Abstract
Incremental sheet forming is a process for the production of sheet metal parts in small batch sizes. Due to the relatively low geometrical accuracy and the lack of precise and fast finite element analysis simulations of the process, industrial use cases are rare. Recently, a vast amount of scientific approaches simulated the process by utilizing machine learning techniques. Their success is limited by the quantity and quality of the used process data. Research institutes are struggling to gather enough data without industrial cooperations. For maximizing the distribution of process data in an experimental series and therefore their applicability for machine learning, the authors present a novel cluster analysis approach to systematically extend an existing database. The whole established process database consisting of 70 forming experiments and their toolpaths and digitizations is published to be used as a foundation for similar research.
Sign-in/Register to access full text options 
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 callMore From: The International Journal of Advanced Manufacturing Technology
Disclaimer: 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.
