Publicaciones en las que colabora con Roberto José García Martín (12)

2024

  1. Surrogate metamodels from digital image correlation for testing high-performance composite vessels

    Heliyon, Vol. 10, Núm. 8

2023

  1. Finite Element Analysis of the Influence of Chamfer Hub Geometry on the Stress Concentrations of Shrink Fits

    Applied Sciences (Switzerland), Vol. 13, Núm. 6

  2. Influence of the Friction Coefficient on the Stress Distributions and Contact Pressure in Press-Fits via Finite Element Analysis

    Lubricants, Vol. 11, Núm. 11

  3. Smartvessel: A New Extinguisher Prototype Based on New Materials and IoT Sensors

    Sensors, Vol. 23, Núm. 6

2022

  1. Finite Element Analysis of the Reduction in Stress Concentration Factors in Shrink Fits by Using Contact Rings

    Applied Sciences (Switzerland), Vol. 12, Núm. 19

  2. Step heating thermography supported by machine learning and simulation for internal defect size measurement in additive manufacturing

    Measurement: Journal of the International Measurement Confederation, Vol. 205

2021

  1. A Comparative Study of 2D and 3D Digital Image Correlation Approaches for the Characterization and Numerical Analysis of Composite Materials

    IEEE Access, Vol. 9, pp. 160675-160687

  2. Compression and strain predictive models in non-structural recycled concretes made from construction and demolition wastes

    Materials, Vol. 14, Núm. 12

  3. Control charts based on MATLAB statistical and visualization tools as a compatible with e-learning methodology in the context of quality control.

    ACM International Conference Proceeding Series

  4. Manufacturing of a mechanical respirator valve in the context of e-learning methodology

    ACM International Conference Proceeding Series

2020

  1. Digital image correlation and reliability-based methods for the design and repair of pressure pipes through composite solutions

    Construction and Building Materials, Vol. 248

  2. Predictive models for the characterization of internal defects in additive materials from active thermography sequences supported by machine learning methods

    Sensors (Switzerland), Vol. 20, Núm. 14, pp. 1-26