Mr. Gerald Kolter Profile

Gerald Kolter

at Fraunhofer-ILT

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 | 6 September 2019 Paper

Deep learning-based semantic segmentation for in-process monitoring in laser welding applications

C. Knaak, G. Kolter, F. Schulze, M. Kröger, P. Abels

Proceedings Volume 11139, 1113905 (2019) https://doi.org/10.1117/12.2529160

KEYWORDS: Image processing, Laser welding, Image segmentation, Cameras, Feature extraction, Visual process modeling, Laser processing, Convolutional neural networks, Defect detection, Machine learning

Read Abstract +

The broad uses of laser welding in various industrial applications such as shipbuilding, automotive production and battery manufacturing, result from its capabilities of high productivity, flexibility and effectiveness¹. However, the complex nature of laser-material interaction requires additional measures in order to reach the high-quality standards of the goods produced. Therefore, continuous process monitoring in laser welding is crucial to achieve reliable mass production and high-quality products at once. Camera-based process monitoring offers great advantages compared to one-dimensional observation techniques. The spatial resolution enables the monitoring of several process characteristics simultaneously, which leads to a more detailed description of the current process state². In the last few years, we proposed a coaxially integrated camera system with external illumination. Process images taken by this system typically show the keyhole area, the weld pool, but also areas of solidified weld and areas of the blank sheet³. To automate image evaluation with respect to the recognition of aforementioned areas, we propose a convolutional neural network architecture to perform pixel-wise image classification⁴. In this paper, we investigate the influence of multiple hyper-parameters required for the network architecture in use, but also the amount of data that is necessary for high segmentation accuracies. In a second step, the outcome of the network is used to detect process deviations in laser welding image data using supervised machine learning. With the help of the Random Forest algorithm, assessment of the extracted process characteristics with respect to prediction accuracy takes place. Based on the information of the segmented image data, further investigations are carried out into the possibility of predicting individual process parameters such as laser power, welding speed and focus size simultaneously.

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