The monitoring of micro milling tool wear conditions by wear area estimation. (1st September 2017)

• Record Type: Journal Article
• Authors: Zhu, Kunpeng; Yu, Xiaolong
• Is Part Of: Mechanical systems and signal processing. Volume 93(2017)
• Page Start: 80
• Page End: 91
• Publication Date: 2017-09-01
• Subjects: Micro milling -- Tool wear area estimation -- Morphological component analysis -- Region growing; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2017.02.004
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: Developed a novel tool wear area estimation method with morphological component analysis. Identified the noise and background properties in micromilling tool wear image. Investigated the rationale of wear area estimation method and its connection to traditional width estimation approach. Verified the effectiveness of the approach with various experimental studies. Abstract: In micro milling, the tool wear condition is key to the geometrical and surface integrity of the product. This study proposes a novel tool wear surface area monitoring approach based on the full tool wear image, which can reflect the tool conditions better than the traditional tool wear width criteria. To meet the challenges of heavy noise, blur boundary, and mis-alignment of the captured tool wear images, this paper develops a region growing algorithm based on morphological component analysis (MCA) to solve the problems. It decomposes the original micro milling tool image into target tool images, background image and noise image. Then, the region growing algorithm is used to detect the defect and extract the wear region of the target tool image. In addition, rotation invariant features are extracted from wear region to overcome the inconsistency of wear image orientation. The experiment results show that region growing based on MCA algorithm can extract the wear region of the target tool image effectively and the extracted wear region also has good indication of tool wear conditions. It also demonstrates that the estimation of wear area can generalize the tool wear width estimation approach, and yield more accurate results than the traditional approaches.