Dr. Ramón Moreno Profile

Dr. Ramón Moreno

at Lortek S.Coop.

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 16 July 2019 Paper

Mass production quality control of welds based on image processing and deep learning in safety components industry

Ander Muniategui, Aitor García de la Yedra, Jon Ander del Barrio, Manuel Masenlle, Xabier Angulo, Ramón Moreno

Proceedings Volume 11172, 111720L (2019) https://doi.org/10.1117/12.2520578

KEYWORDS: Image analysis, Defect detection, Image processing, Machine vision

Read Abstract +

In capital goods industry, there are some components that are employed for safety purposes and, due to this fact, parts are subjected to high quality control demands. This is especially relevant for the case safety components that contain welds because of the inherent complex process and the likelihood of defects appearance. In this context, this work presents a machine vision system that was employed for replacing costly quality control procedures based on visual inspection. This was possible thanks to the proper design of all the machine vision system components including the image processing algorithm. As a special feature of the system, it has to be highlighted the low cycle time of the production process (<2s), which stablished some requirements on the image processing algorithms. During the inspection system development, the main efforts were concentrated for obtaining a reliable and balanced database of defective and non-defective parts images useful to train the classification model. At this respect, the main contributions consisted of image analysis software development and visual curation of data. As a result, tailor made filters were developed that allowed together with color information the identification of common flaws, as Lacks of Fusion (LoF). Due to the high amount of inspected samples, a preliminary deep learning based model was developed that included these filters with the aim of increasing defect detection accuracy.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years