Correct detection of input and output parameters of a welding process is significant for successful development of an automated welding operation. In welding process literature, we observe that output parameters are predicted according to given input parameters. As a new approach to previous efforts, this paper presents a new modeling approach on prediction and classification of welding parameters. 3 different models are developed on a critical welding process based on Artificial Neural Networks (ANNs) which are (i) Output parameter prediction, (ii) Input parameter prediction (reverse application of output prediction model) and (iii) Classification of products. In this study, firstly we use Pareto Analysis for determining uncontrollable input parameters of the welding process based on expert views. With the help of these analysis, 9 uncontrollable parameters are determined among 22 potential parameters. Then, the welding process of ammunition is modeled as a multi-input multi-output process with 9 input and 3 output parameters. 1st model predicts the values of output parameters according to given input values. 2nd model predicts the values of correct input parameter combination for a defect-free weld operation and 3rd model is used to classify the products whether defected or defect-free. 3rd model is also used for validation of results obtained by 1st and 2nd models. A high level of performance is attained by all the methods tested in this study. In addition, the product is a strategic ammunition in the armed forces inventory which is manufactured in a limited number of countries in the world. Before application of this study, the welding process of the product could not be carried out in a systematic way. The process was conducted by trialand- error approach by changing input parameter values at each operation. This caused a lot of costs. With the help of this study, best parameter combination is found, tested, validated with ANNs and operation costs are minimized by 30%.
In this paper, we consider the interaction between a rigid body and an incompressible, homogeneous, viscous fluid. This fluid-solid system is assumed to fill the whole space $\Bbb R^d$, $d=2$ or $3$. The equations for the fluid are the classical Navier-Stokes equations whereas the motion of the rigid body is governed by the standard conservation laws of linear and angular momentum. The time variation of the fluid domain (due to the motion of the rigid body) is not known {\it a priori}, so we deal with a free boundary value problem. \endgraf We improve the known results by proving a complete wellposedness result: our main result yields a local in time existence and uniqueness of strong solutions for $d=2$ or $3$. Moreover, we prove that the solution is global in time for $d=2$ and also for $d=3$ if the data are small enough.
Parasitological examination of freshwater fishes of the Phongolo River in north-eastern KwaZulu-Natal, South Africa resulted in the discovery and morphological and molecular characterisation of a new species of Wenyonia Woodland, 1923 (Cestoda: Caryophyllidea). The new species from the plain squeaker, Synodontis zambezensis Peters (Siluriformes: Mochokidae), is morphologically most similar to Wenyonia acuminata Woodland, 1923, a species reported from three species of Synodontis in north-eastern, western and central Africa (Sudan, Nigeria, Democratic Republic of the Congo). Both these species are markedly different from congeners by having a nematoform body and a digitiform scolex. Wenyonia gracilis sp. n. differs from W. acuminata in its general body size, length and width of main body regions (testicular and uterine regions), a posterior extension of the testes into the uterine region, numerous postovarian vitelline follicles filling the entire medulla, eggs c. 1/3 larger in size, and a scolex with an apical introvert but devoid of longitudinal furrows and a well-defined base. Wenyonia gracilis is the seventh species in the genus and the first autochthonous caryophyllidean ever reported and described from southern Africa (south of the Zambezi River)., Bjoern C. Schaeffner, Divan van Rooyen, Ruan Gerber, Tomáš Scholz, Nico J. Smit., and Obsahuje bibliografii