam Santhi, S.B. Sakri, Dharwada Hanumantha Rao, Srinivasan Sundarrajan Journal of Materials Research, 2013 Aluminum alloy castings find extensive applications in automobile and other engineering industries. Production of defect-free castings requires a good understanding of the volume deficit characteristic. The volume deficit of a casting depends on the casting material and casting conditions. Patterson and Engler have classified the volume deficit into four types namely, macrocavities, internal porosity, surface sinking, and volumetric contraction. The influence of process parameters on the characteristics determines the casting quality. The process parameters considered in this study are bottom chill, casting shape, and pouring temperature. Two basic shapes rectangle and cylinder are considered. The volume deficit decreases with an increase in the silicon content. The AA 356.0 alloy shows more amount of volume deficit than AA 413.0 alloy. X-ray computer tomography (XCT) helps to reveal the size, shape, and location of defects in castings. Quantification of internal closed porosity of AA 413.0 casting is done using XCT and successfully validated through destructive testing of castings.
Optimized high speed turning on Inconel 718 using Taguchi method based Grey relational analysis Indian Journal of Engineering and Materials Sciences, 2013
Prediction of resin bonded sand core properties using fuzzy logic B. Surekha, D. Hanumantha Rao, G.K. Mohan Rao, Pandu R. Vundavilli, M.B. Parappagoudar Journal of Intelligent and Fuzzy Systems, 2013 This paper introduces an intelligent system for the prediction of mechanical properties of silica-based resin bonded sand core system. The properties of sand cores, such as tensile strength, compression strength, shear strength and permeability depends upon various process parameters, namely percentage of resin, of hardener, number of strokes and curing time. In the present paper, Mamdani-based fuzzy logic FL approach is used to perform forward modeling, in which the outputs are expressed as the functions of input variables. Moreover, the performance of FL system depends on the knowledge base KB, which consists of rule base and data base. Three different approaches have been developed in the present work. Manually constructed FL system is developed in the first approach, whereas in approach 2, genetic algorithm GA is used to optimize the data base and rule base of FL system developed in Approach 1. On the other hand in Approach 3, automatic evolution of rule is considered along with the use of GA to optimize data base and rule base. It is important to note that the developed fuzzy model uses triangular membership functions for fuzzification and centroid area method for de-fuzzification process. The developed FL system eliminates the need of extensive experimental work in selecting the most influential process parameters. The performances of all three approaches have been tested with the help of twenty test cases. It is to be noted that all three approaches, developed can be effectively used in foundry for making prediction. The results showed that the Approach 3 has outperformed the remaining two, in terms of prediction accuracy.
Application of response surface methodology for modeling the properties of chromite-based resin bonded sand cores International Journal of Mechanics, 2013
Hybrid modeling and optimization of hardness of surface produced by electric discharge machining using artificial neural networks and genetic algorithm Journal of Engineering and Applied Sciences, 2010
