Online adaption of milling parameters for a stable and productive process. Issue 1 (2021)

• Record Type: Journal Article
• Authors: Bergmann, Benjamin; Reimer, Svenja
• Is Part Of: CIRP annals. Volume 70:Issue 1(2021)
• Page Start: 341
• Page End: 344
• Publication Date: 2021
• Subjects: Machine tool -- Chatter -- Machine Learning; Production engineering -- Research -- Periodicals; 670.5
• DOI: 10.1016/j.cirp.2021.04.086
• Languages: English
• ISSNs: 0007-8506

Abstract

Abstract: On the way to fully autonomous machine tools it is essential to independently select suitable process parameters and adapt them on-the-fly to the appropriate process conditions in a self-controlled manner. Such systems require complex physical process models and are usually limited to feed and spindle speed adaption during the milling process. This paper introduces a new approach enabling machines during the milling process to learn which parameters lead to a stable process with maximum productivity and to adjust them autonomously. It is shown that this approach enables the machine tool to independently find stable process parameters with maximum productivity.