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Khanghah S.P.,University of Tabriz | Boozarpoor M.,Islamic University | Lotfi M.,Technical and Vocational University of Neyshabour | Teimouri R.,Babol Noshirvani University of Technology
Transactions of the Indian Institute of Metals | Year: 2015

In micro-machining process, there are a lot of factors that affect formation of burr along micro-component’s edges. Therefore, finding suitable machining parameters significantly reduces the burr size in microproducts. In the current work, experimental investigations have been performed to study effects of micro-milling parameters (i.e. cutting speed, feed rate and depth of cut) on burr height and burr thickness of the micro-grooves of 316 stainless steel in up milling and down milling operations. The aforementioned factors were varied over their working ranges while other parameters such as tool material, tool condition, lubrication etc. were kept constant. Here, experiments were designed and conducted based on three factors-three levels face centered central composite design. The response surface methodology (RSM) and analysis of variances were applied on generated data to correlate empirical relationships between micro-milling parameters and burr characteristics. Further, the developed RSM models was then associated with principal component analysis and simulated annealing to minimize burr characteristics in both up-milling and down-milling processes, simultaneously. According to optimization results, spindle speed of 15,000 RPM, feed rate 5 mm/tooth and depth of cut of 0.15 is the most optimal factor combination that causes minimum burr sizes. © The Indian Institute of Metals - IIM 2015. Source

Enjilela V.,Islamic Azad University at Karaj | Salimi D.,University of Tabriz | Tavasoli A.,Payame Noor University | Lotfi M.,Technical and Vocational University of Neyshabour
International Journal of Modern Physics C | Year: 2016

In the present work, the meshless local Petrov–Galerkin vorticity-stream function (MLPG-VF) method is extended to solve two-dimensional laminar fluid flow and heat transfer equations for high Reynolds and Rayleigh numbers. The characteristic-based split (CBS) scheme which uses unity test function is employed for discretization, and the moving least square (MLS) method is used for interpolation of the field variables. Four test cases are considered to evaluate the present algorithm, namely lid-driven cavity flow with Reynolds numbers up to and including (Formula presented.), flow over a backward-facing step at Reynolds number of (Formula presented.), natural convection in a square cavity for Rayleigh numbers up to and including (Formula presented.), and natural convection in a concentric square outer cylinder and circular inner cylinder annulus for Rayleigh numbers up to and including (Formula presented.). In each case, the result obtained using the proposed algorithm is either compared with the results from the literatures or with those obtained using conventional numerical techniques. The present algorithm shows stable results at lower or equal computational cost compared to the other upwinding schemes usually employed in the MLPG method. Close agreements between the compared results as well as higher accuracy of the proposed method show the ability of this stabilized algorithm. © 2016 World Scientific Publishing Company Source

Jafari M.,Islamic Azad University at Sari | Lotfi M.,Technical and Vocational University of Neyshabour | Ghaseminejad P.,Islamic Azad University at Dezful | Roodi M.,Technical and Vocational University of Neyshabour | Teimouria R.,Babol Noshirvani University of Technology
Transactions of the Indian Institute of Metals | Year: 2015

Springback is an undesirable phenomenon that extensively occurs during sheet metal forming processes. There are many parameters which have great influence on springback. Hence, selection of appropriate controllable parameters may lead to spingback reduction. In the present work an attempt has been made to find optimal combination of L-bending parameters (i.e. die temperature, step distance, lower punch radius, die clearance and step height) regarding minimum springback. Here, combination of finite element model (which was validated through trial experiments) and Taguchi experimental design were used to form design matrix. Then adaptive neuro-fuzzy inference system (ANFIS) was then applied to correlate intelligent relationships between process inputs and springback. The accuracy of developed ANFIS model was compared with FE model and experimental testing data. Finally, the teaching learning based optimization algorithm was combined with the developed ANFIS model to minimize the springback. The obtained optimal results were then compared with those derived from FE model and experiments and showed that the proposed approach can predict the optimal drawing process accurately. Furthermore, the optimum results were discussed carefully according to mechanical behavior of L-bending process and through implicit finite element model. © The Indian Institute of Metals - IIM 2015. Source

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