Eduardo Martínez-Cámara

Control of Discrete Event Systems by Means of Discrete Optimization and Disjunctive Colored PNs: Application to Manufacturing Facilities

Artificial intelligence methodologies, as the core of discrete control and decision support systems, have been extensively applied in the industrial production sector. The resulting tools produce excellent results in certain cases; however, the NP-hard nature of many discrete control or decision making problems in the manufacturing area may require unaffordable computational resources, constrained by the limited available time required to obtain a solution. With the purpose of improving the efficiency of a control methodology for discrete systems, based on a simulation-based optimization and the Petri net (PN) model of the real discrete event dynamic system (DEDS), this paper presents a strategy, where a transformation applied to the model allows removing the redundant information to obtain a smaller model containing the same useful information. As a result, faster discrete optimizations can be implemented. This methodology is based on the use of a formalism belonging to the paradigm of the PN for describing DEDS, the disjunctive colored PN. Furthermore, the metaheuristic of genetic algorithms is applied to the search of the best solutions in the solution space. As an illustration of the methodology proposal, its performance is compared with the classic approach on a case study, obtaining faster the optimal solution.

Compound Petri nets and alternatives aggregation Petri nets: two formalisms for decision-making support

Decision-making in technological systems, such as communication networks, manufacturing facilities and supply chains, constitutes a common requirement able to lead companies galore to success or failure. This article presents a decision-making methodology, where the feasible structural configurations to be analysed are chosen heuristically in the frame of a single optimization problem. For stating the optimization problem and solving it efficiently, appropriate formalisms would be used. Compound Petri nets, a particular kind of parametric Petri nets, and alternatives aggregation Petri nets, are two Petri net–based formalisms able to integrate in the same model different alternative structural configurations. Moreover, even having different characteristics that might make them useful for different applications, both formalisms present common features, such as including a set of exclusive entities and the possibility of developing compact Petri net models, by the removal of redundant information. This article is also focused on the transformation algorithm between compound Petri nets and alternatives aggregation Petri nets. This algorithm is devoted to transform a model described by one of the formalisms into an equivalent model, that is, with the same behaviour, represented using the other formalism. Finally, several application examples are given for illustrating the steps of the transformation algorithm.

Wavelets Application in Prediction of Friction Stir Welding Parameters of Alloy Joints from Vibroacoustic ANN-Based Model

This paper analyses the correlation between the acoustic emission signals and the main parameters of friction stir welding process based on artificial neural networks (ANNs). The acoustic emission signals in Z and Y directions have been acquired by the AE instrument NI USB-9234. Statistical and temporal parameters of discomposed acoustic emission signals using Wavelet Transform have been used as input of the ANN. The outputs of the ANN model include the parameters of tool rotation speed and travel speed, and tool profile, as well as the tensile strength. A multilayer feed-forward neural network has been selected and trained, using Levenberg-Marquardt algorithm for different network architectures. Finally, an analysis of the comparison between the measured and the calculated data is presented. The model obtained can be used to model and develop an automatic control of the parameters of the process and mechanical properties of joint, based on the acoustic emission signals.