• Transactions of Materials Processing
• /
• v.7 no.2
• /
• pp.127-138
• /
• 1998

Process design of multi-step wire drawing process, conducted by means of finite element analysis and ANN(Artificial Neural Network) has been considered. The investigated problem involves the ade-quate selection of the drawing die angle and the correspondent reduction rate in the condition of desired initial and final diameter. Combinations of the process parameters which are used in finite ele-ment simulation are selected by using the orthogonal array. Also the orthogonal array. Also the orthogonal array and the results of finite element simulation which are related to the process energy are used as train data of ANN. In this study it is shown that the application of new technique using ANN and Othogonal array table to the process design of metal forming process is useful method.

• Journal of the Korean Society for Precision Engineering
• /
• v.20 no.11
• /
• pp.71-78
• /
• 2003

The exit burrs in the micro-drilling of precision miniature holes are of interest, especially for ductile materials. As burrs from this process can be difficult to remove, it is important to acquire the way of predicting burr types as well as optimal cutting conditions which minimize the burrs. In this paper, an artificial neural network was used for the prediction of burr formation in micro-drilling. First, the influence of cutting conditions including cutting speed, feed and drill diameter on the exit burr characteristics, such as burr size and type, were observed and analyzed. Then. the burr types were classified by using the influential experimental data as input parameters to the neural nets.

• Geomechanics and Engineering
• /
• v.3 no.4
• /
• pp.285-290
• /
• 2011

This article employs Multivariate Adaptive Regression Spline (MARS) for determination of friction capacity of driven piles in clay. MARS is non-parametric adaptive regression procedure. Pile length, pile diameter, effective vertical stress, and undrained shear strength are considered as input of MARS and the output of MARS is friction capacity. The developed MARS gives an equation for determination of $f_s$ of driven piles in clay. The results of the developed MARS have been compared with the Artificial Neural Network. This study shows that the developed MARS is a robust model for prediction of $f_s$ of driven piles in clay.

• Water for future
• /
• v.29 no.6
• /
• pp.217-224
• /
• 1996

In this paper, the evaluation of diameter, global velocity, global roughness coefficients of the water distribution pipes are examined by using pressure and flowrate measurements in selected points of the network. The selected pipe network parameters are determined through reformulation of the continuity and energy equation. Additional energy equation is considered to analyze the coefficient matrix. The resulting nonlinear equations are solved by using Newton Raphson method. Three computer models with complex pipe system are used to demonstrate these procedures. The computed results of hydraulic parameters show good agreements with KYPIPE2 flow analysis model.

• Korea-Australia Rheology Journal
• /
• v.11 no.4
• /
• pp.293-304
• /
• 1999

Refinements of classical theories for entangled or crosslinked polymeric systems have led to incommensurable models for rubber networks and polymer melts, contrary to experimental evidence, which suggests a great deal of similarity. Uniaxial elongation and compression data of linear and branched polymer melts as well as of crosslinked rubbers were analyzed with respect to their nonlinear strain measure. This was found to be the result of two contributions: (1) affine orientation of network strands, and (2) isotropic strand extension. Network strand extension is caused by an increasing restriction of lateral movement of polymer chains due to deformation, and is modelled by a molecular stress function which in the tube concept of Doi and Edwards is the inverse of the relative tube diameter. Up to moderate strains, $f^2$ is found to be linear in the average stretch for melts as well as for rubbers, which corresponds to a constant tube volume. At large strains, rubbers show maximum extensibility, while melts show maximum molecular tension. This maximum value of the molecular stress function governs the ultimate magnitude of the strain-hardening effect of linear and long-chain branched polymer melts in extensional flows.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.7 no.4
• /
• pp.42-49
• /
• 1998

This wear debris can be harvested from the lubricants of operating machinery and its morphology is directly related to the damage to the interacting surface from which the particles originated. The morphological identification of wear debris can therefore provide very early detection of a fault and can also often facilitate a diagnosis. The purpose of this study is to attempt the developement of intelligent system for moving condition diagnosis of the machine driving system. The four shape parameter(50% volumetric diameter, aspect, roundness and reflectivity) of war debris are used as inputs to the neural network and learned the moving condition of five values(material3, applied load 1, sliding distance 1). It is shown that identification results depend on the ranges of these shape parameter learned. The three kinds of the wear debris had a different pattern characteristics and recognized the moving condition and materials very well by neural network.

• Proceedings of the Korean Society for Technology of Plasticity Conference
• /
• 1997.03a
• /
• pp.144-147
• /
• 1997

Process design of multi-step wire drawing process, conducted by means of finite element analysis and ANN(Artificial Neural Network), has been considered. The investigated problem involves the adequate selection of the drawing die angle and the correspondent reduction rate sequence in the condition of desired initial and final diameter. Combinations of the process parameters which are used in finite element simulation are selected by using orthogonal array. Also the orthogonal array and the results of finite element simulation which are related to the process energy are used as train data of ANN. In this study, it is shown that the new technique using ANN is useful method in application to the wide range of metal forming process.

• The KSFM Journal of Fluid Machinery
• /
• v.12 no.4
• /
• pp.44-53
• /
• 2009

Numerical design optimization of a fan-shaped hole for film-cooling has been carried out to improve film-cooling effectiveness by combining a three-dimensional Reynolds-averaged Navier-Stokes analysis with the radial basis neural network method, a well known surrogate modeling technique for optimization. The injection angle of hole, lateral expansion angle of hole and ratio of length-to-diameter of the hole are chosen as design variables and spatially averaged film-cooling effectiveness is considered as an objective function which is to be maximized. Twenty training points are obtained by Latin Hypercube sampling for three design variables. Sequential quadratic programming is used to search for the optimal point from the constructed surrogate. The film-cooling effectiveness has been successfully improved by the optimization with increased value of all design variables as compared to the reference geometry.

• Journal of Magnetics
• /
• v.16 no.1
• /
• pp.58-63
• /
• 2011

This paper presents design procedures of a transverse flux linear motor (TFLM). The minimum and maximum flux linkage was determined by the simplified equivalent magnetic circuit and estimated average magnetic flux density at the air gap region by considering the shape of applied magnetomotive force (MMF). With this information, the number of turns of each phase winding was calculated based on the amplitude of applied voltage and speed of a mover. The rated current, coil diameter, and winding area were obtained with the aid of an empirical formula for determining the required MMF. The usefulness of the proposed design method for TFLM is verified by the three-dimensional equivalent magnetic circuit network (EMCN) method and the experimental results of prototyped machine.

• Steel and Composite Structures
• /
• v.33 no.2
• /
• pp.181-194
• /
• 2019

This paper presents an efficient approach to generate a new empirical formula to predict the axial compression capacity (ACC) of circular concrete-filled tube (CCFT) columns using the artificial neural network (ANN). A total of 258 test results extracted from the literature were used to develop the ANN models. The ANN model having the highest correlation coefficient (R) and the lowest mean square error (MSE) was determined as the best model. Stability analysis, sensitivity analysis, and a parametric study were carried out to estimate the stability of the ANN model and to investigate the main contributing factors on the ACC of CCFT columns. Stability analysis revealed that the ANN model was more stable than several existing formulae. Whereas, the sensitivity analysis and parametric study showed that the outer diameter of the steel tube was the most sensitive parameter. Additionally, using the validated ANN model, a new empirical formula was derived for predicting the ACC of CCFT columns. Obviously, a higher accuracy of the proposed empirical formula was achieved compared to the existing formulae.