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    An approach for the optimum design of heat exchangers
    (JOHN WILEY & SONS LTD, 2004) Unuvar, A; Kargici, S
    In this paper, an approach for the optimum design of heat exchangers has been presented. Traditional design method of heat exchangers involves many trials in order to meet design specifications. This can be avoided through the present design method, which takes the minimization of annual total cost as a design objective. In alternative optimum design methods, such as Lagrange multiplier method, by changing one variable at a time and using a trial-error or a graphical method, optimum results are obtained in a long time. In the present design optimization problem, the total annual cost has been taken as the objective function and heat balance, and rate equation have been taken as equal constraint. The method using the penalty function transforms the constrained problem into a single unconstrained problem. To solve the optimal problem, the method of steepest descent has been used. Initial design variables include the tube-inside coefficient of heat transfer, tube-outside coefficient of heat transfer, temperature difference and outside tube area of heat transfer. The changes in variables are considered simultaneously to reach an optimum solution. The results show that the present approach is a powerful tool for optimum design of heat exchangers and is expected to be beneficial to energy industry. Copyright (C) 2004 John Wiley Sons, Ltd.
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    Tool condition monitoring in milling based on cutting forces by a neural network
    (TAYLOR & FRANCIS LTD, 2003) Saglam, H; Unuvar, A
    Automated machining systems require reliable online monitoring processes. The application of a multilayered neural network for tool condition monitoring in face milling is introduced and evaluated against cutting force data. The work uses the back-propagation algorithm for training neural network of 5 x 10 x 2 architecture. An artificial neural network was used for feature selection in order to estimate flank wear (Vb) and surface roughness (Ra) during the milling operation. The relationship of cutting parameters with Vb and Ra was established. The sensor selection using statistical methods based on the experimental data helps in determining the average effect of each factor on the performance of the neural network model. This model, including cutting speed, feed rate, depth of cut and two cutting force components (feed force and vertical Z-axis force), presents a close estimation of Vb and Ra. Therefore, the neural network with parallel computation ability provides a possibility for setting up intelligent sensor systems.