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Reyes A.,Corporacion Mexicana de Investigacion en Materiales COMIMSA | Calliari I.,University of Padua | Ramous E.,University of Padua | Zanellato M.,University of Padua | Merlin M.,University of Ferrara
Materials Research Society Symposium Proceedings | Year: 2010

A lot of duplex and super duplex stainless steels are prone to secondary phases but with different sequence and kinetic which depend on the chemical composition and thermo-mechanical history of the steel. In this paper the results of secondary phase's determination in a welding grade 2510 duplex steel, heat treated at 850-1050°C for 3-30 min are presented. The precipitation stars at grain boundaries with a consistent ferrite transformation for short times. The noses of the TTP curves are at 1000°C (sigma phase) and at 900°C (chi phase) with a partial transformation of chi to sigma, as evidenced in 2205 and 2507 grades. © 2010 Materials Research Society. Source


Praga-Alejo R.J.,Corporacion Mexicana de Investigacion en Materiales COMIMSA | Cantu-Sifuentes M.,Corporacion Mexicana de Investigacion en Materiales COMIMSA | Gonzalez-Gonzalez D.S.,Autonomous University of Coahuila
Expert Systems with Applications | Year: 2015

Generally, statistical methods and mathematical models are useful for process optimization. Nonetheless, other methods might be used for modeling and optimizing the manufacturing process. Among these, we can mention the neural networks and the Radial Basis Function technique. Hence, a suitable alternative is complementing statistical methods and neural networks as a Hybrid Learning Process. This work applies the Radial Basis Function Canonical Analysis in order to achieve the welding process optimization. One of the most important results is that the Radial Basis Function neural networks along with the Canonical Analysis are really useful methods. These methods are applied for predicting the optimal point, which establishes a reliable method for the process modeling and optimizing. The Canonical Analysis can determine stationary and saddle points, as it was in this case of study, which Canonical Analysis with RBF represented it adequately and can plot a surface and contour lines. Since in this case of study there is a surface that contains a ridge saddle system, also often called minimax. Then the results show that the Canonical Analysis can explore the region with oblique stationary and rising ridge systems. In this way, the RBF neural network with Canonical Analysis could be an alternative method for analyzing data, whenever the Hybrid Learning Process is adequate or satisfies the test assumption and fulfills the evaluation criteria. In this case of study, validation is represented by the Hybrid Learning Process (Radial Basis Function with Canonical Analysis) presenting an excellent effectiveness. As a conclusion we can say that the resulting Radial Basis Function has improved the model accuracy after using the Canonical Analysis. © 2015 Elsevier B.V. All rights reserved. Source


Praga-Alejo R.J.,Corporacion Mexicana de Investigacion en Materiales COMIMSA | Gonzalez-Gonzalez D.S.,Autonomous University of Coahuila | Cantu-Sifuentes M.,Corporacion Mexicana de Investigacion en Materiales COMIMSA
International Journal of Advanced Manufacturing Technology | Year: 2015

In this paper, the Ridge method is applied to improve radial basis function neural network. The resulting redesigned radial basis function is built to test the statistical significance in an array of independent variables considering the existence of a collinearity problem, as well as obtaining appropriate assumptions for concluding those significances. The radial basis function allows the determination of a relationship between a response, and one or more independent variables, determining the importance of each factor for the model. However, this testing may obtain negative results, when one or more columns of the design matrix are linearly dependent; for this reason, we have adapted the Ridge method for the radial basis function. The results show that the variance inflation factor is a good metric alternative for validating the effectiveness of neural network inference. Our primary conclusion is that the redesigned radial basis function results in improved model accuracy when combined with the Ridge method. Additionally, this model can also be used to validate the statistical assumptions required to find the sources of the multicollinearity in an analysis, discovering the corrections and interpreting the model. © 2015, Springer-Verlag London. Source


Medina G.Y.P.,Corporacion Mexicana de Investigacion en Materiales COMIMSA | Padovani M.,University of Ferrara | Merlin M.,University of Ferrara | Perez A.F.M.,Corporacion Mexicana de Investigacion en Materiales COMIMSA | Valdes F.A.R.,Corporacion Mexicana de Investigacion en Materiales COMIMSA
Materials Research Society Symposium Proceedings | Year: 2015

Gas tungsten arc welding-tungsten inert gas (GTAW-TIG) is focused in literature as an alternative choice for joining high strength low alloy steels; this study is performed to compare the differences between gas metal arc welding-metal inert gas (GMAW-MIG) and GTAW welding processes. The aim of this study is to characterize microstructure of dissimilar transformation induced plasticity steels (TRIP) and martensitic welded joints by GMAW and GTAW welding processes. It was found that OMAW process lead to relatively high hardness in the HAZ of TRIP steel, indicating that the resultant microstructure was marten- site. In the fusion zone (FZ), a mixture of phases consisting of bainite, ferrite and small areas of martensite were present. Similar phase's mixtures were found in FZ of GTAW process. The presence of these mixtures of phases did not result in mechanical degradation when the GTAW samples were tested in lap shear tensile testing as the fracture occurred in the heat affected zone. In order to achieve light weight these result are benefits which is applied an autogenous process, where it was shown that without additional weight the out coming welding resulted in a high quality bead with homogeneous mechanical properties and a ductile morphology on the fracture surface. Scanning electron microscopy (SEM) was employed to obtain information about the specimens that provided evidence of ductile morphology. © 2015 Materials Research Society. Source


Praga-Alejo R.J.,Autonomous University of Coahuila | Torres-Trevino L.M.,Autonomous University of Nuevo Leon | Gonzalez-Gonzalez D.S.,Autonomous University of Coahuila | Acevedo-Davila J.,Corporacion Mexicana de Investigacion en Materiales COMIMSA | Cepeda-Rodriguez F.,Corporacion Mexicana de Investigacion en Materiales COMIMSA
Expert Systems with Applications | Year: 2012

The Hybrid Learning Process method proposed in this work, is applied to a Genetic Algorithm and Mahalanobis distance, instead of computing the centers matrix by Genetic Algorithm. It is determined in such a way as to maximize the coefficient of determination R 2 and the Fitness Function depends on the prediction accuracy fitted by the Hybrid Learning approach, where the coefficient of determination R 2 is a global metric evaluation. The Mahalanobis distance is a measurement of distance which uses the correlation between variables and takes into account the covariance and variance matrix in the input variables; this distance helps to reduce the variance into variables. The purpose of this work is to show a methodology to modify the Radial Basis Function and also improve the parameters and variables that are associated with Radial Basis Function learning processes; since the Radial Basis Function has mainly two problems, the Euclidean distance and the calculation of centroids. The results indicated that the statistical methods such as Residual Analysis are good alternatives and excellent methods for validation of the effectiveness of the Neural Network models. The principal conclusion of this work is that the Radial Basis Function Redesigned improved the accuracy of the model using a Hybrid Learning Process and the Radial Basis showed very good performance in a real case, considering the prediction of specific responses in a laser welding process. © 2012 Elsevier Ltd. All rights reserved. Source

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