Abstract
An acceptable tool wear evaluation and acquisition approach is critical for developing an accurate, rapid, and practical online tool wear prediction system. The one-dimensional crater wear depth is difficult to quantify, and relying solely on flank wear width is very simplistic. External conditions such as vibration and illumination have a large impact on two-dimensional wear area and three-dimensional volume. In practice, these are difficult to successfully characterize tool wear. This work proposes a two-dimensional rake face edge loss area as an additional indicator to assess milling cutter wear. An adaptive measurement approach based on 2D image feature point set matching is presented to suppress the area calculation error caused by image position offset and chips or built-up edges. 201 images were collected in two groups of cutting experiments for comparison with the proposed method and the other two methods, manual registration and non-registration. The results show that the proposed method's measurement values are closer to the actual values and have a higher measurement efficiency. Furthermore, the proposed indicator is sensitive to micro-scale changes to the blade edge, especially at the tip, and can be used as a supplement one to monitor tool wear more comprehensively.
Published Version
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.
