Electroplastic cutting influence on power consumption during drilling process

The aim of this study is to report the use of non-conventional material removal process technique. It was found that electropulses (EPs) assisted drilling process improves the material machinability based on the eletroplastic influence. The influence of EPs in drilling process is studied by combining different feed rates, drills diameters, and current densities in 7075 aluminium and 1045 carbon steel. The results show that the electrically assisted drilling process improves material machinability, decreases the specific cutting energy up to 27 % in aluminium and 17 % in steel.Peer ReviewedPreprin

The Linear Ordering Problem Revisited

The Linear Ordering Problem is a popular combinatorial optimisation problem which has been extensively addressed in the literature. However, in spite of its popularity, little is known about the characteristics of this problem. This paper studies a procedure to extract static information from an instance of the problem, and proposes a method to incorporate the obtained knowledge in order to improve the performance of local search-based algorithms. The procedure introduced identifies the positions where the indexes cannot generate local optima for the insert neighbourhood, and thus global optima solutions. This information is then used to propose a restricted insert neighbourhood that discards the insert operations which move indexes to positions where optimal solutions are not generated. In order to measure the efficiency of the proposed restricted insert neighbourhood system, two state-of-the-art algorithms for the LOP that include local search procedures have been modified. Conducted experiments confirm that the restricted versions of the algorithms outperform the classical designs systematically. The statistical test included in the experimentation reports significant differences in all the cases, which validates the efficiency of our proposal

New methods for generating populations in Markov network based EDAs: Decimation strategies and model-based template recombination

Methods for generating a new population are a fundamental component of estimation of distribution algorithms (EDAs). They serve to transfer the information contained in the probabilistic model to the new generated population. In EDAs based on Markov networks, methods for generating new populations usually discard information contained in the model to gain in efficiency. Other methods like Gibbs sampling use information about all interactions in the model but are computationally very costly. In this paper we propose new methods for generating new solutions in EDAs based on Markov networks. We introduce approaches based on inference methods for computing the most probable configurations and model-based template recombination. We show that the application of different variants of inference methods can increase the EDAs’ convergence rate and reduce the number of function evaluations needed to find the optimum of binary and non-binary discrete functions

Using network mesures to test evolved NK-landscapes

In this paper we empirically investigate which are the structural characteristics that can help to predict the complexity of NK-landscape instances for estimation of distribution algorithms. To this end, we evolve instances that maximize the estimation of distribution algorithm complexity in terms of its success rate. Similarly, instances that minimize the algorithm complexity are evolved. We then identify network measures, computed from the structures of the NK-landscape instances, that have a statistically significant difference between the set of easy and hard instances. The features identified are consistently significant for different values of N and K

A review on Estimation of Distribution Algorithms in Permutation-based Combinatorial Optimization Problems

Estimation of Distribution Algorithms (EDAs) are a set of algorithms that belong to the field of Evolutionary Computation. Characterized by the use of probabilistic models to represent the solutions and the dependencies between the variables of the problem, these algorithms have been applied to a wide set of academic and real-world optimization problems, achieving competitive results in most scenarios. Nevertheless, there are some optimization problems, whose solutions can be naturally represented as permutations, for which EDAs have not been extensively developed. Although some work has been carried out in this direction, most of the approaches are adaptations of EDAs designed for problems based on integer or real domains, and only a few algorithms have been specifically designed to deal with permutation-based problems. In order to set the basis for a development of EDAs in permutation-based problems similar to that which occurred in other optimization fields (integer and real-value problems), in this paper we carry out a thorough review of state-of-the-art EDAs applied to permutation-based problems. Furthermore, we provide some ideas on probabilistic modeling over permutation spaces that could inspire the researchers of EDAs to design new approaches for these kinds of problems

Machinability estimation by drilling monitoring

This article describes the development of a methodology to measure the specific cutting energy (SCE) in drilling. The SCE allows to characterize the machining process, thus obtaining a technologic approach to plan the technologic road map of the chip removal process. The main frame of this machine consists of a pedestal drill, instrumented with a meter, a motor that controls the spindle feed rate, safety elements such as limit switches and an active power meter coupled to the drill motor. The mechanical power is calculated indirectly through the active power. To this end, it must have a previous calibration with a dynamometer torque cell to record the output curve and, subsequently, display the relationship between the mechanical power and the active electrical power. To validate the capability of this method, SCE of three different materials are evaluated: 7075-T6 aluminium, C45E steels and 34CrNiMo6. Accordingly, it has been verified that the estimated SCE values correspond to those described by the literature. The influence of several parameters, like the cutting speed and the feed rate, on the SCE let us estimate the sensitivity of this method. Finally, this works shows that the SCE in steel increases with the increase of the feed rate, whereas in aluminium the behaviour is the opposite, the SCE decreases as the feed rate increases.Este artículo describe el desarrollo de un método para evaluar la Energía Específica de Corte (EEC) en el taladrado. Este parámetro permite caracterizar el proceso de mecanizado, obteniendo con ello un modelo tecnológico general que permite realizar la planificación del proceso de arranque de viruta. El cuerpo principal de la máquina está formado por un taladro de pedestal, instrumentalizado con: un motor que controla la velocidad de avance del husillo, los elementos de seguridad para los finales de carrera y un medidor de potencia activa acoplado al motor del taladro. La potencia mecánica se obtiene indirectamente a través de la potencia activa, realización una calibración previa con un dinamómetro de par, de la que se obtiene la curva que relaciona la potencia mecánica con la potencia activa. Para demostrar el correcto funcionamiento del método se evalúa la EEC de tres materiales, un aluminio 7075-T6 y los aceros C45E y 34CrNiMo6. Se ha podido comprobar que los valores estimados de energía corresponden a los descritos en la literatura. Un estudio de la dependencia que tiene la EEC de los parámetros velocidad de corte y velocidad de avance, permite observar la sensibilidad del método. Finalmente, este trabajo muestra que la EEC en los aceros aumenta a medida que aumenta la velocidad de avance, en cambio en el aluminio el comportamiento es inverso, la EEC disminuye a medida que la velocidad de avance aumenta.Peer ReviewedPostprint (published version

Extending Distance-based Ranking Models In Estimation of Distribution Algorithms

Recently, probability models on rankings have been proposed in the field of estimation of distribution algorithms in order to solve permutation-based combinatorial optimisation problems. Particularly, distance-based ranking models, such as Mallows and Generalized Mallows under the Kendall’s-t distance, have demonstrated their validity when solving this type of problems. Nevertheless, there are still many trends that deserve further study. In this paper, we extend the use of distance-based ranking models in the framework of EDAs by introducing new distance metrics such as Cayley and Ulam. In order to analyse the performance of the Mallows and Generalized Mallows EDAs under the Kendall, Cayley and Ulam distances, we run them on a benchmark of 120 instances from four well known permutation problems. The conducted experiments showed that there is not just one metric that performs the best in all the problems. However, the statistical test pointed out that Mallows-Ulam EDA is the most stable algorithm among the studied proposals

A quantitative analysis of estimation of distribution algorithms based on Bayesian networks

The successful application of estimation of distribution algorithms (EDAs) to solve different kinds of problems has reinforced their candidature as promising black-box optimization tools. However, their internal behavior is still not completely understood and therefore it is necessary to work in this direction in order to advance their development. This paper presents a new methodology of analysis which provides new information about the behavior of EDAs by quantitatively analyzing the probabilistic models learned during the search. We particularly focus on calculating the probabilities of the optimal solutions, the most probable solution given by the model and the best individual of the population at each step of the algorithm. We carry out the analysis by optimizing functions of different nature such as Trap5, two variants of Ising spin glass and Max-SAT. By using different structures in the probabilistic models, we also analyze the influence of the structural model accuracy in the quantitative behavior of EDAs. In addition, the objective function values of our analyzed key solutions are contrasted with their probability values in order to study the connection between function and probabilistic models. The results not only show information about the EDA behavior, but also about the quality of the optimization process and setup of the parameters, the relationship between the probabilistic model and the fitness function, and even about the problem itself. Furthermore, the results allow us to discover common patterns of behavior in EDAs and propose new ideas in the development of this type of algorithms

Evolving Gaussian process kernels from elementary mathematical expressions for time series extrapolation

[EN]Choosing the best kernel is crucial in many Machine Learning applications. Gaussian Processes are a state-of-the-art technique for regression and classification that heavily relies on a kernel function. However, in the Gaussian Processes literature, kernels have usually been either ad hoc designed, selected from a predefined set, or searched for in a space of compositions of kernels which have been defined a priori. In this paper, we propose a Genetic Programming algorithm that represents a kernel function as a tree of elementary mathematical expressions. By means of this representation, a wider set of kernels can be modeled, where potentially better solutions can be found, although new challenges also arise. The proposed algorithm is able to overcome these difficulties and find kernels that accurately model the characteristics of the data. This method has been tested in several real-world time series extrapolation problems, improving the state-of-the-art results while reducing the complexity of the kernels.This work has been supported by the Spanish Ministry of Science and Innovation (project PID2019-104966 GB-I00) , and the Basque Government (projects KK-2020/00049 and IT1244-19, and ELKARTEK program) . Jose A. Lozano is also supported by BERC 2018-2021 (Basque government) and BCAM Severo Ochoa accred-itation SEV-2017-0718 (Spanish Ministry of Science and Innovation)

Interactivo para Autoaprendizaje del Análisis de Mecanismos

Con este informe se da a conocer la herramienta y la metodología utilizada en el aprendizaje de análisis de mecanismos, planteado en la asignatura Mecánica y Teoría de Mecanismos 2 (MTM 2) impartida para la titulación de Ingeniería Industrial Mecánica, en la Escola Universitaria de Enginyers Industrials de Barcelona (E.U.E.T.I.B.). En este trabajo se explican los contenidos y la aplicación del material multimedia "Interactivo", utilizado para dar soporte a las actividades docentes presenciales y al estudio no presencial de la asignatura. La idea de crear el interactivo responde a la necesidad de adaptar los contenidos y recursos docentes a los requerimientos que las pautas de la declaración de Bolonia y las bases del futuro Espacio Europeo de Educación Superior (E.E.E.S.) plantean, entre ellos, la adecuación de la metodología de impartición y de evaluación de las asignaturas. [2] El desarrollo del interactivo tiene como objetivos facilitar la comprensión de la asignatura y proporcionar a los alumnos un recurso que incite a desarrollar aptitudes de autoformación, que despierte en ellos un espíritu de investigación y creatividad, y que fomente las habilidades de búsqueda y aplicación de los conocimientos. En él se le propone al alumno una serie de actividades que le permiten tener una participación individual y colectiva más activa, así como desarrollar aptitudes y actitudes en el planteamiento y resolución de problemáticas reales en el estudio de mecanismos. En este informe se describen los resultados de la positiva valoración que hacen los alumnos del interactivo y se exponen las aportaciones desde el punto de vista del aprendizaje y de la adquisición de habilidades