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Vallejo A.J.,Institute Automatica Industrial | Morales-Menendez R.,Monterrey Institute of Technology | Ramirez-Mendoza R.,Monterrey Institute of Technology | Garza-Castanon L.,Monterrey Institute of Technology
2009 European Control Conference, ECC 2009

An online surface roughness prediction module for peripheral end milling in High Speed Machining was developed. An Artificial Neural Network framework integrated five cutting parameters and one process variable signal. Vibration signal in the workpiece showed high correlation with the surface roughness. This signal was pre-processed as Mel Frequency Cesptrum Coefficients. This could be a practical solution for a wide cutting conditions with several Aluminium alloys and cutting tools. Results were validated by using an industrial High Speed Machining center. © 2009 EUCA. Source

Vallejo A.J.,Institute Automatica Industrial | Morales-Menendez R.,Monterrey Institute of Technology
Annual Reviews in Control

A proposal for an intelligent monitoring and control system is introduced. Achievement of a specific surface roughness is themain goal because it is a well-known index of product quality and a technical requirement for mechanical products. The system integrates four modules: Data acquisition, surface roughnessmonitoring, cutting toolmonitoring, and intelligent process planning. Values of the cutting parameters for a peripheral milling process are estimated a priori, and by using aGeneticAlgorithm, the optimal cutting parameters are determined. In intelligent process planning module, aMarkov decision process is implemented to compute an optimal machining policy. Based on this policy, the system generates recommendations that optimize the operating costs. © 2009 Elsevier Ltd. Source

Guijarro M,Complutense University of Madrid | Pajares G.,Complutense University of Madrid | Riomoros I.,Complutense University of Madrid | Herrera P.J,Complutense University of Madrid | And 2 more authors.
Computers and Electronics in Agriculture

One important issue emerging strongly in agriculture is related with the automatization of tasks, where the optical sensors play an important role. They provide images that must be conveniently processed. The most relevant image processing procedures require the identification of green plants, in our experiments they come from barley and corn crops including weeds, so that some types of action can be carried out, including site-specific treatments with chemical products or mechanical manipulations. Also the identification of textures belonging to the soil could be useful to know some variables, such as humidity, smoothness or any others. Finally, from the point of view of the autonomous robot navigation, where the robot is equipped with the imaging system, some times it is convenient to know not only the soil information and the plants growing in the soil but also additional information supplied by global references based on specific areas. This implies that the images to be processed contain textures of three main types to be identified: green plants, soil and sky if any. This paper proposes a new automatic approach for segmenting these main textures and also to refine the identification of sub-textures inside the main ones. Concerning the green identification, we propose a new approach that exploits the performance of existing strategies by combining them. The combination takes into account the relevance of the information provided by each strategy based on the intensity variability. This makes an important contribution. The combination of thresholding approaches, for segmenting the soil and the sky, makes the second contribution; finally the adjusting of the supervised fuzzy clustering approach for identifying sub-textures automatically, makes the third finding. The performance of the method allows to verify its viability for automatic tasks in agriculture based on image processing. © 2010 Elsevier B.V. Source

Martin C.J.,Institute Automatica Industrial | Martinez-Graullera O.,Institute Automatica Industrial | Godoy G.,Escuela Polytechnic Superior | Ullate L.G.,Institute Automatica Industrial
IEEE Transactions on Image Processing

Synthetic aperture (SA) techniques have been frequently used to reduce the volume and complexity of the imaging systems. A useful tool for designing synthetic aperture configurations is the coarray. This is the virtual aperture that produces in one way the same beam pattern as the SA system in emission and reception. In this correspondence, we propose a new algorithm, based on the polynomial decomposition, that allows to obtain any wanted coarray on a linear array using whatever synthetic aperture configuration. With this fast and simple algorithm, the desired coarray is decomposed into a set of sub-apertures, whose length is determined by the requirements and resources of the system. The result is the set of weights that have to be applied on the sub-apertures to get the desired coarray, and consequently, the wanted beam pattern. © 2006 IEEE. Source

Gajate A.,Institute Automatica Industrial | Haber R.E.,Institute Automatica Industrial | Haber R.E.,Autonomous University of Madrid | Del Toro R.M.,Institute Automatica Industrial
International Journal of Innovative Computing, Information and Control

This paper reports on the design and implementation of a neurofuzzy system for modelling and controlling drilling processes in an Ethernet-based application. The neurofuzzy system in question is an Adaptive Network based Fuzzy Inference System (ANFIS), where fuzzy rules are obtained from input/output data. The design of the control system is based on the internal model control paradigm. The main advantages of the suggested approach are that its use of a neurofuzzy system to deal with nonlinear drilling process behaviour and process uncertainty eliminates the need for an exact mathematical model to design and tune the control system, and that it offers a simple and computationally efficient procedure for real-time applications. The results are positive in both simulation and in the real-time application of networked control. The case study indicates that the proposed method outperforms a PID control strategy and an optimal fuzzy controller. This improved behaviour is verified by several performance indices. © 2010 ISSN. Source

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