Ganesh M. Kakandikar
Bio: Ganesh M. Kakandikar is an academic researcher from College of Engineering, Pune. The author has contributed to research in topic(s): Thinning & Simulation software. The author has an hindex of 2, co-authored 11 publication(s) receiving 5 citation(s).
11 Oct 2017-
TL;DR: In this paper a new approach is presented to optimise the geometric parameters of spring seat – circular geometry component, process parameters such as blank holder pressure and coefficient of friction, etc.
Abstract: Forming/drawing is a compression-tension process involving a wide spectrum of phenomenon and flow conditions. The process result depends on the large number of parameters and their interactions. The selection of various parameters is still based on trial and error methods in most industries. In this paper a new approach is presented to optimise the geometric parameters of spring seat – circular geometry component, process parameters such as blank holder pressure and coefficient of friction, etc. The optimisation problem has been formulated with the objective of optimising the draw load. In present work genetic algorithm is used as a tool for the optimisation, to optimise the draw load and process parameters. Optimisation results are validated through finite element analysis simulation software fast form advanced.
28 May 2012-
TL;DR: In this paper the authors presents a new approach to optimize the geometry parameters of circular components, process parameters such as blank holder pressure and coefficient of friction etc.
Abstract: Forming is a compression-tension process involving wide spectrum of operations and flow conditions. The result of the process depends on the large number of parameters and their interdependence. The selection of various parameters is still based on trial and error methods. In this paper the authors presents a new approach to optimize the geometry parameters of circular components, process parameters such as blank holder pressure and coefficient of friction etc. The optimization problem has been formulated with the objective of optimizing the maximum forming load required in Forming. Genetic algorithm is used for the optimization purpose to minimize the drawing load and to optimize the process parameters. A finite element analysis simulation software Fast Form Advanced is used for the validations of the results after optimization.
31 Oct 2017-
31 Oct 2017-
01 Jan 2015-Materials Today: Proceedings
TL;DR: Genetic algorithm is a multi-path algorithm that searches many peaks in parallel, hence reducing the possibility of local minimum trapping and solve the multi-objective optimization problems.
Abstract: Genetic Algorithm is optimization method based on the mechanics of natural genetics and natural selection. Genetic Algorithm mimics the principle of natural genetics and natural selection to constitute search and optimization procedures.GA is used for scheduling to find the near to optimum solution in short time. In a genetic algorithm representation is done with variable length of sub-chromosome.GA is developed to generate the optimal order scheduling solution. GA is used as tool in different processes to optimize the process parameters. This paper reviews the genetic algorithms that are designed for solving multiple problems in applications of material science and manufacturing in field of mechanical engineering. Genetic algorithm is a multi-path algorithm that searches many peaks in parallel, hence reducing the possibility of local minimum trapping and solve the multi-objective optimization problems.
01 Feb 2018-
TL;DR: Numerical simulation proves to be a good option for studying the process and developing a control strategy for reducing the springback in deep drawing of an automotive Shock Absorber Cup with finite element method.
Abstract: Drawing or forming is a process normally used to achieve a required component form from a metal blank by applying a punch which radially draws the blank into the die by a mechanical or hydraulic action or combining both. When the component is drawn for more depth than the diameter, it is usually seen as deep drawing, which involves complicated states of material deformation. Due to the radial drawing of the material as it enters the die, radial drawing stress occurs in the flange with existence of the tangential compressive stress. This compression generates wrinkles in the flange. Wrinkling is unwanted phenomenon and can be controlled by application of a blank-holding force. Tensile stresses cause thinning in the wall region of the cup. Three main types of the errors occur in such a process are wrinkling, fracturing and springback. This paper reports a work focused on the springback and control. Due to complexity of the process, tool try-outs and experimentation may be costly, bulky and time consuming. Numerical simulation proves to be a good option for studying the process and developing a control strategy for reducing the springback. Finite-element based simulations have been used popularly for such purposes. In this study, the springback in deep drawing of an automotive Shock Absorber Cup is simulated with finite element method. Taguchi design of experiments and analysis of variance are used to analyze the influencing process parameters on the springback. Mathematical relations are developed to relate the process parameters and the resulting springback. The optimization problem is formulated for the springback, referring to the displacement magnitude in the selected sections. Genetic Algorithm is then applied for process optimization with an objective to minimize the springback. The results indicate that a better prediction of the springback and process optimization could be achieved with a combined use of these methods and tools.
01 Jan 2020-
Abstract: Swarm intelligence is a branch which deals in research that models the population of interacting agents or swarms that are self-organizing in nature. Grasshopper optimization algorithm is a modern algorithm for optimization which is inspired from the swarm-based nature. This algorithm simulates the behaviour of the grasshopper in nature and models that mathematically for solving optimization problems. Grasshopper optimization algorithm is used for the optimization of mechanical components and systems. Snubber spring is a kind of helical spring which is a part of suspension system in railway bogie. In this work, the design of snubber spring is optimized by using grasshopper optimization algorithm. The suspension system of railway bogie consists of inner spring, outer spring, and snubber spring. Optimization is done for the weight minimization of snubber spring. Wire diameter, number of active turns and mean coil diameter are the design parameters for the optimization. These parameters are optimized by using grasshopper optimization algorithm according to bounds, loading, and boundary conditions. The optimized parameters are validated experimentally and also by using a software. The spring is modelled in CATIA V5 and analyzed in ANSYS 17.0. The comparison of results is done and is validated with results experimentally in which the spring is tested on universal testing machine for compression test.
01 Sep 2021-Materials today communications
Abstract: The paper presents a hybrid strategy to determine constitutive parameters for thin-walled tubes based on experimental responses from hydraulic bulge tests. This developed procedure integrates the analytical model, finite element analysis and gradient-based optimization algorithm, where initial guesses of material parameters are generated quickly by a theoretical method, then they are input to an inverse framework integrating Gauss-Newton algorithm and finite element method. The solving for this inverse problem leads to a more accurate identification of material parameters by reducing the discrepancies between simulated results and experimental data. To evaluate its feasibility and performance, hydraulic bulge tests with different end-conditions for annealed 6060 and 5049 aluminium tubes are carried out. The strength coefficient and hardening exponent are determined using the hybrid strategy based on the collected measurements in the experiment. These material parameters are used to compare with those obtained by a single analytical model and inverse model. The comparison validates that the proposed hybrid strategy is not sensitive to starting points and can improve the calculation efficiency and determine more accurate constitutive parameters.
18 Oct 2021-
Abstract: This article describes the optimization of processing parameters for the surface roughness of AISI316 austenitic stainless steel. While experimenting, parameters in the process like feed rate (fd), speed (vc), and depth of cut (DoC) were used to study the outcome on the surface roughness (Ra) of the workpiece. The experiment was carried out using the design of experiments (DOE) on a computer numerical control (CNC) lathe. The surface roughness is tested for three conditions i.e. Dry, Wet, and cryogenic conditions after the turning process. Samples are step turned on CNC Lathe for all three conditions with a set of experiments designed. The response surface methodology is implemented, and mathematical models are built for all three conditions. The nature-inspired algorithm is the best way to get the optimal value. For the discussed problem in the paper, nature-inspired techniques are used for obtaining the optimum parameter values to get minimum surface roughness for all set conditions. The Grasshopper optimization algorithm (GOA) is the technique that is the most effective method for real-life applications. In this research, GOA is used to get optimum values for the surface roughness (Ra) at Dry, Wet and cryogenic conditions. Finally, results are compared, and it's observed that the values obtained from GOA are minimum in surface roughness value.
Author's H-index: 2