Física Aplicada
Departamento
ROBERTO
FERNANDEZ MARTINEZ
PROFESORADO TITULAR DE UNIVERSIDAD
Publicaciones en las que colabora con ROBERTO FERNANDEZ MARTINEZ (12)
2023
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The determination of optimum segmentation parameters using genetic algorithms: Application to different segmentation algorithms and transmission electron microscopy tomography reconstructed volumes
Microscopy Research and Technique, Vol. 86, Núm. 10, pp. 1237-1248
2021
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Quantitative electron tomography of polylactic acid/clay nanocomposites for better comprehension of processing–microstructure–elastic modulus
Polymers and Polymer Composites, Vol. 29, Núm. 6, pp. 724-732
2019
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Use of support vector machines, neural networks and genetic algorithms to characterize rubber blends by means of the classification of the carbon black particles used as reinforcing agent
Soft Computing, Vol. 23, Núm. 15, pp. 6115-6124
2018
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Influence of cryogenic treatment on wear resistance and microstructure of AISI A8 tool steel
Metals, Vol. 8, Núm. 12
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Semi-automated quantification of the microstructure of PLA/clay nanocomposites to improve the prediction of the elastic modulus
Polymer Testing, Vol. 66, pp. 280-291
2017
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Carbide distribution based on automatic image analysis for cryogenically treated tool steels
Materiali in Tehnologije, Vol. 51, Núm. 4, pp. 609-611
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Methodology to classify the shape of reinforcement fillers: optimization, evaluation, comparison, and selection of models
Journal of Materials Science, Vol. 52, Núm. 1, pp. 569-580
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Tensile strength prediction of rubber blends using linear regression techniques
IEEE 4th International Conference on Soft Computing and Machine Intelligence, ISCMI 2017
2015
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3D TEM reconstruction and segmentation process of laminar bio-nanocomposites
AIP Conference Proceedings
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Pin on disk against ball on disk for the evaluation of wear improvement on cryo-treated metal cutting shears
AIP Conference Proceedings
2014
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Modeling of the mechanical properties of carbon-black reinforced rubber blends by machine learning techniques
Applied Mechanics and Materials
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Use of decision tree models based on evolutionary algorithms for the morphological classification of reinforcing nano-particle aggregates
Computational Materials Science, Vol. 92, pp. 102-113