Deep Learning Algorithms in Industry 4.0; Application of Surface Defect Inspection for Quality Control
This PhD thesis aims to develop an automated method for defect identification based on the magnetic particle technique using deep learning.
We are in the transition between Industry 4.0 and 5.0, where in addition to productivity, flexibility is also sought to adjust processes to specific customer needs.
A large part of this research work presented in this document focuses on quantifying the error committed by the main mapping and localisation methods, offering different alternatives for improving their positioning.
This thesis focuses on the study and scaling of a disruptive technology called "non-immersion ultrasonic cleaning".
This thesis presents a methodology for start-ups that outlines the steps to design and develop embedded medical devices.
Contributions to time series analysis, modelling and forecasting to increase reliability in industrial environments
The integration of the Internet of Things in the industrial sector is considered a prerequisite for achieving intelligence in a company. To obtain this, AI systems with analytical and learning capabilities are required for the optimisation of industrial processes.