Abstract
Non-uniform heating during metal bar hot forming may impact its straightness. In this study, an infrared non-destructive inspection system is proposed to acquire steel temperature profiles in runtime which should correlate to straightness deviations. Additionally, a machine learning algorithm detects outliers to identify oxides on the metal, which in turn is correlated to process parameters. This allows for proactive temperature adjustment to mitigate the risk based on historical profiles. The proposed approach has been tested in a use case coming from the steel industry.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
