Government College of Technology

About: Government College of Technology is a based out in . It is known for research contribution in the topics: Ultimate tensile strength & Inverter. The organization has 171 authors who have published 170 publications receiving 1515 citations.

Prediction of tool wear using regression and ANN models in end-milling operation

Abstract: Tool wear prediction plays an important role in industry for higher productivity and product quality. Flank wear of cutting tools is often selected as the tool life criterion as it determines the diametric accuracy of machining, its stability and reliability. This paper focuses on two different models, namely, regression mathematical and artificial neural network (ANN) models for predicting tool wear. In the present work, flank wear is taken as the response (output) variable measured during milling, while cutting speed, feed and depth of cut are taken as input parameters. The Design of Experiments (DOE) technique is developed for three factors at five levels to conduct experiments. Experiments have been conducted for measuring tool wear based on the DOE technique in a universal milling machine on AISI 1020 steel using a carbide cutter. The experimental values are used in Six Sigma software for finding the coefficients to develop the regression model. The experimentally measured values are also used to train the feed forward back propagation artificial neural network (ANN) for prediction of tool wear. Predicted values of response by both models, i.e. regression and ANN are compared with the experimental values. The predictive neural network model was found to be capable of better predictions of tool flank wear within the trained range.

Optimization of machining parameters using genetic algorithm and experimental validation for end-milling operations.

Abstract: Optimization of cutting parameters is valuable in terms of providing high precision and efficient machining. Optimization of machining parameters for milling is an important step to minimize the machining time and cutting force, increase productivity and tool life and obtain better surface finish. In this work a mathematical model has been developed based on both the material behavior and the machine dynamics to determine cutting force for milling operations. The system used for optimization is based on powerful artificial intelligence called genetic algorithms (GA). The machining time is considered as the objective function and constraints are tool life, limits of feed rate, depth of cut, cutting speed, surface roughness, cutting force and amplitude of vibrations while maintaining a constant material removal rate. The result of the work shows how a complex optimization problem is handled by a genetic algorithm and converges very quickly. Experimental end milling tests have been performed on mild steel to measure surface roughness, cutting force using milling tool dynamometer and vibration using a FFT (fast Fourier transform) analyzer for the optimized cutting parameters in a Universal milling machine using an HSS cutter. From the estimated surface roughness value of 0.71 μm, the optimal cutting parameters that have given a maximum material removal rate of 6.0×103 mm3/min with less amplitude of vibration at the work piece support 1.66 μm maximum displacement. The good agreement between the GA cutting forces and measured cutting forces clearly demonstrates the accuracy and effectiveness of the model presented and program developed. The obtained results indicate that the optimized parameters are capable of machining the work piece more efficiently with better surface finish.

Development of mathematical models for prediction of weld bead geometry in cladding by flux cored arc welding

Abstract: The mechanical and corrosion resistant properties of cladded components depend on the clad bead geometries, which in turn are controlled by the process parameters. Therefore it is essential to study the effect of process parameters on the bead geometry to enable effective control of these parameters. The above objective can easily be achieved by developing equations to predict the weld bead dimensions in terms of process parameters. Experiments were conducted to develop models, using a three factor, five level factorial design for 317L flux cored stainless steel wire with IS:2062 structural steel as base plate. The models so developed were checked for their adequacy. Confirmation experiments were also conducted and the results show that the models developed can predict the bead geometries and dilution with reasonable accuracy. It was observed from the investigation that the interactive effect of the process parameters on the bead geometry is significant and cannot be neglected.

Optimization of squeeze casting process parameters using Taguchi analysis

Abstract: Squeeze casting is a hybrid metal forming process combining features of both casting and forging in one operation. This paper reports a research in which an attempt was made to prepare solid cylindrical components of LM24 aluminum alloy through squeeze casting. The primary objective was to investigate the effect of process parameters on the mechanical properties exhibited by the castings produced though squeeze casting process. A set of trials were conducted based on parameters settings suggested in Taguchi’s offline quality control concept. The experimental results indicate that the squeeze pressure and the die-preheating temperature were the parameters making significant contribution toward improvement in mechanical properties of squeeze cast LM24 aluminum alloy.

Optimization of squeeze casting parameters for non symmetrical AC2A aluminium alloy castings through Taguchi method

Abstract: This paper reports a research in which an attempt was made to prepare AC2A aluminium alloy castings of a non symmetrical component through squeeze casting process The primary objective was to investigate the influence of process parameters on mechanical properties of the castings Experiments were conducted based on orthogonal array suggested in Taguchi’s offline quality control concept The experimental results showed that squeeze pressure, die preheating temperature and compression holding time were the parameters making significant improvement in mechanical properties The optimal squeeze casting condition was found and mathematical models were also developed for the process