Investigating the influence of tool shape during FSW of aluminum alloy via CFD analysis

This paper describes the application of the CFD code, Comsol Multiphysics, to modeling the 3-D metal flow in friction stir welding of AA 2024-T3 aluminum alloy in order to investigate the influence of tool shape over the metal flow. Heat transfer and non-Newtonian flow equations were solved simultaneously. The results from the benchmark experiments found in the literature were used for validation purposes. Scrolled shoulders and threaded pins were given as kinematic boundary conditions. This made the computational problem an easy one. A welding engineer can predict the metal flow around the tool with different scrolls and threads under any welding conditions without making expensive experiments. Investigation of the velocity field before actual welding can save a lot of engineering hours.     

Dynamic material properties of wood subjected to low-velocity impact

This article studied the effects of low-velocity impact on the failure stresses and stiffness using a pendulum test. The specimens were of variable depth (20, 30, and 40 mm), a width of 50 mm, length of 650 mm, and span-length of 480 mm. The smallest specimen depth was similar to specimen sizes tested in the literature used to create the duration of load curve, while the largest specimen depth are considered structural size specimens. The impact was predicted using a numerical approach with Euler–Bernoulli beam, as well as Timoshenko beam theory, with a plastic contact law. The models were validated for impact from a low release-angle (where the beam remained elastic), but could use improvement for the force prediction at a high incidence velocity. The measured force signals were used as forcing functions to obtain the dynamic failure stresses for all of the evaluated specimens, and the Timoshenko–Goens–Hearmon Method to derive the dynamic E. The resulting strain rates ranged from 9.11?×?10^?5 s^?1 for the quasi-static specimens up to 25 s^?1 for the greatest incidence velocity. The results from this study suggest different duration of load factors than the Madison Curve, influencing the design of structures subjected to dynamic loading.     

The axisymmetric crack problem in a non-homogeneous interfacial region between homogeneous half-spaces

In this paper, the axisymmetric crack problem in a non-homogeneous interfacial region between two homogeneous half-spaces is considered. It is assumed that the shear modulus varies continuously between that of the two half-spaces; and the shear modulus for the interface region is approximated by = ₀ e^mz. By using Hankel transform technique the problem is reduced to a pair of singular integral equations. The solutions of the problem are obtained for different material combinations and loading conditions; and modes I and II stress intensity factors, and the direction of a probable crack growth are calculated.     

Pseudo Bond Graph modelling and on-line estimation of unknown kinetics for a wastewater biodegradation process

This paper deals with the problem of modelling and on-line estimation of kinetics for a biomethanation process. This bioprocess is in fact a wastewater biodegradation process with production of methane gas, which takes place inside a Continuous Stirred Tank Bioreactor. The reaction scheme and the analysis of biochemical phenomena inside the bioreactor are used in order to obtain a nonlinear dynamic model of the bioprocess, by means of the pseudo Bond Graph method. Two nonlinear estimation strategies are developed for the identification of unknown kinetics of the bioprocess. First, an estimator is developed by using a state observer based technique. Second, an observer based on high-gain approach is designed and implemented. Several numerical simulations are performed in order to analyse and compare the behaviour and the performance of the proposed estimators.     

An assessment of total RMR classification system using unified simulation model based on artificial neural networks

Engineering design has great importance in the cost and safety of engineering structures. Rock mass rating (RMR) system has become a reliable and widespread pre-design system for its ease of use and variety in engineering applications such as tunnels, foundations, and slopes. In RMR system, six parameters are employed in classifying a rock mass: uniaxial compressive strength of intact rock material (UCS), rock quality designation (RQD), spacing of discontinuities (SD), condition of discontinuities (CD), condition of groundwater (CG), and orientation of discontinuities (OD). The ratings of the first three parameters UCS, RQD, and SD are determined via graphic readings where the last three parameters CD, CG, and OD are estimated by the tables that are composed of interval valued linguistic expressions. Because of these linguistic expresions, the estimated rating values of the last three become fuzzy especially when the related conditions are close to border of any two classes. In such cases, these fuzzy situations could lead up incorrect rock class estimations. In this study, an empirical database based on the linguistic expressions for CD, CG, and OD is developed for training Artificial Neural Network (ANN) classifiers. The results obtained from graphical readings and ANN classifiers are unified in a simulation model (USM). The data obtained from five different tunnels, which were excavated for derivation purpose, are used to evaluate classification results of conventional method and proposed model. Finally, it is noted that more accurate and realistic ratings are reached by means of proposed model.     

An automatic diabetes diagnosis system based on LDA-Wavelet Support Vector Machine Classifier

In this paper, an automatic diagnosis system for diabetes on Linear Discriminant Analysis (LDA) and Morlet Wavelet Support Vector Machine Classifier: LDA–MWSVM is introduced. The structure of this automatic system based on LDA-MWSVM for the diagnosis of diabetes is composed of three stages: The feature extraction and feature reduction stage by using the Linear Discriminant Analysis (LDA) method and the classification stage by using Morlet Wavelet Support Vector Machine (MWSVM) classifier stage. The Linear Discriminant Analysis (LDA) is used to separate features variables between healthy and patient (diabetes) data in the first stage. The healthy and patient (diabetes) features obtained in the first stage are given to inputs of the MWSVM classifier in the second stage. Finally, in the third stage, the correct diagnosis performance of this automatic system based on LDA–MWSVM for the diagnosis of diabetes is calculated by using sensitivity and specificity analysis, classification accuracy, and confusion matrix, respectively. The classification accuracy of this system was obtained at about 89.74%.     

A new feature selection method on classification of medical datasets: Kernel F-score feature selection

In this paper, we have proposed a new feature selection method called kernel F-score feature selection (KFFS) used as pre-processing step in the classification of medical datasets. KFFS consists of two phases. In the first phase, input spaces (features) of medical datasets have been transformed to kernel space by means of Linear (Lin) or Radial Basis Function (RBF) kernel functions. By this way, the dimensions of medical datasets have increased to high dimension feature space. In the second phase, the F-score values of medical datasets with high dimensional feature space have been calculated using F-score formula. And then the mean value of calculated F-scores has been computed. If the F-score value of any feature in medical datasets is bigger than this mean value, that feature will be selected. Otherwise, that feature is removed from feature space. Thanks to KFFS method, the irrelevant or redundant features are removed from high dimensional input feature space. The cause of using kernel functions transforms from non-linearly separable medical dataset to a linearly separable feature space. In this study, we have used the heart disease dataset, SPECT (Single Photon Emission Computed Tomography) images dataset, and Escherichia coli Promoter Gene Sequence dataset taken from UCI (University California, Irvine) machine learning database to test the performance of KFFS method. As classification algorithms, Least Square Support Vector Machine (LS-SVM) and Levenberg–Marquardt Artificial Neural Network have been used. As shown in the obtained results, the proposed feature selection method called KFFS is produced very promising results compared to F-score feature selection.     

Medical application of information gain based artificial immune recognition system (AIRS): Diagnosis of thyroid disease

In this paper, we have made medical application of a new artificial immune system named the information gain based artificial immune recognition system (IG-AIRS) which minimizes the negative effects of taking into account all attributes in calculating Euclidean distance in shape–space representation which is used in many artificial immune systems. For medical data, thyroid disease data set was applied in the performance analysis of our proposed system. Our proposed system reached 95.90% classification accuracy with 10-fold CV method. This result ensured that IG-AIRS would be helpful in diagnosing thyroid function based on laboratory tests, and would open the way to various ill diagnoses support by using the recent clinical examination data, and we are actually in progress.