Image edge detection based tool condition monitoring with morphological component analysis. (July 2017)

• Record Type: Journal Article
• Authors: Yu, Xiaolong; Lin, Xin; Dai, Yiquan; Zhu, Kunpeng
• Is Part Of: ISA transactions. Volume 69(2017:Jul.)
• Page Start: 315
• Page End: 322
• Publication Date: 2017-07
• Subjects: Tool condition monitoring -- Image edge detection -- Morphological component analysis; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2017.03.024
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: The measurement and monitoring of tool condition are keys to the product precision in the automated manufacturing. To meet the need, this study proposes a novel tool wear monitoring approach based on the monitored image edge detection. Image edge detection has been a fundamental tool to obtain features of images. This approach extracts the tool edge with morphological component analysis. Through the decomposition of original tool wear image, the approach reduces the influence of texture and noise for edge measurement. Based on the target image sparse representation and edge detection, the approach could accurately extract the tool wear edge with continuous and complete contour, and is convenient in charactering tool conditions. Compared to the celebrated algorithms developed in the literature, this approach improves the integrity and connectivity of edges, and the results have shown that it achieves better geometry accuracy and lower error rate in the estimation of tool conditions. Highlights: Developed a novel tool wear area estimation method with morphological component analysis. Reduced the influence of texture and noise for edge detection, and convenient for tool wear measurement. Improved the integrity and connectivity of tool image edges. Verified the effectiveness of the approach with various experimental studies.