Patent details
Status
Spanish patent granted
Concession number
ES2947809 A1
Inventors
Federico Sket, Juan Ignacio Caballero, Carlos Daniel González , Ernestina Menasalvas Ruiz y Consuelo Gonzalo Martín
Priority date
9 May 2022
Applicant
IMDEA Materials Institute, Universidad Politécnica de Madrid
Transfer opportunity
License of technology
Summary
Non-destructive machine learning-based methodology capable of improving porosity estimation and types of porosity on composite materials.
Description
Composite materials are usually formed by two elements: a fabric and a polymer resin. During the manufacturing process of composite components, tiny bubbles and voids appear accidentally. This effect called porosity is the main defect in the production lines of the aerospace industry, and in a less term in the eolic power generation and automotive.
The problem is that the estimation of the amount of porosity is performed by techniques that are not accurate enough and do not provide other relevant information about its impact on the components.
IMDEA Materials and UPM have developed techniques based on signal analysis and machine learning that enable a better estimation of the amount of porosity and other technical details.
Advantages and innovations
- The ability to establish different types of porosity.
- The improvement of the estimation of porosity thanks to a new methodology based on using X-rays, the types of porosity and machine learning.
Contact
Knowledge & Technology Transfer Department, IMDEA Materials Institute
email: techtransfer.materials@imdea.org
telephone: +34 91 5493422
